Framework You Can Use: "EIW failed and DataStar endorsed now"
My three questions: Harmonizing data versus integrating the data?; Integrating unstructured and structured data?; and Using RDF and OWL?
What Went Wrong with the Obamacare Web Site, and How Can It Be Fixed?
Linked Data "Also, these data are independently maintained and not truly semantically integrated, as discussed below."
Purpose: We are interested in learning about Taxonomy and Enterprise Vocabulary for fundamental architectural elements to enable interoperability and provide consistent understanding of shared architecture information across enterprise. Source: Walt Okon, October 4, 2011, Email.
2. Build that inventory in an Excel spreadsheet so it supports faceted search in a Spotfire dashboard.
4. Provide the multiple sample knowledgebases in a Spotfire dashboard so they can be seen, compared, merged, harmonized, sorted, searched, downloaded, and shared on mobile devices (e.g. iPad).
5. Scale the previous architectural pattern with more content volume and types if necessary.
Goal: Federate the DoD IEA and BEA. Note: This pilot shows how that could be done.
A statement in the Linked Data Section of the SYSTAP Literature Survey of Graph Databases seems very appropriate: "Also, these data are independently maintained and not truly semantically integrated, as discussed below (see Challenges section) which says: "The Linked Open Data Cloud is an impressive example of what can be achieved with standards for graph data interchange and simple access protocols. However, there are significant problems introduced by the success of RDF, SPARQL, and linked data. First, these data sets are not truly semantically integrated – instead they tend to share some schemas and provide some level of cross-‐linking. As more and more graph data sets are published (or shared privately by corporations), it becomes increasingly important to find ways to automatically semantically align and mine the relationships in these data. Otherwise we will find ourselves with too much data and too little information that we can actually use. Second, as more and more data sets are combined, it becomes increasingly impossible for even expert users to write queries that properly integrate all of the available data that bears on their problem. This leads quickly to published and semantically disclosed information that nevertheless cannot be adequately leveraged."
My comment: If data sets are not collected with integration in mind, then it is very difficult, if not impossible to semantically integrate them afterwards, and efforts to build new semantically interoperable systems is likely to be more successful by adding common key fields, etc, through collaborative agreements that this is in the common interests to all the data set stewards.
My close colleague Dr. Geoffrey Malafsky will lead a team of data scientists for an in-depth discussion of how to reconcile disparate information systems. Dr. Malafsky earned his stripes by saving a $300 million data reconciliation project several years ago using the most elegant methodology I've seen.
In this one-day seminar, we provide a framework and methodology to help you address critical business challenges in a practical, cost-effective way, regardless of which tools you use, or your data maturity.
Feel free to bring any examples of issues you're facing right now, including actual data models, Visio diagrams or anything else. Part of the day will focus on solving real-world problems for attendees.
The afternoon most enlightening as it dealt with the real use case of many of the questions you asked. But, I want to clarify some issues so we continue to understand each others perspective on semantics and how we are each using it to solve challenges in the data field.
I frequently state that there is no evidence of large scale operational success of semantic technology over the past 20 years. Many attempts; large investments; many proud public claims; but no actual production use in heterogeneous data environments. This includes what is widely considered a boondoggle failure of Wiznosky's Semantic Web effort EIW which was killed (with my overt cheering) by DCMO in early 2013.
I appreciate your detailed explanation below and what comes to mind is that this would make an excellent panel session for our new Federal Big Data Working Group Meetup, that I have invited Eric to participate in using your Healthcare.gov critique.
So if you are willing, I would like to share your thoughts with a few experts from Semantic Web-Semantic Technology and invite you to suggest someone else who shares your views (DoD, Navy, etc.), or even a different view from yours and the SW-ST experts, to participant in a panel discussion, so we can have an email discussion before the panel say in early-February.
Dennis, Kingsley, and Jim, Please see the email from Geoffrey Malafsky below and provide your responses in preparation for a panel discussion that I would like to have for the new Federal Big Data Working Group Meetup.
Decided not ready, need to see data sets, and need to focus on Healthcare.gov instead.
The Ψ–KORS™ System Model: Is This the Enterprise Data Model?
This workshop was held in GMU Enterprise Center where i read in the Fall 2013 magazine about its new president, Angel Cabrera, and the Mason IDEA of being innovative, diverse, entrepreneurial, and accessible. George Mason University is one of the fastest growing and most culturally diverse universities in the United States, The university's student enrollment has increased to more than 32,000 students who major in a wide variety of undergraduate and graduate programs.
The Mason Enterprise Center Was Honored as Top Global University Business Incubator by the UBI Index.
Based in Stockholm, Sweden, the UBI Index is a European research initiative with an international research team specializing in incubation and entrepreneurship indexes. This was UBI's first global benchmark study of university business incubators.
I am going to try to get the UBI Index data set for the Data Science Course I am preparing to teach at GMU in January 2014.
Want to start or transform an incubator? Read on!
Everyone who knows incubation up close would agree that if done right, business incubation has the potential to transform businesses. But the question is what is the right method? One way of finding it is to observe and benchmark the top performing incubators globally and learn from their savoir faire. Surprisingly, no serious professional e!ort has been made on a global scale to highlight these best practices. Doing so will not only help aspiring incubators, policy makers and organizations that support entrepreneurship but also revive the debate and aid the incubation industry to move forward. This is what this professional benchmark report is all about!
As it is impossible to cover all types of incubators in a single benchmark, this first report in a series of other upcoming benchmarks focuses on university business incubators. One of the main reasons being that a multitude of incubators are found in universities around the world. According to the NBIA's 2012 State of the Incubation Industry report, academic institutions are the single largest sponsors (32%) of all incubators in North America. Consequently, we feel it is legitimate to start with university business incubators in our first benchmark.
Top incubators have 3.3 times higher sales revenue generated by graduates per year than global average incubators?
We would love to hear your thoughts and perception about incubation of tomorrow and the Global Benchmark of Top University Business Incubators.
Insights from our study - what are your thoughts?
Do you know that top list incubators have 2x higher number of clients receiving VC or angel funding per year copared to the global average? According to our study, almost 7 clients of top list incubators receive VC or angel funding.
Why don't we schedule a 20 min call and talk about some of the insights and highlights from our research. We would also love to hear about your thoughts and concerns and take the opportunity to tell you what we are planning next.
How's your schedule for next week?
UBI Index is a Stockholm based startup which provides expertise in starting and transforming incubators. It was founded by Joel Eriksson Enquist as his master thesis at the Chalmers School of Entrepreneurship, Dhruv Bhatli, a Paris based researcher and entrepreneur, Lars-Henrik Friis Molin, a serial entrepreneur with over 20 successful companies to his credit such as Universum group, Job line etc. and Ali Amin, a dynamic student and entrepreneur. For more info: http://ubiindex.com.
Being a top university does not necessarily make a university incubation friendly. What then is the state of incubation at top 100 universities in the world?
This report assesses these universities on a range of criteria, e.g. preparedness, infrastructure, e!orts to promote incubation etc. to identify best practices and provide insights about the state of incubation at top 100 universities in the world.
Deep in the heart of your information systems, serious problems await. Learn how to find and resolve them before they harm your business! Our team of data scientists at the PSIKORS Institute will lead a workshop explaining the nature of the challenges to achieving rationalized data for meaningful data analytics, decision making, and enterprise applications. We will cover the critical factors impeding almost all organizations, and how to overcome them without organizational turmoil or following strategies shown time and again to fail. Dr. Geoffrey Malafsky, the chief Data Scientist, will lead the interactive day joined by experts, including a speaker from the Department of Defense. In this one-day seminar, we provide a framework and methodology to help you address critical business challenges in a practical, cost-effective way, regardless of which tools you use, or your data maturity.
Some complimentary passes are available to both Government and corporate personnel who are willing to provide a detailed use case example of their data rationalization challenges that can be discussed during the workshop. Submit your request and synopsis to Eric Kavanagh. Group discounts are available at registration.
Phasic Systems Inc has developed a unique agile methodology and core technology suite, DataStar, to solve the toughest data and management problems organizations face. DataStar is a cost-effective and efficient tool which uses agile business-centric modeling and integration. Agility focuses on producing value quickly instead of following structured methodologies. Our agile technology, together with our facilitated methodology, supporting products and integration, delivers the agile facilitated solutions other companies simply can't. It is easy to learn, easy to use and can handle every business variation using simplified standards-based semantics.
I think that is an excellent topic. Within that topic, I think we are technically and strategically aligned but are bound by respective focal points. That is, I state that a required precursor to the semantic tech is missing thus... while someone else believes either this precursor is not important or is assumed to be available.
Most of the data on slide 48 is FOUO at this time. I cannot distribute it but I may be able to walk through some examples in a web conference, or even better, coffee shop discussion. Some of it I can show since it is open source: FedBizOpps and FPDS (former has RFPs, latter has procurement award data). The key data that really opened up the solution and got DoD CIO excited was cross-correlating to the actual contracts and invoices per item per vendor per purchasing office.
Little to no correlation in raw data. DataStar performed the analysis to show the factual lack of alignment and led to Corporate NoSQL Common Data Model with integration and correlation. This is the crux of my belief that the really big problems with HC.gov are yet to even be recognized, and the semantic layers cannot possibly do this rationalization since the knowledge of how to merge, modify, eliminate data values is unknown by the people managing the source systems.
Since I always like to close with an example, I had breakfast this morning with someone who is a CIO of a major Fed agency. Simply shaking his head in disbelief that he cannot get sub-agencies to even acknowledge the serious problems in their data values while they want to spend much more money on ERPs that everyone agrees are flailing. He sums it up by saying no one wants to deal with the hard challenge so it is easy to spend money throwing a fad at the situation, whether that happens to be ERP, NoSQL, Hadoop, semantics, Informatica, or whatever.
I think the technical communities, and especially anyone actually working on large systems like HC.gov, have an obligation to provide ground truth as to the prerequisites and scope boundaries of each technology and not just be happy they were selected to play.
Geoff, Thank you for your comments. I agree this is not ready for a broadcast, but that is up to Eric.
I would like to see the data sets mentioned in slide 48 on Information Sources. Are they available?
since you have done the critique and I have done the data science leading to the Be Informed pilot by Kees van Mansom.
Are you available on February 4th at 7 p.m.?
I am glad we were able to calm things down and have a bit of a dialogue. Certainly I learned a lot about Jim and Kingsley's perspectives, and Kingsley's technology.
For a possible joint broadcast by you two, I still think there is a foundational piece missing. That is, Kingsley provides very good information below but I do not think he is addressing the actual issue I raised and I think I spent a fair amount of time articulating and giving examples for. Maybe this is just a symptom of the poor knowledge transfer in email. However, if we cannot focus on the same specific issue then the broadcast is not useful and will degenerate into each tribe saying things their respective tribes already know and want to hear (hmmm, sounds a lot like modern politics).
I completely defer to you two but I think you will have to solve this challenge before the broadcast or a joint panel can be meaningful.
co-references via owl:sameAs relations, indirect co-references via owl:InverseFunctionalProperty relations etc.., are examples of relation semantics that RDF enables one incorporate into data representation (be a transient or materialized view). The beauty of RDF is that these semantics don't sit confined in an silo (application, service etc..).
Data needs to flow, and the flow should be comprised of packets that include human- and machine-comprehensible relation semantics. Sadly, its taking way to long for everyone to understand that this is the meat of the matter, and that RDF is simply an open specification for addressing this issues in a uniform manner. There's no reason to be draconian about RDF if its essence is proper explained, which simply hasn't been the norm, hence the many misconceptions and poor applications etc..
Our break came when I realized there was no technical solution to this problem.
There are technical solutions to these problems. It's just data.
Also, there was no practical business process solution.
Again, there are, once the applications are constructed is a proper understanding of what data actually is. The trouble is that applications are constructed as silos from the onset, a majority of the time.
Many policies, and projects had come and gone without effect. Indeed, the creator of the still used Functional Areas and Functional Data Managers in the Navy works for PSI, Tim Traverso. I took a big step back and thought about how I could capture the requisite knowledge from reluctant humans. I treated the problem as a chemical kinetics situation and realized I could play one facet of the situation against another and adapt on the fly if one went in direction A vs B. Thus, our technology grew.
