I have created a special version of CHDK for Canon S95 in a IkeLite housing. It is a "hack" and is not part of the normal CHDK distribution - use at your own risk.
To simulate the control wheel you must hold down S and press LEFT or RIGHT.
It is not easy to feel when the shutter button is half pressed and often you end up doing a full press.
Pressing LEFT or RIGHT works the same as holding down S and pressing LEFT or RIGHT. So in order to simulate turning the rear wheel simply press S to enter ALT mode, then press LEFT or RIGHT as needed and finally press S to leave ALT mode. Notice that pressing the UP button display the current value of the parameter assigned to the rear wheel. The UP button works in NORMAL and ALT mode.
Pressing SET or DOWN works the same as zoom in or out. So in order to zoom in or out press S to enter ALT mode, then press SET or DOWN as needed and finally press S to leave ALT mode. I have chosen the SET button in favor of the UP button because the UP button is not so easy to press in the IkeLite housing.
As soon as the SHUTTER is half pressed the display is "cleared". This gives a visual indication of the half shutter press and compensate for the lack of tactile feedback.
The shutter button, zoom button, front wheel and rear wheel have their normal function ALT mode. Thus it is now possible to take pictures in ALT mode.
From version 0.2 the new behavior in ALT mode is optional and disabled by default. To enable the new behavior.
Since I have changed the behavior of some buttons in ALT mode, there are some functions of CHDK that are not available anymore.
It is not possible to execute scripts in UW Housing mode.
Currently it only works when CHDK auto boot (Card Lock Boot Method). First install the normal CHDK distribution on a SD-CARD and make it auto boot. Then install my hacked version. CHDK can do lots of things, so spend a little time getting familiar with all the features.
I have only tested with firmware E and K, but I think the other firmware versions will also work. And remember it only works with the Card Lock Boot Method at the moment!
Changed position of ALT mode indicator.
Fixed manual focus in ALT mode. Some keys where not working as expected.
I use two Sea&Sea YS-01 flash.
When one flash is connected as slave to the other flash it seems that TTL is not working. I may attach both flash directly to the camera housing to see if that works.
When using two flash, they do not always sync correctly. Experiment with the flash in front of a mirror to check flash sync.
Adjust shutter speed. Slow shutter speeds results in blurred photos.
Adjust ISO. High ISO add noise to the photos.
Adjust aperture. Low aperture values adds more light, but reduce focus range.
In video mode exposure can be locked and adjusted. First press S to enter ALT mode, then press UP to lock exposure. Press LEFT or RIGHT to adjust exposure. Press UP to unlock exposure.
Assign front wheel to Manual Focus.
Use LEFT, RIGHT and UP in ALT mode to control shutter speed and aperture.
Use a low aperture value to create photos where the background is out of focus.
Use a low aperture value in low light situations and slow moving subjects.
Use a high aperture value to create photos where everything is in focus.
Use a high aperture value when there is a lot of light and fast moving subjects.
Use an aperture value around 3.5 as a starting point.
Use a shutter speed to 1/125 or faster to avoid blur.
Use an ISO value of 80, 100, 200, 400 to minimize noise.
In low light conditions select higher ISO, and keep the shutter speed at 1/125 or faster. Noise is better than blur.
If using an external flash remember to set the flash(s) to manual-mode no-pre-flash.
In this mode the camera may choose slow shutter speeds resulting in blurred photos. Select a high ISO value to avoid this.
Use an ISO value of 80, 100, 200, 400 to minimize noise. Or choose AUTO, but set max ISO in AUTO mode to 400.
Use LEFT, RIGHT and UP in ALT mode to control aperture and exposure compensation.
If using an external flash remember to set the flash to TTL-mode.
In this mode the camera choose aperture for you. If the camera choose a very low aperture mode the focus depth will be quite narrow you foreground be "out of focus".
Use LEFT, RIGHT and UP in ALT mode to control shutter speed and exposure compensation.
Customized "tips" for most buttons.
With custom made Sea&Sea Ball with Short Length Stem.


designed for all different learning styles. The resources have been used for over six years with over hundreds of children. We have seen amazing results and learning the arabic/qaidah has never been so much fun.


Process. Frame By Frame helps you develop information for the Internet or Video during an initial meeting to discuss user needs. This six step process will help to become familar with your needs and how you want to present yourself online.
In order to achieve the expected goals of a successful visual presence, we have developed a process consisting of several logical steps. These steps will help us to establish an e-business plan that fits your needs and budget.


text:
Buhari reveals cause of Boko Haram
November 20, 2018, 12:38 pm November 20, 2018 0 252
President Muhammadu Buhari on Monday, said that politics was the root cause of Boko Haram activities in the country, noting that the military alone cannot end the insurgency.
Buhari said successful counter-insurgency operations required what he called "unity of efforts across ministries, departments and agencies covering political, economic, law enforcement, intelligence and, of course, the military."
The President, who spoke in Abuja while delivering a keynote address at the maiden Nigerian Air Force International Air Power Seminar, with the theme 'Air Power in Irregular Warfare: A Framework for Understanding and Responding', however, stated that his vision was to build a stable and prosperous Nigeria.
He said: "Successful counter-insurgency operations would, therefore, require unity of efforts across multiple ministries, departments and agencies covering political, economic, law enforcement, intelligence and of course the military, in what is popularly called 'whole of the nation approach'.
"In this regard, we have mobilised relevant agencies and international organisations to support the counter-insurgency efforts to ensure success."
Continuing, he said: "It is common knowledge that though we require substantial military input in combating an insurgency, its root cause is inherently political in nature.
"This seminar is quite timely, in the sense that since the outbreak of the Boko Haram crisis in the North-East, the Nigerian Air Force and, indeed, the armed forces of Nigeria have been engaged in conducting counter-insurgency operations that have challenged the services' doctrine, tactics, training, order of battle and force protection architecture.
"Seminars like this are, thus, necessary for armed forces like the Nigerian Air Force to draw from the experiences of other armed forces that have either previously or are currently engaged in counter-insurgency operations."
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redpajama_set_name: RedPajamaCommonCrawl


Former Chelsea defender urges Blues to keep hold of Hazard | The Transfer Tavern | Where football transfer rumours fly or die!
Graeme Le Saux, speaking exclusively to 888poker.com, dubbed the PFA Player Of The Year as 'precious' and believes he's an integral part of getting Chelsea back on track this season.
Hazard seems certain to leave Stamford Bridge in the summer due to Chelsea's dwindling hopes of landing Champions League football, with the Blues just four points above the relegation zone, and our punters are worried it could lead to a mass exodus.
Chelsea fans, is Hazard the key to future success or is his exit inevitable? Let us know in the comments below!