The solution had to be the same as engineering any complete complex system: continuous knowledge collection through nudging awareness and understanding forward incrementally while using technology to lower cycle time from knowledge capture to operational system to a few days with management approval at every step. This could not be achieved with the existing stable of tools and methods, whether downscale data modeling or upscale semantic modeling because the knowledge needed would be blocked. In addition, standard database tools and especially integration were so opaque and complicated that their cycle time was very high and their logic divorced from the upstream business rules that were continuously evolving.
The end goal was to merge the data such that accurate, auditable values for core data entities were produced, used, and approved at the same time that the large fraction of non-core values were gracefully handled but explicitly defined and scoped. This is what we did and do now. Note that Enterprise Architecture as a field has failed to deliver this solution.
Applications have dominated the enterprise and in doing simply perpetuated fundamental problems associated with the silo mindset.
Once this occurs, I am happy to send the data to a semantic framework, traditional DB, or typical BI tool. That is not my concern but what we do enhances anything that requires good, accurate, meaningful data to do its functions.
I am sensing the description of yet another silo since there's no mention of standard for data representation endowed with machine- and human-comprehensible relation semantics. Data is all about entity relationships and the relations they facilitate. That's what data is all about.
Thus, our approach to virtualization is to not put an access and brokering layer on top of existing sources as we do not want to propagate bad data leading to bad decision making.
Virtualization doesn't imply propagation of misleading data. It's the mechanism for homogenizing disparity.
Rather we want to use simplified and highly visible knowledge in an intuitive model that connects capture and representation to execution in existing servers while lowering the need for costly hardware. Hence Corporate NoSQL's use of NoSQL within traditional table structures.
NoSQL doesn't deal with use of URIs to facilitate data flow and representation in a uniform manner. Two NoSQL systems are incapable of transmitting data between themselves, for the reasons I just mentioned.
A couple of months ago a DeNodo person told me after one of the broadcasts I did with Eric that every single one of their clients struggles with data rationalization using their virtualization tool.
One size doesn't fit all. Never has an never will. A failed product doesn't define an entire domain of specialization.
- values within many elements in these records are widely variable including job (called rating and rate), seniority, credentials, etc.
Our break came when I realized there was no technical solution to this problem. Also, there was no practical business process solution. Many policies, and projects had come and gone without effect. Indeed, the creator of the still used Functional Areas and Functional Data Managers in the Navy works for PSI, Tim Traverso. I took a big step back and thought about how I could capture the requisite knowledge from reluctant humans. I treated the problem as a chemical kinetics situation and realized I could play one facet of the situation against another and adapt on the fly if one went in direction A vs B. Thus, our technology grew.
The end goal was to merge the data such that accurate, auditable values for core data entities were produced, used, and approved at the same time that the large fraction of non-core values were gracefully handled but explicitly defined and scoped. This is what we did and do now. Note that Enterprise Architecture as a field has failed to deliver this solution. Once this occurs, I am happy to send the data to a semantic framework, traditional DB, or typical BI tool. That is not my concern but what we do enhances anything that requires good, accurate, meaningful data to do its functions.
Thus, our approach to virtualization is to not put an access and brokering layer on top of existing sources as we do not want to propagate bad data leading to bad decision making. Rather we want to use simplified and highly visible knowledge in an intuitive model that connects capture and representation to execution in existing servers while lowering the need for costly hardware. Hence Corporate NoSQL's use of NoSQL within traditional table structures.
4. different kinds of replication.
We are going to showcase more of this in 2014 as we've been a little preoccupied integrating column-wise storage into the product. That and vectorzied execution is why we will be going beyond our 150 Billion Triples benchmark record to 500 Billion triples. .
 http://bit.ly/18ovowq -- Querying DBpedia from SQL using SPASQL (SPARQL inside SQL) .
Accepted and cooling down from me as well.
But, to introduce some more information about the basis of my view on EIW, I just left a meeting this morning at DODCIO with people involved in dealing with the remnants of EIW and whose tenure overlapped DWs. Straight from them is (paraphrasing without editorial comment): EIW failed; it only showed what we already knew from DMDC reports and did so with much higher labor and system costs without any additional analytical or reporting benefit. They had information that the EIW approach was not addressing real data and information needs and avoiding the tough challenges of incompatible data and large scale organizational resistance.
In fact, for my own ongoing work with DODCIO on rationalizing data from myriad program, financial, and asset inventory systems one major issue is how to circumvent the ubiquitous organizational resistance, and what I call civil disobedience, whereby the critical data necessary to merge is hidden. So, my technology is addressing this by using a common model with simple, clear semantic definitions of each entity but one in which I welcome extensions as a way to remove the ability of these groups to use standard civil disobedience excuses like we dont have resources, we have to do it this way, yada yada. This is a key ingredient of our approach and the PSIKORS model. Then, I cross-correlate data across data sources (much like evidence extraction) and move the combined data into the common model (in Corporate NoSQL - our tech). While this lacks the richer computable semantics using RDF and other tools, it adds in the critical capability to get human knowledge extracted, viewed, adjudicated, and implemented while removing most of the typical organizational impediments. That is, I seek to solve the overall challenge that includes humans and trade-off technology for this .
In fact, I came up with this approach by cherry-picking parts of approaches from DARPA and other R&D programs like Rapid Knowledge Engineering, your many fine programs, as well as techniques from my research days where a little friction at the outset can be channeled to produce faster, more useful, and more widely accepted insights from a group of strong willed, highly knowledgeable people.
Jim, the silly response about SPAWAR is profoundly ludicrous.
The other issues are not in contention. Get off your iphone and buy a real computer with a larger screen.
Seems we're talking about two areas of application here: 1) enterprise information management, which is Geoffrey's focus; and, 2) consumer technology, which has a vastly broader audience but decidedly different requirements.
A Defining Moment for Semantics? The Innovators Explain!
Brand and I could co-host the event, which would maybe last 75 minutes and feature 10-minute presentations by 3-4 experts, then some moderated discussion about the granular details.
We'd be happy to publish at InsideAnalysis.com, and maybe write something for Techopedia as well, with whom we've been working closely these days.
I do not disagree with any of your premises. I only differ in whether for the use case and total lifecycle being addressed whether the overhead of RDF is worth it. In what I am doing, the answer was no.
Indeed, because of performance issues I stopped transferring data among service interfaces in XML but solely in JSON. Same data load (meaningful info), but 2x smaller transfer size. For total transactional latency, this is important.
My platform, DataStar, allows management, correlation, analysis and editing of any number of data models, glossaries, code dictionaries, and business models (in PSIKORS metamodel) as the key feature of rationalizing data. That is, most places do not even know that they have nor have accurate versions of these artifacts. I just went through a major win for a client where DataStar automatically compared 7 versions of their major company data warehouse and identified the dissimilarities and points of assumption that were false, The results were detailed and easy to understand so were immediately shown to the senior managers who could make adjudication decisions on the spot. This is what we enable that is missing from almost all current approaches. We then convert that business decision into implementable data models (using Corporate NoSQL) that can go into existing DB servers since it is a methodology. But, I have to move the models from the server to the client and render them in a browser with JS, so any extra processing or transmission load is a big problem. I have unfortunately become very familiar with the many paths to memory leaks in IE.
Thus, without a doubt RDF could have done something useful here but the front end tools and the back end storage would make the process too opaque to get the understanding and decision making by the necessary people, and communicate the result in such a way that the IT group agrees. This is how we were able to garner 100% unanimous agreement in their steering committee which I had never seen before in either the IT realm, or the Nanotech/MEMS realm from whence I come.
I also agree with you that for Identity Mgmt, the additional information in RDF could be very useful. Yet, for other reasons, I have gone the other route. Every transaction in DataStar is checked multiple times both in the browser and on the web server and then again on the app server for correctness of the request and also consistency of the sessionID. If the latter does not match what I store (encrypted) for each user's login, DataStar sends a message and forces a logout. This is to maintain security at each transaction and to guard against unexpected commands buried in text strings. I can answer your next question: I do not use JS eval at all for this reason.
I agree wholeheartedly with your assertion about misuse of RDF (as well as other technologies). This is the crux of my critique. I am, and have been, a excited supporter of the promise of semantic technologies. But, I have seen it misapplied and over-touted to the point that it falls into the category of "typical IT fad", of which there are many. IT as a field seems unable to shake off weak engineering and stop squandering large sums of money and time chasing approaches that if we were talking about an electrical engineering signal processing application would never make it past the first meeting. I am flummoxed by all the dollars going to Informatica when most of the ETL is pt A --> pt B.
"That gives you what? Do all the entities relevant in the domain have HTTP URIs that resolve to their descriptors? Is this data reusable by any tool capable of making an HTTP GET without loss of semantic fidelity? "
Those are not relevant issues for creating and managing the enterpise scale structured data environment to feed applications (e.g. DIMHRS), reports, and analytics. It is the combining and existence and use of the data itself that is the focus. It is getting the data together that is the main hurdle, not using it once it is together. To use the same example, DIMHRS failed because it could not even begin to combine data.
This is within each service and command in each service let alone across the DoD. Even today, the Army has 6 ERPs and the number is growing. At least 2 of these act as data brokers under the naive idea that if they do not know how to combine data from 4 ERPs, #5 will make it easier.
A specific example to close with: the National Stock Number is supposed to be a well governed standard across logistics and ERP systems, and used as a primary key sometimes. The actual values stored in different systems varies, without documentation as to why, such that sometimes there is a prefix, sometimes a suffix, and sometimes not. Hence, ERP and logistics data is impeded from being meaningfully integrated, and the ERP programs are bleeding large dollars. No semantic technology can solve this because there is no knowledge on what to do about it and when to apply rules etc.
> Seems like we agree far more than we disagree. Also, I fully accept your more accurate definition of RDF. My point has to do with the latter parts which you also comment on. Namely, whther we discuss semantics or any other technology, it actually does a disservice to exuberantly tout capabilities that the technology just cannot deliver.
The thing is RDF is a specification for implementing solutions that add the profoundly difficult issue of heterogeneous data integration. Unfortunately, narratives went ary, in many different directions, so you ended up with a misconception that RDF was a magic bullet and RDF/XML the only kind of gun compatible with this magic bullet.
The real RDF i.e., the W3C specification that enables the uniform construction of webby structured data, endowed with machine- and human-comprehensible entity relationship semantics, is extremely capable of dealing with the challenge of heterogeneous data integration, that scales up to the Internet.
5. trust webs (clouds or networks).
Basically, how do enterprises and/or service providers constrain access to resources without compromising privacy?
What is privacy? Basically, self calibration of one's vulnerabilities in a given realm.
Through RDF it is possible to address the identity challenge without conflating any of the items outlined in the list above. Basically, using existing standards, it is possible for people and organizations to create verifiable identifiers that can be used to craft resource access controls (basically what NIST refers to as "Attributed Based Access Control" or ABAC). Likewise, actual identifier verification is based on loosely coupled authentication protocols all leading up the the ability to add Trust Webs/Networks/Clouds, to what we already have re, the Internet (computer network cloud), World Wide Web (document network cloud), and Linked Open Data (data network cloud).
 http://bit.ly/16EVFVG -- Venn diagram Illustrating intersections of Identifiers, Linked Data, and RDF.
> t can be augmented with heuristics and empirical rules, which is fine, but then should not be passed off as a wow factor result of probabilistic reasoning. My background is as a Nanotechnology research scientist who then went on to be a tech expert for troubled DARPA, ONR, DOE programs spanning novel solid state devices to real-time information fusion. I have watched and joined the excitement of the promise of computational reasoning as a major aid to language based computing. I still do. But, for large serious data environments where the source data is not well defined, cannot be easily made to align, and then encumbered with significant organizational resistance, semantic technology is a very poor choice because it starts with the assumption that the knowledge and authority exists to create well defined entities and the mappings from sources to these mappings. This rarely if ever occurs and is the primary reason the semantic approach has failed in almost all implementations going back to the '90s in Intel.
> Some of the very expensive failures along the way, using a little to a lot of semantics: DoD DIMHRS (no data rationalization whatsoever, $850M); NGA GeoScout (all talk, we showed up the Lockheed results with an Innovision IB prototype we built using the PSIKORS model, $1B); FBI Trilogy (actually a rather simple use case to solve but failed due to the reality of organizational civil disobedience and a naive view that IT can solve that, $250M); plus many others.
Soon to be wasting money: NIEM as a standard. No chance of success. No one in DoD commands, with the exception of the always well defined and documented METOC community, has any idea of how to map their sources to any type of model let alone an abstraction. I have seen this data very closely. This is why Wiznosky's program could not succeed and why it should have been framed from the beginning to address this issue rather than the Revelytix (smart folks) SPARQL framework.