The brilliant themes just keep on rolling over at Wandering through the Shelves. This weekly series has been a blast so far. I'm gonna get deep and dark on this one.
Oftentimes, as movie lovers, we delight in the choices of our favorite characters. We enjoy identifying with mistakes that people make, mistakes we've made ourselves. Other times, we cringe knowing that our hero is setting him/herself up for a huge embarrassment, even some catastrophe.
And then there are the mistakes that keep on coming. Mistakes that build tension, dread, suspense, even horror. Mistakes that haunt the characters. Mistakes that, in turn, haunt us, sending a sensation through our bodies that cannot be described with words.
Note: Please don't think I'm one-note. I know I used a Coen Brothers' movie last week as well. I can't help it. I'm just a huge fan.
Bill Macy's Jerry Lundegaard is one in a long line of Joel and Ethan Coen's victims. He is my favorite, though strong cases can be made for Oscar Isaac's Llewyn Davis in Inside Llewyn Davis, Billy Bob Thornton's Ed Crane in The Man Who Wasn't There, or Michael Stuhlbarg's Larry Gopnik in A Serious Man. He is a bumbling, nervous, weakling of a man to begin with, then the Brothers just go to work on him. It is one mistake after another. The film is masterpiece of tone, building a sense of tension and pitch dark comedy that no other movie, for me, has ever done before or since.
The Mistake: Involving oneself in a horribly conceived kidnapping plot. Just to start.
The great-grandfather of the "found footage" movie that is The Blair Witch Project is the scariest movie ever made in my opinion. I am not a fan of horror movies. Never have been. Jump scares make me angry. I prefer "scary" movies that are about real human fear. That is what this film is all about. It terrifies me every time. It is about obtaining fear in the mind, going into a dark place, and finding that fear is real...maybe.
The Mistake: Walking into a dark wood with not enough smokes and terrifying stories on the brain.
Todd Field's feature debut is one of the most well-made movies I've seen. It is utterly shocking, dark and tragic. It is about a mistake, one big mistake, that isn't really what one would normally consider a mistake. It's about the choice to get into a relationship, but, for those who haven't seen it, it is certainly not a "Fatal Attration"-type scenario. Nick Stahl plays Frank Fowler, home from college for the summer, who strikes up a relationship with an older, yet gorgeous, woman, recently separated from her husband and father of her child, played by Marisa Tomei. His parents, played by Tom Wilkinson and Sissy Spacek, warn their son that this may not be the best decision. He may be getting himself into more than he can handle. Are they right? This is a film of incredible power. The feeling I got in my stomach when this movie makes an early, totally unexpected turn in plot, has never been matched. And the performances from all the actors, especially Wilkinson and Spacek, are career best across the board.
The Mistake: Trusting that you're able, as a young man, to handle a relationship with a woman with so much more experience...and baggage. Of course, the outcome found in this film isn't typical, but these things happen.
Just a few hours ago I was going to buy this bargain of a Fargo DVD with lots of special features like Roger Deakin's commentary track... I ended up dismissing it, but now I'm regretting it. I've always loved it as much as you do. Big mistake!
I do that kind of thing all the time. I talk myself out of buying something I know I should have. Come to think of it, I need a Fargo upgrade. Think I might pick up the remastered Blu soon. Thanks for stopping by, man!
These are excellent choices, and ones I never would've thought of. Reminds me that I need to see In The Bedroom..
Thanks so much. In the Bedroom is so great. It will stick with you forever.
In The Bedroom... GAH I love that movie so much. Although I would say that the Irreparable Mistake is what happens at the end - how does he come back from that?!?
Also: No, I have not seen Blair Witch since I saw it in theaters, and no, I am not ashamed of that. That thing is freaking SCARY.
Agreed. I didn't want to say too much about it, so I shyed away from how that first decision by the Stahl character really leads to real mistakes. Such a dense, haunting movie. You are so right on about Blair Witch. I was in high school when it hit theaters. Then, I re-watched it about 5 or 6 years later, by myself, at night. That was a mistake.
OMG! In the Bedroom is in my Top Ten movies...OF ALL TIME!!!! Love that movie so fricking much! Great application of the theme this week.
Now go watch Mean Creek!
Thanks man. One of my favorites ever as well, though it's been hard for me to revisit it. Mean Creek is going on my Netflix queue ASAP.
Fargo is a great movie. Mistake #1 was killing someone in Marge's jurisdiction. Sorry, but he only thing I found scary about Blair Witch was how boring it was. And I hate that ending. I know I'm in the minority on that one, so don't mind me. Haven't seen your third choice. Sounds interesting, though. Glad you're still playing along.
Good call, Wendell! You don't involve yourself in any "malfeasance" in Brainerd, MN, when she's running the show. I find Blair Witch brilliant. Always have. After The Exorcist, it's the only movie that ever made me lose sleep. I could not get it out of my head.
Good pick with In the Bedroom...wow seems like such a long time ago when I saw it.
Thank you. Great theme this week! Need to re-watch myself.


text:
Centre officials discuss successful flood repairs
EMMA GOSALVEZ, egosalvez@lockhaven.com
PHOTOS PROVIDED These before-and-after photos show flood repair and prevention work done at Howard.
BELLEFONTE — It has been less than a year since last October's severe storm flooded parts of Centre County, and now, 10 affected landowners' properties have been repaired.
After parts of the Bald Eagle Valley and Coleville areas were affected by the flooding that took place on Oct. 21, 2016, the county commissioners and the county conservation district requested federal assistance to repair damaged properties. Back in March, the county submitted a federal grant application request to the Emergency Watershed Protection program, which is run through the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service.
The total project cost was $203,995, and the county was ultimately awarded $187,000 in funding, with the Pennsylvania Department of Environmental Protection funding the remaining 25 percent.
During the Oct. 10 meeting of the Centre County Board of Commissioners, Bob Sweitzer, manager of the county conservation district, discussed the completion of the projects, which helped out 10 eligible landowners in Coleville, Julian and Howard with rehabilitation, stream bank stabilization and sediment deposition. The program was created to address erosion and flooding concerns regarding streams that impact homes and businesses located within 50 feet of such streams.
The USDA Natural Resources Conservation Service evaluated more than 30 sites to determine funding eligibility, and originally, it was thought that only five landowners were eligible, Sweitzer said. Five additional impacted homes were discovered to be eligible for the funding.
In early June, the county did a request for bids and awarded all of the projects to Steven Krieger Excavating of Saxton, Pa. The company's bids totaled $187,800.
Work on the first impacted property began on July 17, Sweitzer said. Final site inspections were completed on Aug. 4. In addition, the landowners signed an agreement to provide operation and maintenance for the projects to their property for a period of 10 years.
"I would like to thank the legislators, the townships, the governor, for all their help and assistance, and hopefully, if we have weather like this again, we won't have damage quite as bad," said Commissioner Mark Higgins.
In other business, the commissioners declared the month of October as "Domestic Violence Awareness Month."
According to Dawn McKee of the Centre County Women's Resource Center, the center is hoping to make its awareness more widespread this year. Typically, there is an awareness event held on the lawn of the Centre County Courthouse, but this year, a travelling exhibit was created.
This wall, McKee said, shares stories of local individuals who were killed as a result of domestic violence. The wall, called "The Empty Place at the Table," features a place setting for each victim, with a story behind the place setting. There are 13 to represent known victims, and one for unknown victims.
"It remembers the people in our community who were killed as a result of domestic violence, but it also talks about things that we can do as a community to increase safety for victims and hold offenders accountable," McKee said.
Throughout the month, the exhibit will travel to the county's four YMCAs and to the Penn State campus, McKee said. Last week, the exhibit was at the Moshannon Valley location, and it is currently at the State College location. Next Sunday, the exhibit will be moved to the Bellefonte YMCA, and it will be at the Penns Valley YMCA the following week.
For more information about the CCWRC and how to help in the community, visit www.ccwrc.org.
CM alumni Alex Garbrick reveals future baseball plans
Brain drain and the polarization of America
Summer Library Program promotes learning and literacy


text:
Kenny Omega talks about AEW's tag team division
Dec 2, 2020 - by Steve Gerweck
"I never lost faith in the pursuit of (tag team success). There are a lot of people who probably don't know my history and what I accomplished in Japan or overseas. I enjoy tag wrestling and think it's an art form in itself and it's a different challenge than putting together a singles match. Especially with AEW, we built the tag division to be the greatest in all of the promotions. I have a lot of faith in our tag division, there are some incredible athletes and teams. The best part of that is that these are all teams that are committed to being the best tag team and the alpha dog in our division. It was awesome to run the gauntlet and work with all these guys. For me, to have this long story and relationship with Hangman Page and explore our characters more so within that division was really rewarding. I'm really glad that Hangman was allowed to show his personality. Inside and outside of wrestling, he has one of the most fun characters to watch in pure form. With how many people we have in AEW, it's tough to see everyone, so you have to make the best of every situation. You would have thought that maybe our characters would be watered-down in the tag division because we have more bodies to deal with, but being able to play off each other, really helped tell the story of camaraderie and motivation of who we are as people. Now that we're out of the situation, you're going to know more about us as people in our singles careers. I'm happy to be able to work with one of my friends and exit out when it was time to do the singles thing, and coming out and having a stronger character for both of us."
source: Fightful
Good News For Lucha Underground?
Jim Ross: "Braun Strowman, in my opinion, is the future face of the WWE"
Source: Mood at the PC today – "uncertainty"
Wrestler claims Ember Moon stole her gimmick
Roman Reigns first wrestled Shad Gaspard in FCW