You can judge any specification on what manifests in a single implementation. Too often, one failure associated with RDF (rightly or wrongly so) ends up being an indictment on the entire realm. I never understood this unique treatment of RDF -- bearing in mind my own experience with many data related standards, over a 20+ year timeframe. This could have followed but the initial issue is rationalization.
> I am on a project in DoDCIO now where I successfully brought together and rationalized data from 10 disparate completely misaligned sources using PSIKORS and Corporate NoSQL. No way anything using RDF and reasoning could do that.
You can't make blanket statements like that, especially when they are also inaccurate and quite trivial to refute, with the right RDF product in hand.
If I told you an RDF DBMS could toy with 50 Billion triples and be accessible to the Web for anyone to hammer, I bet you wouldn't believe that claim. If I told you it could happen at the 150 and even 500 Billion scales I bet you wouldn't believe that either.
Memory and CPUs are cheap today. DBMS technology has advanced at the storage and query execution levels.
> It came down to using PSIKORS as a simple way to figure out (no one told me) the context of various data (not from data dictionaries) and then I implement this is a new semantic model (Corporate NoSQL) and put the results into pretty pictures in QlikView in a web system.
That gives you what? Do all the entities relevant in the domain have HTTP URIs that resolve to their descriptors? Is this data resuable by any tool capable of making an HTTP GET without loss of semantic fidelity?
> The one place I disagree is with your statement: "There are no scenarios associated with modern data representation, access, integration, and management where reasoning over entity relationship semantics isn't a virtue. "
> The reason I disagree is the issue of deterministic data collated from disparate sources.
we decided RDF provided a open standard for exposing what we had been developing etc..
> I do not want any fungible results at all.
You are but one entity :-) The trick is having a solution that caters for heterogeneity at both the data, user, and operator levels. It all part of the challenge.
> I want completely auditable results with traceable logic and documented business rules of --both-- values and merging (aka integration).
An engine that make sophisticated use of RDF is your friend here. We have such technology.
> An example is my corporate year end taxes. No reasoning over entity relationships is desired nor allowed.
Then how are you going to grok the realm known as the tax code? You have to understand the terms and the semantics that connect the terms in the collection of statements that are ultimately processed by a database.
> I have had enough conversations with the IRS. This distinction is completely lost on the managers (technical included) who blindly go forth chasing either traditional approaches that have way more than enough examples to show they do not work (Informatica for all), to applying semantic technology without doing what every scientist and engineer should do every day and on every task they have: what is the scope of cost vs performance for various approaches to solving challenge x of N? I can solve almost any challenge in multiple ways. While not elegant, I can do all of the reasoning executed on RDF with simple metadata fields stored in JSON using a set of high level code routines.
You can use JSON to construct data that still bears RDF model based relation semantics. The data format doesn't matter one bit. It's about the relation semantics and their visibility to data processors that understand RDF.
> Indeed, I wrote quantum mechanic wave function analysis routines in Fortran to back up my experiments. The question is if this is the best use of time, money and produces the best result.
> I have a client in a large financial and risk services corporation that is deploying a brand new computational application for daily risk assessments as an Excel application deployed in Sharepoint. Not how I would do it, but it works.
All the stuff in Excel can be transformed into RDF model based Linked Data. That can be in the form of conventional ETL or through views over the data. You just need the right tools and the right people. The RDF community isn't homogenous, there are a variety of profiles within the community, as is always the case.
> While I do not disagree with you, I do not want to clarify use cases: "What's important is the flow of data across heterogeneous databases (or stores). You need to create views over data sources and then orchestrate how data flows across through these data sources using applications and service layering. "
> I do not care about flow if my goal is to centralize newly quality corrected, semantically integrated data. Indeed, I do not want to orchestrate flow if I do not trust the accuracy and meaningfulness of the data.
If you have a web of trust, linked data, and RDF based semantics, the concerns above are zapped, just like that. This isn't speculation, you just have to use the right tools.
> I specialize in data rationalization. I think that statement above is where I critique both semantic technology and traditional DB approaches: the false assumption that the existing data is accurate and trustworthy as is, or that a group of people with enough time and money know how to define rules to make it so. This is not the case.
There is no such thing as perfect data, what we need is the ability to construct data with context-fluidity in mind. This is where RDF is excellent and unrivaled.
Fine, I do know about DIMHRS. I was developing my technology at the time on contract to SPAWAR and very successfully merging all the grossing discordant Navy HR data for 1 Million people that had never been done before. We had technical interchange meetings with the DIMHRS technical leads who both admitted and were symied that the basic impediment was the complete inability to even know how to marge data from different sources, such as 3 versions of PeopleSoft, let alone do it within the program. It is this lack of appreciation for depth of the underlying real challenges that makes semantic technology unserious. I am standing on the tracks reading email and in no danger of being run over.
I agree 100% with Dr. Hendler with respect to both Semantic Technology and what he says about the US Government. I was most happy to get out of the Beltway for exactly those reasons.
Re EIW, Jim is the only person in this discussion close enough to have any idea what was going on. My name is not even spelled correctly by the commenters who know nothing about EIW and who are also misinformed about DIMHRS.
EIW did not fail. Every 90 days, EIW delivered exactly what it promised at a cost that was de minimis. Every experiment that we conducted with Semantic Technology worked exactly as was planned. Today, DoD the Business Enterprise Architecture (BEA) continues to follow the path of the Business Enterprise Ontology Framework (BEOF) that was modeled based on the EIW experience. This is the only practical way to achieve the Business Architecture federation that is necessary for the Department to finally figure out how to aggregate data with assured provenance.
Every bashing of this technology that I have seen is from people who have their own either personal axe to grind, or a substantial investment in his or her idiosyncratic methods and/or technology. Therefore there is an enormous bias not to change.
Within the Beltway, I fought this attitude everyday for five years. I have literally never seen this in the truly commercial semantic technology applications that Jim and Kingsley have mentioned.
My advice to those who continue to naively whisper about the past rather than listen to the howling whistle of the future is to get on this train or prepare to be run over.
Thanks but actually I do know about it, have worked with some, and have been working on the actual large data for some time deep into the data itself, the business analysis portion, and all sorts of technical approaches and tools. Planning for the future is not the same as making it happen. Also, there are plenty of lessons learned about prior attempts to lead a serious engineer effort to do better. This does not happen in Govt IT and did not happen in EIW. Govt actually does accept new ideas and spends a lot of money on them. This is not news.
Intelligence, possibly with your help, tried to use ontological approaches in the 90's and did not get far. Indeed, many large Govt programs like the notion of semantic techs but do not do the due diligence around them. I suggest that you, as a a highly respected expert, could do more to guide them to realistic uses and learn from the many past failures.
Additionally, I say again if you spent more time reading you would see that I carefully described the use cases I was addressing and the factual examples of what failed and why.
I have been a technical reviewer and made corrective plans on technologies such as info fusion, MEMS, and Nanotechnology encountering world class scientists each time. Every time the verbiage and personalities are the same and can be a rerun of each other. Our current discussion fits right in. However, once the facts (pesky critters those) are enumerated and a no-fault corrective strategy disclosed, everyone is happy and glad progress is made.
Indeed, I did one such project at Los Alamos/Sandia Labs on their Nanotechnology program for nuclear bomb fuses with me versus >80 scientists, some of whom I knew well from my own research days at NRL.
Incredible resistance. But, I marched them through the facts and existing lessons learned of what they were proposing and why it was not reasonable nor supportable. As usual, they grew to acknowledge and then made the dramatic leap to restructuring that part of the program making everyone, including Congressional oversight, happy. The issue they faced was a deep technical issue regarding atomic scale stiction and how they expected to solve it. Short answer: atoms are like toddlers and do not stay put despite your best efforts. The moral is that even the best people get caught up in belief over science sometimes and a focused objective helpful critique can benefit everyone.
One example that stuck with me and I used in my workshop Monday that you may find interesting.
I was brought into the DARPA program Small Unit Operations Situational Awareness System because it was flailing. They had reached a point of spending a great deal of political capital bringing in operational people and Grey Beards and the nascent technology was not doing well. My task was to find out what was going wrong with the information technology portion and produce a corrective plan. There were many other aspects to the program such as autonomous peer-peer parallel computing, mobile power management, node health monitoring and adjustments. The program was based on someones proposition that a non-linear Kalman filter could be used for real-time, power sensitive, peer-peer fusion with much greater precision of the result. One of the excellent companies often used in DARPA programs was highly resistant to me looking at their code but that is what I did. The fix was easy to find and propose. The section of their code where the non-linear matrix was supposed to be calculated was commented out with "TBD" and a simple linear extrapolation inserted. The team was flustered, embarrassed, but not chastised.
So, I continue to say you are not grasping the points being made.
Gents, thanks for the lively discussion!
On a tangential but interesting note regarding Open Government, I have an immediate need for speakers at a conference in Abu Dhabi, April 28-30. A gentleman named Richard Kerby from the United Nations is helping the government there put this conference together, and I said I'd submit a handful of names and bios to him today. Is anyone interested? All expenses are paid, but no speaker fees.
Thanks for the reply Jim, but I think you are overlooking the major issue. It is not whether semantics are being used somewhere successfully. That is not in contention. The entire discussion, well articulated, is about large scale disparate structured data environments and their needs to rationalize existing data into enterprise assets. I suggest you spend some time reading when you get to a laptop/desktop.
I have similarly spent a lot of years education people including those irrationally exuberant about any number of technologies that do not work in some misapplied, expensive, and high profile cases.
Thanks for the good discussion. Jim Hendler's is included below.
Seems like we agree far more than we disagree. Also, I fully accept your more accurate definition of RDF. My point has to do with the latter parts which you also comment on. Namely, whther we discuss semantics or any other technology, it actually does a disservice to exuberantly tout capabilities that the technology just cannot deliver. Semantic technology for all of its promise and power cannot provide the guaranteed, auditable, exact record x among all the others based just on reasoning algorithms. Never going to happen. It can be augmented with heuristics and empirical rules, which is fine, but then should not be passed off as a wow factor result of probabilistic reasoning. My background is as a Nanotechnology research scientist who then went on to be a tech expert for troubled DARPA, ONR, DOE programs spanning novel solid state devices to real-time information fusion. I have watched and joined the excitement of the promise of computational reasoning as a major aid to language based computing. I still do. But, for large serious data environments where the source data is not well defined, cannot be easily made to align, and then encumbered with significant organizational resistance, semantic technology is a very poor choice because it starts with the assumption that the knowledge and authority exists to create well defined entities and the mappings from sources to these mappings. This rarely if ever occurs and is the primary reason the semantic approach has failed in almost all implementations going back to the '90s in Intel.
Some of the very expensive failures along the way, using a little to a lot of semantics: DoD DIMHRS (no data rationalization whatsoever, $850M); NGA GeoScout (all talk, we showed up the Lockheed results with an Innovision IB prototype we built using the PSIKORS model, $1B); FBI Trilogy (actually a rather simple use case to solve but failed due to the reality of organizational civil disobedience and a naive view that IT can solve that, $250M); plus many others.
Soon to be wasting money: NIEM as a standard. No chance of success.
No one in DoD commands, with the exception of the always well defined and documented METOC community, has any idea of how to map their sources to any type of model let alone an abstraction. I have seen this data very closely. This is why Wiznosky's program could not succeed and why it should have been framed from the beginning to address this issue rather than the Revelytix (smart folks) SPARQL framework. This could have followed but the initial issue is rationalization.
I am on a project in DoDCIO now where I successfully brought together and rationalized data from 10 disparate completely misaligned sources using PSIKORS and Corporate NoSQL. No way anything using RDF and reasoning could do that. It came down to using PSIKORS as a simple way to figure out (no one told me) the context of various data (not from data dictionaries) and then I implement this is a new semantic model (Corporate NoSQL) and put the results into pretty pictures in QlikView in a web system.
"There are no scenarios associated with modern data representation, access, integration, and management where reasoning over entity relationship semantics isn't a virtue. "
mechanic wave function analysis routines in Fortran to back up my experiments. The question is if this is the best use of time, money and produces the best result.
I have a client in a large financial and risk services corporation that is deploying a brand new computational application for daily risk assessments as an Excel application deployed in Sharepoint. Not how I would do it, but it works.