text:
English Language & Usage Stack Exchange is a question and answer site for linguists, etymologists, and serious English language enthusiasts. Join them; it only takes a minute:
How should the first "o" in "operator" be pronounced?
I've always been taught that a vowel before a double consonant following another vowel should have a short sound. Conversely, there are many situations where a vowel preceding a single consonant and vowel gets a long sound.
Short Sounds:
Mississippi    - All I's except the last get short sound
Communication  - First O gets short sound
Oppose         - First O gets short sound where second O gets long
Long Sound:
Ape            - A gets long sound
Popery         - O gets long sound
Yet, I hear people use a short O in "operator" when using the word. Is this the correct pronunciation?
pronunciation speech
Chad HarrisonChad Harrison
Whether it is a short-o or long-o depends on another factor: syllabification. Operator: op-ra-tor; here, the first syllable rhymes with hop. So, you may ask how to syllabify English words? It is a complex question, which was tackled by many folks like Kahn, C-J. Bailey, etc. – RainDoctor Aug 20 '12 at 22:24
Indeed, it is. 'Operator' is pronounced ˈɒpəreɪtə (IPA), with the o- sound of 'offense' or 'orange'.
Most spellings as we use them today were standardized in the late 18th century on a fairly arbitrary basis. Most choices were based in common transliteration habits, but others followed historical convention. In this case, 'operator' had been historically spelled with a single p thanks to its root the Latin 'operari', and so the habit stuck.
Edit: I'm aware our American cousins have some regional variations, but these accents evolved more recently.
Jimmy Breck-McKyeJimmy Breck-McKye
I guess commonality will always trump phonetics. – Chad Harrison Aug 20 '12 at 17:08
I have to disagree with the "offense"/"orange" comparison; for me the "o-" in "operator" is like the o in "hop", while "offense" starts with "aw" and "orange" is clearly an "or". – Hellion Aug 20 '12 at 17:14
Southerners in the US tend towards "Awringe". – Chad Harrison Aug 20 '12 at 17:21
@Mitch Fifty years ago I could have told you where a Southerner came from within fifty miles or so. Not any more; my ear's destroyed from living in Missouri, and I doubt that degree of regionalism prevails any more. – StoneyB Aug 20 '12 at 19:27
Concur: remove "orange". The first syllable is commonly pronounced like the word "or", as well as the word "are". – MetaEd♦ Aug 21 '12 at 6:55
In America, most dialects don't distinguish between RP /ɒ/ and /a/, so operator is normally pronounced with [a]:
['apəˌɹeiɾɚ]
Standard American is rhotic, with [ɹ] instead of [r] and final [ɚ] instead of [ə]; and the /t/ is reduced to a tap [ɾ] between a preceding stressed vowel [ei] and a following unstressed vowel [ɚ].
John LawlerJohn Lawler
isn't the first vowel [ɑ]? I thought that vowel belongs to the LOT set. – RainDoctor Aug 20 '12 at 22:19
There's only one low back vowel in American English; I use "a" for it because it's on my keyboard. In any event, it is not the same vowel as the one in orange or offense, which is /ɔ/ for most Americans, at least East of the Mississippi. Except on the West Coast, where the two phonemes merge so that Don with /a/ and Dawn with /ɔ/ are homophonous. – John Lawler Aug 20 '12 at 22:28
The first "o" in "operator" is pronounced with the "short o" sound found in the word "lot" (typically transcribed in the IPA as /ɑ/ in American English, and /ɒ/ in Southern British English.)
It is indeed the case that a single vowel letter tends to be pronounced as a "short vowel" before a double consonant.
But the inverse is not true: it is not uncommon for a single vowel letter to be pronounced as a "short vowel" when it does not precede a double consonant. Actually, there isn't any one rule that tells you how to pronounce a single vowel letter that comes before a single consonant letter and another vowel letter. Before certain common vowel-initial suffixes (such as -ing, -ed, -er, -est and -y) a stressed vowel tends to be "long" before a single consonant letter (note that words like fading, closest, biker, stony have as their bases words that end in "silent e": fade, close, bike, stone) but there are a number of endings that show the reverse pattern (e.g. verbs ending in -ish, such as famish, perish, vanish, finish, or adjectives ending in -id, such as livid, placid, intrepid) as well as miscellaneous words that just happen to have a short vowel before a single consonant, like camel (in contrast to e.g. label).
In operator, what's relevant is that there are more than two syllables after the stressed syllable. In this context, all single vowel letters except for "u" tend to be pronounced as "short" vowels. This tendency has been given various names, e.g. "Luick's law". It can be seen as related to the phenomenon of "three-syllable shortening" or "trisyllabic laxing", but there are different theoretical analyses that deal with the details in different ways (e.g. "trisyllabic laxing" is often specifically used to refer to a rule about the pronunciation of derived words, and while "operator" is etymologically derived from Latin opus, operis, it seems very doubtful that it should be analyzed as being synchronically derived from the English word "opus").
sumelicsumelic
The phonetics rules of thumb can be useful, but there are always exceptions. This is one of them.
Thanks for contributing an answer to English Language & Usage Stack Exchange!
Not the answer you're looking for? Browse other questions tagged pronunciation speech or ask your own question.
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text:
Anaea ornata är en fjärilsart som beskrevs av Johannes Karl Max Röber 1916. Anaea ornata ingår i släktet Anaea och familjen praktfjärilar. Inga underarter finns listade i Catalogue of Life.

Källor

Praktfjärilar
ornata

redpajama_set_name: RedPajamaWikipedia


text:
#=============================================================================
# Copyright 2006-2009 Kitware, Inc.
# Copyright 2006 Alexander Neundorf <neundorf@kde.org>
#
# Distributed under the OSI-approved BSD License (the "License");
# see accompanying file Copyright.txt for details.
#
# This software is distributed WITHOUT ANY WARRANTY; without even the
# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the License for more information.
#=============================================================================
# (To distribute this file outside of CMake, substitute the full
#  License text for the above reference.)

# use pkg-config to get the directories and then use these values
# in the find_path() and find_library() calls
find_package(PkgConfig QUIET)
PKG_CHECK_MODULES(PC_LIBXSLT QUIET libxslt)
set(LIBXSLT_DEFINITIONS ${PC_LIBXSLT_CFLAGS_OTHER})

find_path(LIBXSLT_INCLUDE_DIR NAMES libxslt/xslt.h
    HINTS
   ${PC_LIBXSLT_INCLUDEDIR}
   ${PC_LIBXSLT_INCLUDE_DIRS}
  )

find_library(LIBXSLT_LIBRARIES NAMES xslt libxslt
    HINTS
   ${PC_LIBXSLT_LIBDIR}
   ${PC_LIBXSLT_LIBRARY_DIRS}
  )

find_library(LIBXSLT_EXSLT_LIBRARY NAMES exslt libexslt
    HINTS
    ${PC_LIBXSLT_LIBDIR}
    ${PC_LIBXSLT_LIBRARY_DIRS}
  )

set(LIBXSLT_EXSLT_LIBRARIES ${LIBXSLT_EXSLT_LIBRARY} )

find_program(LIBXSLT_XSLTPROC_EXECUTABLE xsltproc)

if(PC_LIBXSLT_VERSION)
    set(LIBXSLT_VERSION_STRING ${PC_LIBXSLT_VERSION})
elseif(LIBXSLT_INCLUDE_DIR AND EXISTS "${LIBXSLT_INCLUDE_DIR}/libxslt/xsltconfig.h")
    file(STRINGS "${LIBXSLT_INCLUDE_DIR}/libxslt/xsltconfig.h" libxslt_version_str
         REGEX "^#define[\t ]+LIBXSLT_DOTTED_VERSION[\t ]+\".*\"")

    string(REGEX REPLACE "^#define[\t ]+LIBXSLT_DOTTED_VERSION[\t ]+\"([^\"]*)\".*" "\\1"
           LIBXSLT_VERSION_STRING "${libxslt_version_str}")
    unset(libxslt_version_str)
endif()

include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
FIND_PACKAGE_HANDLE_STANDARD_ARGS(LibXslt
                                  REQUIRED_VARS LIBXSLT_LIBRARIES LIBXSLT_INCLUDE_DIR
                                  VERSION_VAR LIBXSLT_VERSION_STRING)

mark_as_advanced(LIBXSLT_INCLUDE_DIR
                 LIBXSLT_LIBRARIES
                 LIBXSLT_EXSLT_LIBRARY
                 LIBXSLT_XSLTPROC_EXECUTABLE)

redpajama_set_name: RedPajamaGithub


Sorry this is coming late but I only got back in the office yesterday and had an opportunity for a face to face with Alex.
Please see email thread below and attached analysis regarding "spillover parking". WE wish to add this to the City's documentary evidence.
The City's two (2) witnesses may wish to refer to this information during their evidence. If there are any objections, I suggest we address them at the time.
The amendment was unanimously endorsed by Planning Committee and adopted by Council in July 2016, with no members of the public speaking against, and no appeals. We're already starting to see developments with no parking, marketed to car-free urban households.
In the course of developing this amendment, we produced a whack of background research. One of the most important documents was a new theoretical framework explaining why you don't necessarily need to require on-site parking to control "spillover"--the single biggest concern raised by stakeholders. What we came up with probably isn't a perfect theory, but I think it's definitely better than the... um... shall we say questionable logic that has historically underlain the minimum parking requirements in North American cities. It helps to deal with that question you'll always be hit with, "How can you justify doing this new thing when we've been doing it the other way for decades?"
I've taken the liberty of attaching the Spillover Parking document. It was always our hope that other cities would be able to benefit from the work we did, if they're inclined to take a hard re-think of their own parking requirements (or even in evaluating individual parking variances.) The plan was to put all of this together into some kind of formal publication but here we are a year and a half later and other stuff just got in the way.
We are writing today to let you know that the City of Ottawa is undertaking a review of the minimum parking requirements under the Zoning By-law. These are the rules that specify how much on-site parking must be provided as part of any new development.
Ottawa's parking minimums have not been comprehensively reviewed in decades; in key areas, the parking requirements are the same under today's zoning as they were in 1964. A lot has changed since then, and these old parking rules are increasingly out of step with today's realities and planning goals.
Recognizing this, the new Official Plan adopted in 2013 calls for these zoning requirements to be reduced in certain parts of the city, notably near rapid-transit stations and in the inner urban area.
As of May 4th, you can access the project website at ottawa.ca/minimumparking. We plan to hold a public open house in the fall of 2015 and expect to bring a zoning amendment to Council in 2016.
In the meantime, the project website includes a short discussion paper that talks about the pros, cons and consequences of the City's parking requirements, as well as some of the possible changes under consideration. It's a good introduction to the issue of parking requirements, what changes we're considering making and why.


text:
4 big reasons Trump tariffs could cost consumers
Marcie Geffner
January 19, 2017 in Savings
Trump's tariffs and consumer impact
TIMOTHY A. CLARY/Getty Images
Economists generally believe free trade between the U.S. and other nations benefits the U.S. economy and consumers. Though some jobs are sacrificed, tariff-free imports mean prices are significantly lower for a vast range of consumer products.
The U.S. has signed trade deals involving 20 countries. The most important is the North American Free Trade Agreement, or NAFTA, which removed trade barriers between the U.S., Canada and Mexico.
Renegotiating or tearing up existing trade deals would reverse the benefits for consumers, says Stuart Anderson, executive director of the National Foundation for American Policy, a research organization in Arlington, Virginia.
"A small percentage of people in the protected industries might end up being personally somewhat better off, but it would come at the expensive of everyone else who would pay higher prices for different goods and services," Anderson says.
Without trade deals, the challenge for consumers would be how to cope financially with those higher prices, which could hit big-ticket items like cars, newly built houses and appliances as well as everyday purchases like groceries, housewares, clothing and toys.
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Cost of consumer goods could rise
Drop of Light/Shutterstock.com
The cost for consumers could be significant and painful, especially for lower-income households.
A study by researchers at Suffolk University in Boston found the tariffs that president-elect Donald Trump has proposed on imports from China, Japan and Mexico could cost the typical U.S. household $11,000 over five years.
The effect would be regressive, which means it would hurt poor people more than the wealthy.
If the proposed tariffs encompassed the entire world rather than only China, Japan and Mexico, the cost to U.S. consumers could be even higher.
"A Trump tariff levied on imports from all countries would cost the average U.S. household $6,112 annually and $30,560 over a 5-year period," the researchers wrote.
According to the Bureau of Labor Statistics, the U.S. inflation rate was only 1.1 percent in the first half of 2016. The highest inflation in the last decade occurred in 2008, when the monthly average rate was 3.8 percent.
Historically, inflation has at times been much higher.
It's impossible to predict how high the rate might rise due to trade protectionism or which products might be most affected. Possible targets can be surmised from the list of countries that account for the highest dollar volume of U.S. imports. The top six on the list are China, Mexico, Canada, Japan, Germany and Korea.
Substitutes could become pricey
The obvious solution for consumers is to substitute different, yet similar products for those that become more costly due to tariffs. And, indeed, that has happened historically.
Anderson says that when U.S. tariffs were applied to particular goods from certain countries in the past, other imports were found and procured from other countries. That's why tariffs against just a few countries probably won't work.
Moreover, as prices for certain products rise, prices for substitutions are likely to increase as well since competitors will try to capitalize on the opportunity, says Alfred McIntosh, principal at McIntosh Capital Advisors, a financial planning and investment management firm in Los Angeles.
Worse yet, tariffs have a way of multiplying, and not in a consumer-friendly way.
"Once you start with protectionism, it starts to become open-door for pleadings from different entities that would love to have protection for their particular products," Anderson says.
Higher prices could slow economic growth
Another solution for consumers is to earn more to offset higher costs, but that's not easy to do.
McIntosh says consumers could try to find a second income, ask their employer for a raise or become self-employed to boost their income-earning opportunities, but "there's no certainty" that any of those scenarios will work.
"Wage levels have been stagnant for decades," he says.
The other solution for consumers is to make sacrifices, though that's not easy either.
"Considering how tight most families are with their budgets, there is not much cutting that they will be able to do," McIntosh says.
Over the long term, rising prices and diminished consumer spending can lead to slower economic growth, softer housing prices and smaller families.
"The less prosperous you are, the fewer kids you have and the smaller the home you buy," McIntosh says.
You might be tempted to buy now
Consumers might well wonder whether they should buy cars, major appliances and other expensive items now before any Trump tariffs take effect and products become more costly.
A 2013 working paper by research economists at the Federal Reserve Bank of Boston found that consumers who anticipated rising prices didn't purchase more appliances or electronics but were more likely to buy a car.
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McIntosh says high inflation is "absolutely an incentive to buy today instead of waiting until tomorrow," but the purchase still has to make sense.
"If you believe you're going to have the income, and you believe prices are going to go up for an extended period and (the purchase) is a need, not a want, then maybe it makes sense to buy early," McIntosh says. "The problem is, it's hard to know."
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Economists survey: Say goodbye to cheap gas?


text:
\section{Introduction}
This paper deals with an emerging problem in the design of Big data applications: reasoning over large volumes of semantically annotated data streams. The main goal amounts to producing sound and complete answers to a set of continuous queries. This problem is quite important for many Big data applications in domains such as science, social and Internet of Things (IoT) in general. For instance, in the Waves\footnote{http://www.waves-rsp.org/} project, we are dealing with ``real-time" anomaly detection in large water distribution networks. By working with domain experts, we found out that such detections can only be performed using reasoning services.

Tackling this issue implies to find a trade-off between data throughput and reasoning over semantically annotated data streams. This is notoriously hard and although it is currently getting some attention, it is still an open problem. RDF Stream Processing (RSP) engines are the prominent systems tackling this problem where annotation are using the RDF (Resource Description Framework)\footnote{https://www.w3.org/RDF/} data model, queries are expressed in a continuous SPARQL form and reasoning are supported by RDFS/OWL ontologies. Existing RSP engines are either not scalable (\textit{i}.\textit{e}.,  they do not distribute data and/or processing) or do not support expressive reasoning services.

Our system, Strider\textsuperscript{lsa} , combines our Strider RSP engine with a reasoning approach, denoted LiteMat. Strider\cite{DBLP:journals/corr/RenC17} is a stream processing engine for semantic data taking the form of RDF  graphs. It is designed on top of state-of-the-art Big Data components such as Apache Kafka and Apache Spark. It is thus the first RSP engine capable of handling at scale high throughput with relatively low latency. Strider is capable of processing and adaptively optimizing continuous SPARQL queries. Nevertheless, it was not originally designed to perform inferences. Hence, the motivation is to integrate an extension of our RDFS LiteMat\cite{DBLP:conf/bigdataconf/CureNRA15} inference  approach. Intuitively, it provides a trade-off between the most common reasoning methods encountered in  Knowledge Bases (KB): query rewriting and materialization.

The LiteMat extension we are considering in this work integrates the popular, \textit{e}.\textit{g}.,  in Linked Open Data (LOD) KBs, \texttt{owl:sameAs}  property. Intuitively, this property enables to define aliases between RDF resources. This is frequently used when a domain's (meta)data is described in a collaborative way, \textit{i}.\textit{e}.,  a given object has been given different identifiers (possibly by different persons) and are later reconciled by stating their equivalence. We discovered several of these situations in the context of the Internet of Things (IoT) Waves project. For instance, we found out that sensors or locations in water networks could be given different identifiers.
Generally, the triples containing a \texttt{owl:sameAs}  property are not present in the streaming data but are rather stored in static metadata, \textit{e}.\textit{g}.,  a RDF store\cite{Cure:2014:RDS:2785631}. Such metadata are needed to perform valuable inferences. In the Waves project, they correspond to the topology of the network, characteristics of the network's sensors, etc. We consider that the presence of static metadata can be generalized to many domains, \textit{e}.\textit{g}.,  life science, social, cultural, and it is hence important to design a solution to reason over their data streams.