"What's important is the flow of data across heterogeneous databases (or stores). You need to create views over data sources and then orchestrate how data flows across through these data sources using applications and service layering. "
Not always. I do not care about flow if my goal is to centralize newly quality corrected, semantically integrated data. Indeed, I do not want to orchestrate flow if I do not trust the accuracy and meaningfulness of the data.
define rules to make it so. This is not the case.
> Dennis, Kingsley, and Jim, Please see the email from Geoffrey Malafsky below and provide your responses in preparation for a panel discussion that I would like to have for the new Federal Big Data Working Group Meetup.
> 1) RDF et al: I frequently hear people, and I inferred the same from your questions, misunderstand the role of RDF in semantics. RDF is a format for storing information and as such is comparable to XML, UMLx XMIy, CWM, XML, and JSON.
2. Data Formats -- covers concrete syntaxes (notations) for actual content creation and serialization.
> It is what goes into the formatted structures that determines whether it is useful, meaningful, or relevant to actual semantics but the storage by itself and the specification of the formatting rules is not semantics.
Again, the misconception that a structured data representation format (e.g., RDF/XML) uniquely determines the semantic fidelity discernible from document content is just another misconception that arose for the same poor narratives that positioned RDF as an interchange format.
3. facts about instances of the aforementioned types above -- so called ABox.
At this juncture, I would like to assume that irrespective of assigned label (be it "RDF" or "something else"), what I am outlining is something fundamental to how observation is captured and expressed for reuse i.e., this is what data representation has always been about, in one guise or another.
> Nor is using RDF an entry requirement to be a semantic technology.
"RDF" the literals aren't important. The model theory associated with the W3C's RDF specification is what outlines an open standard (uniform) for structured data representation that's endowed with human- and machine-comprehensible entity relationship semantics. This is all about enabling us recreate digital sentences using patterns that predate silicon based computing. It's basic sentence structure and grammar tweaked for the the World Wide Web.
> Thus, questions about RDF should be framed in terms of what storage format is the best choice for the intended use of the information and technology, not as is often done, in regard to whether the technology is or is not a semantic technology. This view is false.
5. Anything else enables 3-tuple representation.
2. server side data management systems.
When data is created and persisted to documents, it ultimately needs to be aggregated, indexed, and stored in a database management system or store. Thus, when structured data bearing documents are created using RDF there is a natural need for a query language based on the same fundamental model theory.
SPARQL is the query language for performing create, read, update, and delete (CRUD) operations against RDF based structured data. It serves a role similar to what SQL offers to relational databases that manage data (entity relationships) modeled as records in a table (i.e., tables represent relations). Naturally, it goes further than SQL because in RDF relations take the form of 3-tuple (triple) statements that share a common predicate, some refer to this as a "relational property graph" (or graph) variant of a relational database, with regards to database management system types.
> There is absolutely nothing inherently preferable about SPARQL.
SPARQL combines performance and relations semantics exploitation on a scale that's simply impossible (yes, I said that) to achieve with a pure SQL database management system. I can demonstrate this claim to you on a whim, whenever, using our Virtuoso DBMS engine with supports both SQL and SPARQL.
> If the technology is primarily focused on semantic reasoning and uses RDF, then SPARQL is a good choice.
There are no scenarios associated with modern data representation, access, integration, and management where reasoning over entity relationship semantics isn't a virtue.
> If it is using graph theory based networks, then SPARQL is a bad choice. If it focused on deterministic governance controlled data analysis and I/O, then SPARQL is not even relevant.
RDF model theory is a kind of graph theory. Triples are directed graphs. I don't buy the claim above one bit. I have a live 50 billion triples+ instance of Virtuoso that's accessible to the entire planet for ad-hoc querying using SPARQL or even SQL (since we can even co-mingle both languages). You perform backward-chained inference, transitive closures, all kinds of graph analytics (topic clusters, distances that constitute relationships, and more).
> What is relevant is the total lifecycle of performance from initial load, indexing (if used), updates, transaction based access time, joins (god forbid), etc.
What's important is the flow of data across heterogeneous databases (or stores). You need to create views over data sources and then orchestrate how data flows across through these data sources using applications and service layering.
Thus far, I've deliberately made no mention of Linked Data because RDF and Linked Data aren't the same thing. RDF is a powerful mechanism for creating Linked Data which is itself a powerful solution for data virtualization across heterogeneous data sources, when the right product is used for the job at hand.
Remember, RDF requires entity denotation using IRIs, which means different kinds of identifiers can be used to craft RDF based structured data. In a sense, RDF mandates that statements (sentences) are constructed from words. On the other hand, RDF based Linked Data mandates that statements are created terms (i.e., word specialization that combine denotation and reference).
HTTP scheme based URIs provide a low-cost and high-impact (due its ubiquity) mechanism for creating terms, and when applied to RDF based structured data representation (i.e., triple based statement construction) you end up with a Linked Data Network (Cloud or Graph) abstraction atop the current World Wide Web (or internal HTTP based networks).
> This is where I do not think MarkLogic was a good choice for HC.gov because the use case is deterministic with real-time computations of analytic results (financial status).
> This use case is a very poor fit for backends that are in anyway non-deterministic such as any semantic reasoning or probabilistic engine, or high transactional latency storage structure and engines such as standard RDBMS engines using standards relational data models.
Not so if you have a hybrid DBMS like Virtuoso.
> Note that it is the combination of model and engine that determines efficacy for high load deterministic performance.
> I have always known that ML has modified from pure XML and Xquery to get their performance up. HC.gov merely shows that their improvements are not sufficient for the required environment.
XQuery isn't the tool for this kind of job. I'll just leave it at that.
> But the same is true of a traditional Oracle model and engine.
> HC.gov should not be using anything associated with semantic reasoning or RDF as these are entirely poor fits for the technology requirements.
3) Data rationalization is about the values and the continuous visible auditable control of these values in large heterogeneous data environments. This is the primary challenge facing almost all enterprise scale environments.
> The notion of building a new system occurs in some large programs, such as HC,gov and the ill-fated DIMHRS, but this is not really a practical option in most places due to the combination of cost and organizational authority zones. In many cases that have tried to build the grand new data system, they find out that they cannot actually improve the data in the new system because they cannot rationalize the data from the old systems due to lack of knowledge on all the tentacles of values and relationships among values that are largely undocumented. Hence, you end up with another 100M data system or poor data.
You need a Virtual DBMS engine. One that combines models, and is designed to work with a plethora of data format, access protocols, and query languages. This is why be embarked on the Virtuoso project circa. 1998. This is what lead us to the W3C and its Semantic Web effort i.e., they provided standards for implementing the solution we envisaged a long time ago, based on our backgrounds (DBMS engine and Data Access Middleware specialists).
> Finally, a personal truth be told moment. I frequently state that there is no evidence of large scale operational success of semantic technology over the past 20 years.
3. growing enterprise exploitation at the enterprise level -- we have many enterprise customers taking advantage of this technology .
> Many attempts; large investments; many proud public claims; but no actual production use in heterogeneous data environments.
There are few publicly available success stories, but that doesn't mean they don't exist. That said, I get your point since I am frustrated with the overall appreciation and exploitation of Virtual DBMS technology driven by RDF based Linked Data. The tendency is to produce a lot of toys tools that collapse once challenged, and in the process the concept as a whole gets written off.
The problem here is that we have a misguided expectation that technology that solves serious problems should cost $0.00. The pursuit is disguised as a request for Open Source editions of these products knowing fair well that the "free beer" (as opposed to "free speech") part of Open Source meme is really what most pursue.
8. http://dbpedia.org/sparql -- DBpedia SPARQL endpoint.
That is why I suggested you find some others that share you views so we have balance.
Yes, I am always interested in doing these things. I asked DODCIO about it and he responded with what I expected: in this environment there cannot be formal participation with attribution to the DoD- legal does not allow this except in a few cases anymore. But, he could be a drive by individual with knowledge and experience to share who happens to work in DODCIO.
The second issue that troubles him, and I assume Eric as well, is that there is a chance that this will degenerate into the high priests of semantics holding up the infidels for community stoning. He does not want to be involved in that situation. I enjoy such encounters although it would not be a useful expenditure of time. So, I defer to you whether this can be arranged as a point-counterpoint situation that can inject some much needed new perspective into the semantic community or whether I would have to rent a costume and come dressed as a lamb for slaughter.
Geoff and Eric, Thank you for inviting me to attend and answering my questions at the beginning because I had to take my wife for a pre-op and wasn't sure I would be able to return, which I wasn't.
Brand, Thank you for attending the morning portion of the workshop. I think you would have found the afternoon most enlightening as it dealt with the real use case of many of the questions you asked. But, I want to clarify some issues so we continue to understand each others perspective on semantics and how we are each using it to solve challenges in the data field.
1) RDF et al: I frequently hear people, and I inferred the same from your questions, misunderstand the role of RDF in semantics. RDF is a format for storing information and as such is comparable to XML, UMLx XMIy, CWM, XML, and JSON. It is what goes into the formatted structures that determines whether it is useful, meaningful, or relevant to actual semantics but the storage by itself and the specification of the formatting rules is not semantics. Nor is using RDF an entry requirement to be a semantic technology. Thus, questions about RDF should be framed in terms of what storage format is the best choice for the intended use of the information and technology, not as is often done, in regard to whether the technology is or is not a semantic technology. This view is false.
2) Similarly, the use of SPARQL, XQuery, SQL, or indexed search engines (such as the very good semantic search engine dtSearch that I use) is also a matter of preference determined solely by the intended use and required performance characteristics of the technology. There is absolutely nothing inherently preferable about SPARQL. If the technology is primarily focused on semantic reasoning and uses RDF, then SPARQL is a good choice. If it is using graph theory based networks, then SPARQL is a bad choice. If it focused on deterministic governance controlled data analysis and I/O, then SPARQL is not even relevant. What is relevant is the total lifecycle of performance from initial load, indexing (if used), updates, transaction based access time, joins (god forbid), etc. This is where I do not think MarkLogic was a good choice for HC.gov because the use case is deterministic with real-time computations of analytic results (financial status). This use case is a very poor fit for backends that are in anyway non-deterministic such as any semantic reasoning or probabilistic engine, or high transactional latency storage structure and engines such as standard RDBMS engines using standards relational data models. Note that it is the combination of model and engine that determines efficacy for high load deterministic performance. I have always known that ML has modified from pure XML and Xquery to get their performance up. HC.gov merely shows that their improvements are not sufficient for the required environment. But the same is true of a traditional Oracle model and engine. HC.gov should not be using anything associated with semantic reasoning or RDF as these are entirely poor fits for the technology requirements.
3) Data rationalization is about the values and the continuous visible auditable control of these values in large heterogeneous data environments. This is the primary challenge facing almost all enterprise scale environments. The notion of building a new system occurs in some large programs, such as HC,gov and the ill-fated DIMHRS, but this is not really a practical option in most places due to the combination of cost and organizational authority zones. In many cases that have tried to build the grand new data system, they find out that they cannot actually improve the data in the new system because they cannot rationalize the data from the old systems due to lack of knowledge on all the tentacles of values and relationships among values that are largely undocumented.
Hence, you end up with another 100M data system or poor data.
Finally, a personal truth be told moment. I frequently state that there is no evidence of large scale operational success of semantic technology over the past 20 years. Many attempts; large investments; many proud public claims; but no actual production use in heterogeneous data environments. This includes what is widely considered a boondoggle failure of Wiznosky's Semantic Web effort EIW which was killed (with my overt cheering) by DCMO in early 2013.
Thanks so much for your interest in our Data Science Workshop this coming Monday. I'm just touching base to make sure you have all the relevant details. And will anyone else be coming with you? We're trying to make sure we get the right amount of food for breakfast, lunch and a snack.
Monday, Dec. 16 -- 9 a.m. till 5 p.m.
Use PSI100 (letters PSI number 100) as a coupon code to avoid the fee. Topics to be covered will include a look at how to resolve the data quality issues facing Healthcare.gov, how to reconcile any number of data models or information systems; plus a workshop in which participants are encouraged to share their most daunting challenges, then watch Malafsky's team identify, distill and rationalize key data assets in tens of minutes.
Feel free to pass this along to a colleague who might be interested. If you'd like to see an event like this in your area -- or even as an on-site -- please just let me know. We're planning 2014 right now.