The main contributions of this paper are (i) to combine a scalable, production-ready RSP engine that supports reasoning services over RDFS+ (\textit{i}.\textit{e}.,  RDFS + \texttt{owl:sameAs} ) KB, (ii) to minimize the reasoning cost, and thus to guarantee high throughput and acceptable latency, and (iii) to propose a thorough evaluation of the system and thus to highlight its relevance.

\section{Background knowledge}
\subsection{RDF, SPARQL, RDFS and OWL}
Data on the Web is generally represented using RDF, a schema-free data model.
Assuming disjoint infinite sets I (RDF IRI references), B (blank nodes) and L (literals), a triple (s,p,o) $\in$ (I $\cup$ B) x I x (I $\cup$ B $\cup$ L) is called an RDF triple with s, p and o
respectively being the subject, predicate and object.
We now also assume that V is an infinite set of variables and that it is disjoint with I, B and L.
SPARQL is the W3C query language recommendation for the RDF format.
We can recursively define a SPARQL\cite{sparql11} triple pattern (tp) as follows:
(i) a triple $tp \in$ (I $\cup$ V) x (I $\cup$ V) x (I $\cup$ V $\cup$ L) is a SPARQL triple pattern, (ii) if $tp_1$ and $tp_2$ are triple patterns, then ($tp_1 . tp_2$) represents a group of triple patterns
that must all match, ($tp_1$ \texttt{OPTIONAL} $tp_2$) where $tp_2$ is a set of patterns that may extend the solution induced by $tp_1$, and ($tp_1$ \texttt{UNION} $tp_2$), denoting pattern alternatives, are
triple patterns and (iii) if $tp$ is a triple pattern and C is a built-in condition, then, ($tp$  \texttt{FILTER} C) is a triple pattern enabling to restrict the solutions of a triple pattern match according to the expression C. A set of tps is denoted a Basic Graph Pattern (BGP). The SPARQL syntax follows the select-from-where approach of SQL queries.

We consider that a KB consists of an ontology, aka terminological box (Tbox), and a fact base, aka assertional box (Abox). The least expressive ontology language of the Semantic Web is RDF Schema\cite{rdfs11} (RDFS). Its goal is to provide a mechanism allowing to describe groups of related resources (concepts) and their relationships (properties).
RDFS entailment can be computed using 14 rules. But practical inferences can be computed with a subset of them.
The one we are using is $\rho$df which has been defined and theoretically investigated in \cite{Munoz:2007:MDS:1419662.1419670}. In a nutshell, $\rho df$ considers inferences using \texttt{rdfs:subClassOf}, \texttt{rdfs:subPropertyOf}, \texttt{rdfs:range} and \texttt{rdfs:domain} properties.
An RDF property is defined as a relation between subject and object resources. RDFS allows to describe this relations in terms of the classes of resources to which they apply by specifying the class of the subject (i.e., the domain) and the class of the object (i.e., the range) of the corresponding predicate. The corresponding \texttt{rdfs:range} and \texttt{rdfs:domain} properties allow to state that respectively the subject and the object of a given \texttt{rdf:Property} should be an instance of a given \texttt{rdfs:Class}.
The property \texttt{rdfs:subClassOf} is used to state that a given class (i.e., \texttt{rdfs:Class}) is a subclass of another class.
Similarly, using the property \texttt{rdfs:subPropertyOf}, one can state that any pair of resources (i.e., subject and object) related by a given property  is also related by another property.

Other ontology languages, OWL\footnote{https://www.w3.org/TR/owl2-overview/} (Web Ontology Language) an its fragments, of the Semantic Web stack are more expressive than RDFS, \textit{e}.\textit{g}.,  supporting \texttt{owl:sameAs} . The deductive process has a higher computational cost which can become a problem if low latency is expected.

Two main approaches are generally used to support inferences in KBs. The first approach
consists in materializing all derivable triples before evaluating any queries.  It implies a possibly long loading time due to running reasoning services during a data preprocessing phase. This generally drastically increases the size of the buffered data and imposes specific dynamic inference strategies when data is updated. Besides, data materialization also potentially increases the complexity for query evaluation (\textit{e}.\textit{g}.,  longer processing for table scanning). These behaviors can seriously impact query performance. The second approach consists of reformulating each submitted query into an extended one including semantic relationships from the ontologies. Query rewriting avoids costly data preprocessing, storage extension and complex update strategies but induces slow query response times since all the reasoning tasks are part of a complex query preprocessing step.

In a streaming context, due to the possibly long lifetime of continuous queries, the cost of query rewriting can be amortized.
On the other hand, materialization tasks have to be performed on each incoming streams, possibly on rather similar sets of data, which implies a high processing cost, \textit{i}.\textit{e}.,  lower throughput and higher latency.

\subsection{RDF Stream Processing (RSP)}
The nature of stream processing is to run a set of operations over unbounded data streams. Such processing are generally performed within a windowing operator that slices the incoming infinite data streams into finite chunks. These windows can be defined over some temporal constraints, \textit{e}.\textit{g}.,  take the last 3 minutes of incoming data, or over non-temporal parameters, \textit{e}.\textit{g}.,  counting or session based.

In the last decade, much effort has been devoted to improving RSP. Systems like C-SPARQL \cite{CSPARQL} , CQELS \cite{CQELS}, SparqlStream \cite{SPARQLStream} and ETALIS \cite{ETALIS} provide a SPARQL-based continuous query language to cope with RDF data streams. The aforementioned RSP systems give a basic solution to perform SPARQL query over RDF data stream.
Whilst, C-SPARQL and SparqlStream tackle the requirements of stream reasoning via data materialization and query rewriting, respectively. The proposed straightforward solutions, \textit{i}.\textit{e}.,  data materialization and query rewriting show limitations on systems' scalability. Moreover the centralized design of C-SPARQL and SparqlStream can not support complex reasoning task over massive RDF data stream. On the other hand, distributed RSP engines, \textit{e}.\textit{g}.,  CQELS-Cloud, address flexibility and scalability issues but do not possess of real-time stream reasoning.

In summary, although RSP engines have substantially improved in recent years, none of them cover the scalability and expressive reasoning aspects.

\section{Strider\textsuperscript{lsa}  overview}
In this section, we first give a high-level view on the architecture of Strider\textsuperscript{lsa}  (see Figure \ref{fig:architecture}).

\begin{figure}[ht]
\vspace{-2mm}
\advance\leftskip-0.3cm
\includegraphics[scale=0.4]{Architecture.png}
\caption{Strider\textsuperscript{lsa}  Architecture}
\label{fig:architecture}
\end{figure}

Strider\textsuperscript{lsa}  consists of two principal modules: (1) data flow management. For the purpose of ensuring high throughput and fault-tolerance, we use Apache Kafka to manage the data flow of Strider\textsuperscript{lsa} . The incoming data are assigned to  Kafka \emph{topics}, and partitioned among a cluster to enable parallelism of upstream/downstream operations. (2) Computing core. The query processing pipeline is implemented in the Apache Spark programming framework. Spark Streaming continuously receives data from Kafka, and performs SPARQL queries executions in parallel. We briefly illustrate the general process of query processing and stream reasoning in Strider\textsuperscript{lsa}  as follow: With a given ontology, the Tbox encoding layer runs a mostly distributed off-line knowledge base encoding for further reasoning request. Once a query is registered, the system will, if necessary, first rewrite the query into a LiteMat (see \ref{subsec:litemat} for more details) representation and construct the corresponding query logical plan. Then, Strider\textsuperscript{lsa}  pushes the obtained logical plan into the query processing layer for a continuous SPARQL query evaluation. Note that, Strider\textsuperscript{lsa}  is capable of adjusting the query execution plan at-runtime via its optimization layer.

\section{Reasoning in Strider\textsuperscript{lsa} }

\subsection{Encoding}
\label{subsec:litemat}

\subsubsection{Static TBox encoding}
\label{stbox}
\textbf{Concept and property hierarchies  encoding} is needed upfront to any data stream processing. The TBox Encoding Layer encodes concepts, properties and instances of registered KBs.  This aims to provide efficient encoding scheme and data structures to support the reasoning services associated to the input ontology of an application.
The input ontology is considered to be the union of (supposedly aligned) ontologies necessary to operate over one's application domain.

In the following, we consider the $\rho$df subset of RDFS and use our LiteMat approach\cite{DBLP:conf/bigdataconf/CureNRA15}. To address inferences drawn from \texttt{rdfs:subClassOf}, \texttt{rdfs:subPropertyOf} , we attribute numerical identifiers to ontology terms, \textit{i}.\textit{e}.,  concepts and properties.
The compression principle of this term encoding lies in the fact that subsumption relationships are represented within the encoding of each term.
This is performed by prefixing the encoding of a term with the encoding of its direct parent (a workaround is proposed to support multiple inheritance).
This approach only works if an encoding is computed using a  binary representation.

More precisely, the concept (resp. property) encoding are performed in a top down manner, \textit{i}.\textit{e}.,  starting from the top concept of the hierarchy (the classification is performed by a state-of-the-art reasoner, \textit{e}.\textit{g}.,  HermiT\cite{hermit}, and hence supports all OWL2 logical concept subsumptions), such that the prefix of any given sub-concept (resp. sub-property) corresponds to its super-concept (resp. super-property). Since, the lengths of concept (resp. property) hierarchy branches may be  different, the system normalizes all identifiers to ensure that they are of the same length. The characteristics of this encoding scheme ensures that from any concept (resp. property) element, all its direct and indirect sub-elements can be computed with only two  bit shift operations and are comprised into a discrete interval of integer values, namely its lower and upper bound (resp. LB,UB). Table \ref{table:ssn} presents the identifiers of the encoding of the Semantic Sensor Network (SSN) concept hierarchy. We can observe that the \texttt{Action}'s prefix 111110 corresponds to \texttt{Event}'s identifier, and his hence one of its direct sub-concept. Moreover, \texttt{Event} is a direct sub-concept of \texttt{Entity} and indirect sub-concept of \texttt{owl:Thing}.
More details on the encoding scheme can be obtained in \cite{DBLP:conf/bigdataconf/CureNRA15}.

\begin{table}[!h]
\centering
\begin{tabular}{|l|c|l|}
\hline
Raw ids & Normalized ids & Term\\
\hline
1 & 1000000000 & \texttt{owl:Thing}\\
\hline
101 & 1010000000 & Input\\
\hline
110 & 1100000000 & Output\\
\hline
111 & 1110000000 & Entity\\
\hline
111001 & 1110010000 & Abstract\\
\hline
111010 & 1110100000 & FeatureOfInterest\\
\hline
111011 & 1110110000 & InformationEntity\\
\hline
111100 & 1111000000 & Object\\
\hline
111101& 1111010000& Quality\\
\hline
111110 & 1111100000 & Event\\
\hline
1111100010 & 1111100010 & Action\\
\hline
\end{tabular}
\caption{Encoding for an extract of the concept hierarchy of the SSN ontology}
\label{table:ssn}
\vspace{-5mm}
\end{table}

\textbf{Individual encoding} can take two different forms, depending on whether an individual is involved in a triple with a \texttt{owl:sameAs}  property or not. For non-sameAs individuals, we apply a simple method which attributes a unique integer identifier (starting from 1) to each individual. In \cite{DBLP:conf/bigdataconf/CureNRA15}, we provided an efficient distributed method to perform this encoding.

\begin{figure}[ht]
\includegraphics[scale=0.38]{sameAsSolutions.png}
\caption{\texttt{owl:sameAs}  representation solutions}
\label{fig:sameAsRep}
\end{figure}

The support for individual involved in triples with \texttt{owl:sameAs}  properties (denoted sameA has been integrated in  our LiteMat approach in the context of this work. It is motivated by the popularity of \texttt{owl:sameAs}  across domains of the LOD. For instance, the \texttt{owl:sameAs}  constructor is frequently encountered to practically define or maintain ontologies. In \cite{Halpin10whenowl:sameas}, the authors measured the frequency of \texttt{owl:sameAs} ~in an important repository of LOD. That property was involved in 58,691,520 triples over 1,202 unique domain names with the most popular domains being biology, \textit{e}.\textit{g}.,  bio2rdf and uniprot (respectively 26 and 6 million triples involving \texttt{owl:sameAs} ), and general domains, \textit{e}.\textit{g}.,  DBpedia (4.3 million \texttt{owl:sameAs} ~triples).
Moreover, the knowledge management of LOD, estimated to more than 100 billion triples, clearly amounts to big data issues. In our Waves running example, we also found out that, due to the cooperative ontology building, RDF triples containing \texttt{owl:sameAs}  properties were necessary to re-conciliate ontology designs.

In order to reason over KB supporting the \texttt{owl:sameAs}  property, we can use different approaches. An obvious and naive one materializes all inferences. For instance, from Fig.\ref{fig:sameAsRep}(a), we obtain Fig.\ref{fig:sameAsRep}(b) where all red properties correspond to materialization, resulting in doubling the number of triples (without materializing the symmetric aspect of \texttt{owl:sameAs} ).
Thus one of our challenge is to support sameAs reasoning while answering queries without materialization. In Strider\textsuperscript{lsa} ,~we address this challenge with an approach consisting in selecting a single individual among a clique of sameAs individuals (in Fig. \ref{fig:sameAsRep}(a), three cliques are represent in dotted ellipses). This approach has many advantages, especially in a data streaming context: (i) the inferred graph is more compact without loss of information than the  original graph, (ii) with the proper approach and dictionary data structures, the computing overhead of transforming the graph is not issue and (iii) clique updates (\textit{e}.\textit{g}.,  removing or adding an individual from a clique) has no performance impact since the data streams are ephemeral. Fig.\ref{fig:sameAsRep}(c) is an example of this approach where individuals a,b and c represent the so-called representatives of the cliques.

Our sameAs encoding method aims to guarantee advantages (i) and (ii). This is performed by encoding individuals involved in a sameAs clique with a binary tuple identifier where the first value correspond to a clique identifier and the second one to a local identifier in that clique.

In a nutshell, a static RDF dataset is parsed to extract all triples involving  a \texttt{owl:sameAs}  property. From that graph, a connected component operation is computed to define all the cliques. Then each clique is attributed a unique  integer value and for each clique, each individual is also given a unique integer value. A dictionary, taking the form of a hash map, is created out of this processing: the key is the IRI or literal of the individual and the value is the tuple identifier. This dictionary can be reversed to translate query results. This processing is computed in a distributed and parallel manner using the Apache GraphX component. Hence it is able to scale to very large static KBs.

\begin{figure}[ht]
\advance\leftskip-0.3cm
\includegraphics[scale=0.45]{Encoded_RDD_1.png}
\caption{Parallel partial encoding over DStream}
\label{fig:partialEncod}
\vspace{-3mm}
\end{figure}

\subsubsection{Dynamic Abox stream encoding}

In the previous section, we have encoded the elements of the static KB, \textit{i}.\textit{e}.,  its concepts, properties and individuals. In this section, we explain how data streams are encoded on the fly and how it is mixing static (Tbox and Abox) and dynamic elements (Abox only since it is not probable that ontology modifications are streamed). In the following, we consider that concept, properties and individuals can or can not be in the precomputed dictionaries (nevertheless, note that the absence of concepts and properties in the dictionaries prevents from any reasoning tasks upon them). For instance, it makes sense that blank nodes in data streams are not in our individual dictionary while IRIs and literals may be present. In case of absence, the idea is not to encode the triple element since it would be too costly (generating a unique integer for a possibly infinite set of potential values) and it would possibly not be worth it since that identifier is essentially ephemeral. We thus qualify this encoding as a partial one and describe it in Fig.\ref{fig:partialEncod} which uses the Discretized stream(Dstreams) \cite{dstreams} abstraction of Apache Spark Streaming. Intuitively, a Dstreams is a set of RDD (Resilient Distributed Datasets)\cite{DBLP:journals/cacm/ZahariaXWDADMRV16} which are partitioned over a set of machines. Each RDD is composed of a set of RDF triples. For each RDD, a transformation is performed which takes a IRI/literal based representation to a partially encoded form. This transformation lookups into the TBox and Abox dictionaries precomputed from static KBs. The bottom right table of the figure emphasizes that some triple elements are encoded while some other are not. To apply our representative-based approach, each sameAs individual is replaced only with its clique identifier. Each non-sameAs individual is replaced with its corresponding integer identifier.

We briefly summarize important advantages of the partial encoding of RDF streams: (i) an efficient parallel encoding to meet real-time request; (ii) no extra overhead for dictionary maintenance; (iii) an off-line dictionary encoding on \texttt{owl:sameAs} with no extra overhead for materialization of data stream.