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1. Not getting enough sleep. It makes me cranky, spacey and sometimes physically ill. Not getting enough sleeps makes everything else on this list worse.
If only caffeine could fix all my problems.
3. People with expectations so high I will never meet them. I will try and fail. Then I will try again and fail again. I wish I didn't care if people liked me.
4.When our water stops working. This has been happening too often. It is happening again today. Our well and pipes keep freezing. We have even had a few gushers in the past month. We are terrible at plumbing. We keep buying pipe heaters. It keeps freezing anyway. Send help!
5. Being on the Internet too much. My eyes hurt, my wrists hurt. Type, type…blink, blink. I just have one more thing to say or do…and then one more. Again, send help!
6. Housework. I am not good at it, but think I should be. No matter what I think I am still not good at it.
7. Feeling like I am running out of time. How will I read all of these books, write all of the blogs, see all of these movies, play all of these games or paint all of these paintings by next week, or before I die?
8. When the dog won't stop barking. Startle reflex. Eye twitch. I become a character in a horror movie.
9. When I take too long to make a decision. Or even worse, do not make one at all.
10. When I compare myself to others.
What 10 things stress you out?
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I need a winter home in the tropics…hehe.
Those are all things we all worry about, except I don't have the water problems. Maybe you should "slow" down a little. Maybe for a week post one less blog entry. Or paint one less painting. Would that help you sleep more? Not sure when you do all your blogging,etc.
This is all just advice. I have no idea what it's like to have all those hobbies. Though I am happy to say my blog is officially in production with a designer. Yay!!
Hope you find some balance! I think you are doing great!
The sleep problem has more to do with my daughter and my husband. Jim has been waking me up a lot. Tiny-Small is in another wake up at night phase. I think she gets cold, but doesn't wake up enough to pull her own blankets up. Once I get woken up I have a hard time getting back to sleep!
They all sound so familiar! (even the water, no burst pipes here but a dodgy meter).
Fair play for laying it all out. I like "grant me the patience to accept what I cannot change, the strength to change what I can and the wisdom to know the difference".
Oh yes, Catherine, the serenity prayer has gotten me through some rough moments! Nice to have the reminder. I went to visit my mom today and just getting out was nice. I have a new perspective.
Frozen pipes would stress anyone out. We haven't had any solid frozen or bursting, but some mornings there are ice crystals in the bathroom toilet. Not a good sign, and those drains are very slow. I think it is just the ground being so cold.
As to the housework – your husband wants a wife, not a maid, and Tiny Small doesn't care. Just keep reminding yourself of that.
I can't help with the rest, but they are all familiar.
Ice in the toilet sounds pretty cold. We haven't had that yet. I hope you don't have any frozen pipes that burst this year. It sounds like you are dangerously close to having something freeze. I am going to send you warm pipe thoughts!
You are right about Tiny-Small not caring about a mess. She doesn't seem to even notice. I don't think my husband does either. I am pretty sure it bugs me the most.
Water issues always highlight the numbner of times I "wash" my hands in a day. Turns out I run my hands under the water every 5 minutes minutes while I'm attempting to clean something. I HATE NOT HAVING WATER.
We had weeks and weeks with no water in South Africa and my husband had to go down to the fish pond to get a buckt of water just to flush our toilets.
I totally understand that issue.
It is completely amazing how often we wash our hands! It is such a pain to not have water. Part of the problem is that it isn't supposed to get this cold here. Our pipes are shallow. The ground is so hard here. Now, with weather patterns changing we are having much colder winters. We just aren't built for that sort of thing.
I can totally relate – stress isn't rational so telling myself to relax never seems to help.
As for the water, I grew up in a house with exposed pipes, ugh. Are you leaving a faucet open (at least to a trickle) when sub-freezing temps are predicted? It's a waste of water, I know, but at least you'll increase the chances of at least one working faucet.
Good luck & hang in there, spring is in sight!
We do have a trickle going, but it seems to be freezing between the well and the pump! We have these pipes that come out of the ground and into our pump house on the side of the pump house that doesn't get any sun until the afternoon. We have installed pipe warmers, but there is one part we don't have an easy way to get electricity too. My husband is going to build a little box to cover that section of pipe and hopefully the extra insulation will make a difference. Crossing my fingers and looking forward to Spring!
I can relate to all of these. Especially the lack of sleep and the disorganization with money… AND decision making. I am HORRIBLE when it comes to any kind of decision. Just ask my husband, it drives him batty!
Frozen pipes-our used to freeze every year when we lived in our trailer. I lost a good hair dryer to those pipes. My husband finally bought a space heater and put it under there, to just plug in and turn on whenever the pipes froze. It worked beautifully. Then we bought a house and the first winter the pipes froze. WTH? It turned out a window had been left open in the basement. We haven't had problems with it since. Hang in there girl, and remember that Spring will get here eventually!
My husband and I are both slow decisions makers! We are ridiculous, but I guess at least we can understand each other even though things don't get done. I should appreciate that more somehow!