\subsection{Query Rewriting and Processing}
\label{qrew}
The query rewriting in Strider\textsuperscript{lsa}  is done in the Inference Layer. Intuitively, the system parses a given SPARQL query Q and rewrites it into Q'. This rewriting concerns inferences pertaining to concept and property hierarchies. For sameAs individuals, no specific rewriting is necessary due to our data streams encoding. Due to space limitations, we do not present the rewriting of SPARQL queries into Spark SQL Scala programs but the interested reader can find details on our github page.

\subsubsection{Rewriting pertaining to hierarchies}
In Section \ref{stbox}, we highlighted that to each concept and property corresponds a unique integer identifier. Moreover, one characteristic of our encoding method guarantees that all sub-concept (resp. sub-property) identifiers of a given concept (resp. property) are included into an interval of integer values, denoted lower bound (LB) and upper bound (UB) of that ontology element.

We first present the rewriting for concepts. In order to speed up the rewriting, we take advantage of the following context: concepts are necessarily at the object position of a triple pattern and the property must be \texttt{rdf:type}. Intuitively, if a concept has at least one sub-concept then it is replaced in the triple pattern by a novel variable and a SPARQL \texttt{FILTER} clause is added to the query's BGP. That filter imposes that the new variable is included between the LB and UB values (which have been previously computed at encoding-time and stored in the dictionary) of that concept.

The overall approach is quite similar for the rewriting concerning the property hierarchy but no specific context applies, \textit{i}.\textit{e}.,  all triple patterns have to be considered. For each triple pattern, we check whether the property has some sub-properties. It it is the case then the property is replaced by a new variable in the triple pattern and a SPARQL \texttt{FILTER} clause is introduced in the BGP. That filter clause restricts the new variable to be included in the LB and UB of that property.

As a concrete example of this rewriting, we are using query Q4 of our benchmark since it requires inferences over both the concept and property hierarchies.

\begin{center}
\begin{verbatim}
SELECT ?o ?n
WHERE { ?x rdf:type lubm:Professor;
        memberOf ?o; lubm:name ?n.}
\end{verbatim}
\end{center}

The rewriting Q4' of Q4 contains two \texttt{FILTER} clauses, one for the Professor concept and one for the memberOf property (LB() and UB() functions respectively return the LB and UB of their parameter):

\begin{center}
\begin{verbatim}
 SELECT ?o ?n
 WHERE { ?x rdf:type ?p;
         ?m ?o; lubm:name ?n.
         FILTER (?p>=LB(Professor)
             && ?p<UB(Professor)).
         FILTER (?m>=LB(memberOf)
             && ?m<UB(memberOf)).}
\end{verbatim}
\end{center}

\begin{comment}
\begin{center}
\small
\begin{verbatim}
SELECT ?o ?n
WHERE {
    FILTER (object >= LB(Professor) &&
            object < UB(Professor)).
            SelectAs(?x)
    FILTER (predicate >= LB(memberOf) &&
            predicate < UB(memberOf)).
            SelectAs(?x, ?o)
    FILTER (predicate >= LB(name) &&
            predicate < UB(name)).
            SelectAs(?x, ?n)
}
\end{verbatim}
\end{center}
\end{comment}

Note that this rewriting is much more compact and efficient than the classical reformulation which would require 8 \texttt{UNION} clauses and 18 joins\footnote{Rewriting available on our github page}

\subsubsection{Processing pertaining to sameAs cliques}
Recall that sameAs individuals are identified by a tuple of the form (cliqueId, localId) where cliqueId is unique for each sameAs clique and localId corresponds  to the local identifier of an individual in that clique. Moreover, data streams containing sameAs individuals are encoded with the cliqueId only, thus permitting to consider the whole clique.

Given this encoding, a standard query processing is performed where variable bindings concern both standard and sameAs individuals.

\begin{comment}

\begin{algorithm}[ht!]
\caption{LiteMat Query Rewriting}
\label{algo:litemat-rewriting}
\KwIn{dictionary of concepts $D_{cpt}$, dictionary of properties $D_{prop}$, dictionary of individuals $D_{idv}$, original $BGP = \{tp_{i}\}_{i = {1,...,n}}$, empty filter list buffer $L$}

\newcommand\BlockIf[1]{\KwSty{Start If} \\ #1 \\ \KwSty{End If}}
\newcommand\BlockElseIf[1]{\KwSty{Start Else If} \\ #1 \\ \KwSty{End Else If}}
\newcommand\BlockElse[1]{\KwSty{Start Else} \\ #1 \\ \KwSty{End Else}}

\SetKwFunction{get}{get}
\SetKwFunction{getAdaptiveStrategy}{getAdaptiveStrategy}
\SetKwFunction{getUB}{getUB}
\SetKwFunction{getLB}{getLB}
\SetKwFunction{getBound}{getBound}
\SetKwFunction{getNumClique}{getNumClique}
\SetKwFunction{append}{append}

\ForEach{$tp_i \in BGP$ }{
\If{$tp_i$ is rewritable}{
\If{$D_{idv} \cap (tp^{subject}_i) \neq \emptyset$ } {
   $b \gets$ $D_{idv}$ \getBound ($tp^{subject}_i$) \;
   $L$ $\gets L$ \append ($\sigma_{b}(tp^{subject}_i)$)
}

\If{$tp^{predicate}_i \cap D_{prop} \neq \emptyset$ }{
	 $b \gets$ $D_{prop}$ \getBound ($tp^{predicate}_i$) \;
     $L$ \append ($\sigma_{b}(tp^{predicate}_i)$
}

\If{$D_{cpt} \cap tp^{object}_i \neq \emptyset$ }{
   $b \gets$ $D_{cpt}$ \getBound ($tp^{object}_i$) \;}
 \uElseIf{$D_{idv}$ \get($tp^{subject}_i$) != null }{
   $b \gets$ $D_{idv}$ \getNumClique ($tp^{object}_i$) \;}
   $L$ \append ($\sigma_{b}(tp^{object}_i)$)
}

\Else {Keep original filter condition $cnd$ on subject, predicate or object, $L$ \append ($\sigma_{cnd}(tp^{subject}_i \oplus tp^{predicate}_i \oplus tp^{object}_i)$}

}
\end{algorithm}
\end{comment}

\subsubsection{Configuring the inference methodology}
\label{config}
We found out that in a streaming context, the ability to select which inference approach to execute may be quite useful. So far, we have presented our LiteMat-based approach but we have also introduced a full materialization approach. In Section \ref{eval}, we compare our liteMat approach to a SameAs Materialization (SAM) approach.  The configuration enables to select one method that the end-user may consider to be more efficient. Hence it is possible to configure a registered query to use a precise reasoning method. We have achieved this by extending our previous work \cite{DBLP:journals/corr/RenC17} on SPARQL-based query language with keywords configuring this feature.

\section{Evaluation}
\label{eval}
\subsection{Computing Setup}

We evaluate Strider\textsuperscript{lsa}  on an Amazon EC2/EMR cluster of 11 machines (type m3.xlarge) and manage resources with Yarn. Each machine has 4 CPU virtual cores of 2.6 GHz Intel Xeon E5-2670, 15 GB RAM, 80 GB SSD, and 500 MB/s bandwidth. The cluster consists of 2 nodes for data flow management via the Kafka broker (version 0.8.x) and Zookeeper (version 3.5.x)\cite{Zookeeper}, 9 nodes for Spark cluster (1 master, 8 workers, 16 executors). We use Apache Spark 2.0.2, Scala 2.11.7 and Java 8 in our experiment. The number of partitions for message topic is 16, generated stream rate is around 200000 triples/second.

\subsection{Datasets, Queries and Performance metrics}
Two characteristics prevent us from using well-established RSP benchmarks\cite{SRBench, LSBench, city}: their lack of support for the considered reasoning tasks and their inability to cope with massive RDF streams. We thus created a stream generator based on the Lehigh University Benchmark (henceforth LUBM)\cite{lubm} with 10 universities, \textit{i}.\textit{e}.,  containing 1.4 million triples. For the purpose of our experimentation, we extended LUBM with triples containing the \texttt{owl:sameAs}  property. This extension requires to set two parameters: the number of cliques in a dataset and the number of distinct individuals per clique. To define these parameters realistically, we ran an evaluation over different LOD datasets. The results are presented in Table \ref{table:sameAs}. It highlights that although the number of cliques can be very large (over a million in DBpedia), the number of individuals per clique is rather low, \textit{i}.\textit{e}.,  a couple of individuals. Given the size of our dataset, we will run most of our experimentations with 1.000 cliques and an average of 10 individuals per clique, denoted 1K-10. Nevertheless, on queries requiring this form of reasoning, we will stress Strider\textsuperscript{lsa}  with up to 5.000 cliques and an average of 100 individuals per clique (see Fig.\ref{fig:q7-q8} for more details). Figure \ref{table:matTrip} presents the impact on the size of materialized triples for different clique configurations.