Improving your posture is not an easy task once your body becomes set in its ways. The obvious reason is that your joints lose their range of motion, your muscles become long in the wrong places and short in the wrong places, and everything gets tight. In my case, for example, the ribs and thoracic spinal joints do not move as freely as they should, and especially the upper thoracic spine is habitually curved forward. This places my head too far forward, placing strain on the whole axial system. This is not easy to reverse at my age, and I have been spending just over two hours a day at it for more than six months. (See here and here for more.) I am prepared to work on it for several more years, if necessary. I plan to live at least into my 90s and want to have a strong, flexible, pain free body for as long as possible.
One less obvious way that improving your posture is not an easy task is that habitual body position seems to be activity-specific. I am pretty good and improving at good posture while standing, sitting, and walking, for example, but only while doing extremely simple versions of those activities. Sitting in my truck, driving straight on the highway, it's easy to have good posture as long as I am thinking about it. Making a right turn, however, is a completely different deal, for two reasons. First, the attention that I use to remember posture tends to be taken up by the brain activity of making the turn. Second, it seems that my body has a way of making a right turn that is a gestalt: what I am looking at and for, what I am thinking about, how I move, and the position of my entire body is molded by the pattern and memory of 24 years of right-turn making.
So unravelling that and making right turns with good posture takes some doing. And that leaves left turns pretty much untouched, not to mention playing guitar, having an emotional conversation, or leading an underarm pass while partner dancing.
I was cutting up big pieces of plywood today, using a table saw. I tried to figure out how to do this series of motions while keeping my body in good alignment. I wished that I could have a construction-slash-posture coach there, helping me out. Then I started fantasizing about people who use table saws for a living getting trained like that. I have worked on construction crews, and if you are lucky you get trained how not to cut off your fingers, but you never get trained how not to have a painful back in ten or twenty years. If it was successful, I bet the extra cost would be more than made up for by the reduction in worker's comp claims.
On the other hand, it might not be successful. When I was first learning to dance, my teacher, Karly, spent some time emphasizing the importance of posture and moving my body into good posture. "Remember this," she said. "This is what good posture feels like when you are dancing." The problem was, I did not keep doing it. I think maybe I couldn't. It was too much to think about at the same time–the feel of leading, the moves I was trying to lead, and posture. It was overwhelming. For that to have worked, I think I would have needed Karly to insist on perfect posture and never moving on before I could lead each move with perfect posture. That would have been very slow. On the other hand, I am going to have to do all of that work anyway, so that I can dance without hurting my body. That is my next project with dancing–start over, re-learning the simplest moves with perfect posture.
Reanna and I just moved from Eugene, OR, to Joshua Tree, CA for $693.79. Our boxes were free, mostly from the Oakway Nike outlet dumpster. Our labor was free, mostly our own, plus a couple friends at key moments. We paid $218 for our 5×8 U-Haul trailer ($170 for seven days, plus $48 for trailer-plus-$5k insurance). Our U-Haul guy threw in an extra day (which we used) and a bag of packing blankets (which we used a few of).
We paid $70.71 for food. We ate at Casa Ramos in Corning for $22.76 and bought a date milkshake for $4.95 at Charlie Brown Farms in Littlerock, CA. The rest was for groceries.
The biggest expense was gasoline: $278.09 for 72 gallons for the 1,112 mile trip. That's only 15.44 miles per gallon, but not bad to move that much weight–6,900 pounds, including me and Reanna.
I also bought new tires before the trip ($310 on sale at Wal-Mart) but I am not counting that as a moving expense since I needed them anyway–my old tires were in OK shape for occasional town driving but ten years old with well over 100,000 miles on them.
So our total was $693.79, which is my most expensive move by far, but pretty good, I guess, for moving 40 years worth of possessions over a thousand miles.


Intense disputes over social issues are a feature, not a flaw, of a functioning democracy.
I am, however, calling for a dramatic reassessment of tactics. We need [to] adopt certain rules of engagement—norms to rein in the worst excesses of the culture wars.


text:
A look at using NotateMe with built-in PhotoScore technology
August 12, 2014 April 16, 2017 | by Philip Rothman
A bit of frustration
I moved on to try a real-world case: trying to transpose a B-flat trumpet part to a C part using nothing but NotateMe. I gave myself only 30 minutes to accomplish this task, figuring that if it took any longer to complete such a task I should scurry off with a pencil and blank staff paper to do the job instead. I had already familiarized myself with the basics of editing music in NotateMe; a user guide is available from within the app or online as a PDF.
I used a part from my new arrangement of "America the Beautiful" as a test. NotateMe's readout of the initial photo appeared to achieve 100% note accuracy, but had trouble with multirests, which are not currently supported in the scanning process:
The photo of the trumpet part to "America the Beautiful," taken on an iPhone 5 with NotateMe
NotateMe's initial processing of the trumpet part to "America the Beautiful"
First, I inserted empty bars to account for the missing measures, by simply double-tapping the edge of a bar. I then cleaned up the extraneous time signatures using the eraser pen. I deleted the extraneous markings in the same way, and then used the text pen to apply tempo, expression and technique text.
NotateMe's pen tools make it easy to adjust your work
At this point I was already worried about time. I would have liked to clean up slurs and dynamics, but I wanted to make sure I could finish getting the notes into a new staff.
By renaming my "Part 1" staff to "Trumpet", NotateMe recognized it as a B-flat transposing instrument. I created a Flute staff below it in order to copy and paste the music. (Copying music involves dragging your finger or stylus — I used the Adonit Jot Pro — across bars, and then tapping and holding to paste.)
A couple of problems: Somehow this erased the key signatures in the Flute part, and I was surprised that the music did not transpose to the key of the destination staff like it would have in Finale or Sibelius. So I had to reapply the key signatures, and then manually select the music on the Flute staff and drag it a step down to transpose it. Neuratron says that transposition is a much-requested feature, one that they are likely to add in the near future as a free update.
By that time, my 30 minutes were almost up. My results weren't pretty, but they were usable, and I was able to export a PDF to Dropbox. (The original trumpet staff is intact to show the comparison)
The result after a half hour of work in NotateMe (click for PDF)
To be fair, NotateMe's niche in the world of notation products is not creating publishable-quality output on its own. It shines much more as a convenient sketchpad that plays very nicely with other apps, due to its ability to export MusicXML and MIDI files. The aforementioned Dropbox integration worked seamlessly to keep the files in sync among my iPhone, iPad and Mac.
The better approach to accomplishing my task would have surely been to bring a MusicXML file over to a desktop notation program as soon as I had taken the photo in NotateMe. I could have also used Notion's iPad app, which has more notation features, but not the handwriting recognition element. But I wanted to see what was possible with no other software.
There are quirks with NotateMe, to be sure. For one, it doesn't feel as slick as a native iPad app, with its non-standard buttons, fonts and graphics. Neuratron said that this is a design decision, to lessen the impact of overly colorful native buttons and so on, which they believe take away from the purity of working with one's handwriting on virtual paper. To me, though, the net effect is akin to running a simulator inside iOS.
Measures in the handwriting space sometimes balloon to huge widths if, say, you try to draw a tie across a barline, because currently all editing in NotateMe must be performed somewhat unnaturally on a bar-by-bar basis. Neuratron says that they are looking at ways to improve this. And more generally speaking it can be maddening to draw something over and over that seems totally readable to a human eye but indecipherable to NotateMe, although with each update the app improves.
While the combined price of the app and the PhotoScore add-in ($70 US) is on the high side by the standards of mobile apps, Neuratron does offer a free version called NotateMe Now, which limits the user to working with a single staff.
One big improvement since the early days of the app: There is now a symbol tool pen that allows you to select pre-made musical symbols, and/or tell NotateMe to recognize something you've drawn.
Roundup for now
As said at the outset of this post, there are parallels to the early days of desktop software. I had to remind myself that there are so many features we take for granted now in desktop software that were not present in those early days, and Neuratron has promised continued development and additional features (not the least of which is an integration of its AudioScore technology, so that you can play or sing directly into NotateMe through your device's microphone).
Another parallel: the passion and accessibility of the small developer. I mentioned my frequently calling up Coda tech support many years ago; Steven Reading, in a recent interview for this blog, recalled telephoning Sibelius co-founder Jonathan Finn, whose personal number was published in the manual. Neuratron's similar passion is evident, with its CEO Martin Dawe actively replying to messages across e-mail and social media, actively soliciting feedback and pushing out frequent updates.
Educators and students have the most to gain from NotateMe at the present time, with its synthesis of sight, sound, and touch. As a professional sitting in front of a desktop most days and nights, I'm in the minority when it comes to the current trend moving toward the web and mobile platforms. So NotateMe, as it currently exists, isn't as crucial for my regular work as the familiar programs that have been with us for decades.
But, like most people these days, I carry my smartphone everywhere I go. Next time I'm stuck waiting at the doctor's office or on a stalled train, I'll surely be much more productive with, and grateful for NotateMe in my toolbox.
NotateMe is available from Apple's iOS store and Google Play for $40 (US and Canada), £28 (UK), €36 (EU) and $50 AUD (Australia), as well as in local currencies in most other countries.
The PhotoScore add-in is priced at $30 (US and Canada), £21 (UK) and €26 (EU), and can be purchased from directly within the app. The add-in requires a device with 1GB RAM and a 4MP camera. On Apple devices, this means an iPad 3 and later or an iPhone 5 and later.
It's worth mentioning that the next iPhone is widely rumored to be announced on September 9, appearing in both 4.7″ and 5.5″ sizes, which should be a boon for those wanting to use NotateMe more effectively on a larger smartphone screen.
The free NotateMe Now app, which limits the user to working with a single staff, is available from Apple's iOS store and Google Play.
Tags: android, audioscore, finale, ios, ipad, iphone, musicxml, notion, photoscore, scanning, score, sibelius, smartphone, tablet
Musitek introduces SmartScore Music-to-XML, a PDF music scanning converter
January 9, 2017 July 13, 2018
Musitek's new SmartScore Music-to-XML music notation recognition utility — the same feature originally planned for inclusion in Finale 25 —...
Speaking at MOLA's 2011 Conference
This past weekend I had the privilege of delivering a session on Sibelius to the delegates of the Major Orchestra...
Bach harpsichord concerto revived with a little help from PhotoScore and Sibelius
Sibelius user Wheat Williams got in touch with me to tell me about an interesting and worthwhile project that he...
A fascinating summary of how music notation methods have developed over the past 20 years!
I have NotateMe, but have so far found it of limited value – perhaps because my handwriting is so poor! I hadn't realised that it had been linked up with photoscore, and have just tried out their free 'one stave only' version – and am greatly impressed! It seems that the extra app costs about £21, but I would think this is very good value, judging from the results you obtained. I wonder how well it would work on hand-written manuscript?
Many thanks, Philip, for flagging up this exciting new development!
To me, after the PhotoScore add-in is this (quoted from the NotateMe site):
"Email PDF, MusicXML and MIDI files of your scores to friends or other musicians.
– Open in Sibelius, Finale and other desktop software
– Print to paper."
Game-changer, for sure!
Thanks, Philip
Neil Sands
We're having great results with NotateMe's Photoscore add-on. You need to learn its quirks and it's very unforgiving if your light conditions aren't good. But we're using it on a weekly basis here and it's immensely liberating.
Great to know, Neil, and thanks, Andrei and Gordon!
How much is the photoscore?
It's £21 if you're in the UK – probably somewhat less if you're in the USA! It's an 'in-app' purchase if you've got 'Notate me' installed on one of your devices already. Just click on Photoscore option, and it will take you to the app store.


text:
    Использование сторонних библиотек

    Yii тщательно разработан таким образом, чтобы сторонние библиотеки могли быть легко интегрированы для дальнейшего расширения функциональности Yii.

    TODO: пространства имен и объяснение composer'а

    Использование Yii в сторонних библиотеках

    Yii также может использоваться в качестве автономной библиотеки для поддержки развития и укрепления существующих сторонних систем, таких как WordPress, Joomla и т.д. Для этого включают следующий код в загрузочный код в сторонней системе:

    <?php
    highlight_string("<?php
\$yiiConfig = require(__DIR__ . '/../config/yii/web.php');
new yii\\web\\Application(\$yiiConfig); // No 'run()' invocation!
?>");
    ?>

    Данный код очень похож на код загрузки, используемой в типичном приложении Yii кроме одной вещи: он не вызывает метод run() после создания экземпляра приложения.

    Теперь мы можем использовать большинство возможностей, предлагаемых Yii при разработке усовершенствований в сторонних системах. Например, мы можем использовать Yii::$app для доступа к экземпляру приложения; мы можем использовать функции базы данных, такие как ActiveRecord; мы можем использовать модель и функции проверки и так далее.

    Использование Yii2 с Yii1

    Yii2 может использоваться вместе с Yii1 в одном проекте. В Yii2 используются имена класс из пространства имен они не будут конфликтовать с любым классом из Yii1. Однако есть один класс, название которого используется в Yii1 и Yii2, это класс «Yii». Для того чтобы использовать в Yii1 и Yii2 вам нужно решить эту коллизию. Для этого вам необходимо определить свой собственный класс 'Yii', который будет сочетать в себе содержание 'Yii' с 1.x и 'Yii' от 2.x.

    При использовании композитора вы должны добавить следующие строки в ваш composer.json, чтобы добавить обе версии Yii в проект:

    <?php
    highlight_string("<?php
\"require\": {
    \"yiisoft/yii\": \"*\",
    \"yiisoft/yii2\": \"*\",
},
?>");
    ?>

    Начните с определения собственного потомка от [[yii\BaseYii]]:

    <?php
    highlight_string("<?php
\$yii2path = '/path/to/yii2';
require(\$yii2path . '/BaseYii.php');

class Yii extends \\yii\\BaseYii
{
}

Yii::\$classMap = include(\$yii2path . '/classes.php');
?>");
    ?>

    Теперь у нас есть класс, который подходит для Yii2, но вызывает фатальные ошибки для Yii1. Так, в первую очередь, мы должны включить YiiBase из Yii1 исходного кода в наш файл определения класса 'Yii':

    <?php
    highlight_string("<?php
\$yii2path = '/path/to/yii2';
require(\$yii2path . '/BaseYii.php'); // Yii 2.x
\$yii1path = '/path/to/yii1';
require(\$yii1path . '/YiiBase.php'); // Yii 1.x

class Yii extends \\yii\\BaseYii
{
}

Yii::\$classMap = include(\$yii2path . '/classes.php');
?>");
    ?>

    Используя это, определяются все необходимые константы и автозагрузчик из Yii1. Теперь нам нужно добавить все поля и методы от YiiBase из Yii1 в класс нашего 'Yii'. К сожалению, нет никакого способа, чтобы сделать это, кроме как copy-paste:

    <?php
    highlight_string("<?php
\$yii2path = '/path/to/yii2';
require(\$yii2path . '/BaseYii.php');
\$yii1path = '/path/to/yii1';
require(\$yii1path . '/YiiBase.php');

class Yii extends \\yii\\BaseYii
{
    public static \$classMap = [];
    public static \$enableIncludePath = true;
    private static \$_aliases = ['system'=>YII_PATH,'zii'=>YII_ZII_PATH];
    private static \$_imports = [];
    private static \$_includePaths;
    private static \$_app;
    private static \$_logger;

    public static function getVersion()
    {
        return '1.1.15-dev';
    }

    public static function createWebApplication(\$config=null)
    {
        return self::createApplication('CWebApplication',\$config);
    }

    public static function app()
    {
        return self::\$_app;
    }

    // Rest of \YiiBase internal code placed here
    ...
}

Yii::\$classMap = include(\$yii2path . '/classes.php');
Yii::registerAutoloader(['Yii', 'autoload']); // Register Yii2 autoloader via Yii1
?>");
    ?>

    Примечание: при копировании методы, которые вы не должны были копировать - это метод "autoload()"! Также вы можете избежать копирования "log()", "trace()", "beginProfile()", "endProfile()" в случае, если вы хотите использовать Yii2 отладчик вместо Yii1.

    Теперь у нас есть класс 'Yii', который подходит для Yii 1.x и Yii 2.x. Так загрузочный код, используемый в вашем приложении выглядит следующим образом:

    <?php
    highlight_string("<?php
require(__DIR__ . '/../components/my/Yii.php'); // include created 'Yii' class

\$yii2Config = require(__DIR__ . '/../config/yii2/web.php');
new yii\\web\\Application(\$yii2Config); // create Yii 2.x application

\$yii1Config = require(__DIR__ . '/../config/yii1/main.php');
Yii::createWebApplication(\$yii1Config)->run(); // create Yii 1.x application
?>");
    ?>

    Тогда в любой части вашей программы Yii::$app относится к Yii 2.x приложению, в то время как Yii::app() относится к Yii 1.x приложению:

    <?php
    highlight_string("<?php
echo get_class(Yii::app()); // outputs 'CWebApplication'
echo get_class(Yii::\$app); // outputs 'yii\\web\\Application'
?>");
    ?>

redpajama_set_name: RedPajamaGithub


text:
Q: Java - How to fetch all rows data from database i'm making simple lab management system using java swing GUI where i need to print out all patients data from database table but i cannot found how to retrieve all rows data from database. With below code i'm getting only last row data.
private void inner_search_btnActionPerformed(java.awt.event.ActionEvent evt) {
    String search = this.search_field.getText();
    String name = null;
    try{
        if(search.trim().length() == 0){
            this.results_field.setText("Please fill in search field !!!");
        }
        else
        {
            String query = "select * from lab_tests";
            rs = st.executeQuery(query);

            int i =0;

            while(rs.next())
            {
                i++;
                name = rs.getString("patient_name");
                this.results_field.setText("" + name + "
");
            }

        }
    }
    catch(Exception ex){

    }
}

A: you are replacing text each time .that's why you are getting only final database record .use jtextareainstead jtextfield and use append method
textArea.append(name + "\n");

or you can use stringBuilder

A: best would be to make a domain class, something like
class Patient {
 private String fName;
 private String lName;
 private int age;

 public void setfName(String s){fName=s;}
 public String getfName(){return fName;}

 public void setlName(String s){lName=s;}
 public String getlName(){return lName;}

 public void setAge(Int i){age=i;}
 public int getAger(){return age;}

}

and in the function where you are getting data from the data base be like:
public ArrayList<Patient> getPatients(){

 public ArrayList<Patient> pList= new ArrayList<Patient>();

// part where you do you query on DB

 while(rs.next())
            {
                Patient p = new Patient();
                p.setfName(rs.getString("patient_name"));
                p.setlName(rs.getString(2));
                p.setAge(rs.getInt(3));
                pList.add(p);
            }

 return pList;

}

and then add that data to Jtable

redpajama_set_name: RedPajamaStackExchange