We have defined a set of 8 queries\footnote{https://github.com/renxiangnan/strider/wiki} to run our evaluation (see Appendix for details). Queries Q1 to Q5 are limited to concept or/and property subsumption reasoning tasks. Query Q6 implies sameAs only inferences while Q7 and Q8 mix subsumptions and sameAs inferences.

Finally, we need to define which dimensions we want to evaluate. According to \emph{Benchmarking Streaming Computation Engines at Yahoo!}\footnote{https://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at}, a recent benchmark for modern distributed stream processing framework,  we take system throughput and query latency as two performance metrics. In this paper, throughput refers to how many triples can be processed in a unit of time (\textit{e}.\textit{g}.,  triples per second). Latency indicates the time consumed by an RSP engine between the arrival of the input and the generation of its output.

\subsection{SameAs Materialization - SAM}
Unfortunately, we can not compare Strider\textsuperscript{lsa}  to other available RSP systems. Since the stream rate generated in the experiment is extremely high, the state-of-the-art reasoning-enabled RSPs like C-SPARQL and SparqlStream behave abnormally. This is probably caused by the scalability of data flow management in such RSPs. Overall, the design of these RSP engines was not intended for large-scale streaming data processing.
For these reasons, we have designed a Spark streaming engine that integrates a  materialization approach. A full materialization approach, \textit{i}.\textit{e}.,  generating Fig.\ref{fig:sameAsRep}(b) from Fig.\ref{fig:sameAsRep}(a), is too costly and naïve in a streaming context. Hence we did not considered it. In fact, we defined a lighter system that is denoted SameAs Materialization (henceforth SAM) where only bi-directional \texttt{owl:sameAs}  triples between sameAs individuals in the data streams are generated. In Fig.\ref{fig:sameAsRep}(b), it would correspond to create the bi-directional red dotted edges and to make all sameAs triples bi-directional, \textit{i}.\textit{e}.,  a triple $a~\texttt{owl:sameAs} ~b$ implies $b~\texttt{owl:sameAs} ~a$. SAM also comes with a peculiar query rewriting\footnote{Available on our github page} corresponding to the renaming of BGP  variables with unique variable names, \textit{i}.\textit{e}.,  $?x~type~C. ?x~hasName~A$. becomes $?x~type~C. ?y~hasName~A$. Then these new variable names are joined to a common new variable via sameAs triple patterns, \textit{i}.\textit{e}.,  the previous BGP is extended with $?x~\texttt{owl:sameAs} ~t. ?y~\texttt{owl:sameAs} ~t.$ In terms of concept and property subumption inferences, SAM adopts the standard query rewriting that introduces UNION clauses between combinations of BGP reformulation. Such a rewriting comes at the cost of increasing the number of joins.
 Table \ref{table:joins} sums up the join and union operations involved in the 8 queries of our experimentation.
 In particular, queries Q5, Q7 and Q8 present an important number of joins (resp. 90, 45 and 180) due to a large number of union clauses (resp. 17, 14, 29). Appendix F provides some implementation details on the materialization of sameAs individuals in SAM.

\begin{table}[h]
  \captionsetup{justification=centering}
\advance\leftskip-0.15cm
\begin{tabular}{|l|c|c|c|c|c|c|c|c|}
\hline
Cliques  & 1k-10 & 1k-25 & 1k-50 & 1k-100 & 2k-10 & 5k-10  \\
\hline
SAM & $10^5$ & $6,25 . 10^5$ & $2,5 . 10^6$ & $10^7$ & $2.10^5$ & $5.10^5$ \\
\hline
\end{tabular}
\caption{Number of Materialized triples in SAM; \\
1K-10 signifies 1000 cliques, 10 individuals per clique }
\label{table:matTrip}
\vspace{-3mm}
\end{table}

\begin{table}[h]
\begin{tabular}{|l|c|c|c|c|}
\hline
datasets &
\#triples & $\vert$sameAs cliques$\vert$ & max(ipc) & avg(ipc) \\
\hline
\hline
DBPedia* & 1032723 & 1032723 & 2 & 2 \\
\hline
Yago* &3696623 & 3696622 & 2 & 2 \\
\hline
Drugbank & 4215954 & 7678 & 2 & 2 \\
\hline
Biomodels & 2650964 & 187764 & 2 & 1.95 \\
\hline
SGD & 14617696 & 15235 & 8 & 3 \\
\hline
OMIM & 9496062 & 22392 & 2 & 2 \\
\hline
\end{tabular}
\caption{SamesAs statistics on LOD datasets (ipc = number of distinct individuals per sameAs clique, *: subsets containing only \texttt{owl:sameAs}  triples, Biomodels contains triples of the form $a~\texttt{owl:sameAs} ~a$}
\label{table:sameAs}
\vspace{-3mm}
\end{table}

The window size for involved continuous SPARQL queries with LiteMat reasoning support is set to 10 seconds, which is large enough to hold all the data generated from the dataset. However, since the impacts of extra data volume and more complex overheads are introduced in SAM query processing, we have to increase the window size (up to 60 seconds) to ensure that both LiteMat and SAM approaches return the same result. In a nutshell, we approximately adjust the window size and the incoming stream rate by checking the materialized data volume.

\begin{table}[h]
\begin{tabular}{|l|c|c|c|c|c|c|c|c|}
\hline
\hline
Queries & Q1 & Q2 & Q3 & Q4 & Q5 & Q6 & Q7 & Q8 \\
\hline
\#joins LM& 1 & 4 & 0 & 2 & 5 & 2 & 3 & 5 \\
\hline
\#joins SAM & 3 & 24 & 0 & 18 & 90 & 5 & 45 & 180 \\
\hline
\#union LM & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
\hline
\#union SAM & 2 & 5 & 2 & 8 & 17 & 1 & 14 & 29 \\
\hline
\end{tabular}
\caption{Number of joins per query for LiteMat and SAM (query rewriting) approaches}
\label{table:joins}
\vspace{-3mm}
\end{table}

\subsection{Results evaluation \& Discussion}

\begin{figure*}[h]
  \centering
  \subfigure[Throughput Comparison for \textbf{Q1} to \textbf{Q5}]{\includegraphics[keepaspectratio=true,scale=0.45]{Q1-Q5-THP.png}}
  \subfigure[Latency Comparison for \textbf{Q1} to \textbf{Q5}]{\includegraphics[keepaspectratio=true,scale=0.45]{Q1-Q5-LAT.png}}
\caption{Throughput, Latency Comparison between LiteMat and SAM for \textbf{Q1} to \textbf{Q5}}
\label{fig:q1-q5}
\vspace*{-3mm}
\end{figure*}

\begin{figure*}[h]
  \centering
  \captionsetup{justification=centering}
  \subfigure[Throughput Comparison for \textbf{Q6}]{\includegraphics[scale=0.52]{Q6-THP-1.png}}\hfil
  \subfigure[Latency Comparison for \textbf{Q6}]{\includegraphics[scale=0.52]{Q6-LAT-1.png}}
\caption{Throughput, Latency Comparison between LiteMat (LM) and SAM for \textbf{Q6} by varying the size of clique.}
\label{fig:q6}
\vspace*{-3mm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \captionsetup{justification=centering}
  \subfigure[Throughput Comparison for \textbf{Q7} and \textbf{Q8}]{\includegraphics[scale=0.52]{Q7-Q8-THP-1.png}}\hfil
  \subfigure[Latency Comparison for \textbf{Q7} and \textbf{Q8}]{\includegraphics[scale=0.52]{Q7-Q8-LAT-1.png}}
 \caption{Throughput, Latency Comparison of LiteMat (LM) for \textbf{Q7} and \textbf{Q8} by varying the size of clique.}
\label{fig:q7-q8}
\end{figure*}

All the evaluation results include the cost of LM encoding and SAM stream materialization. Figure \ref{fig:q1-q5} reports the throughput and query latency of Q1 to Q5. When reasoning with LM is triggered, Strider\textsuperscript{lsa}  achieves million-level throughput (up to 2 millions triples/seconds), and the query latency remains at a second-level. To the best of our knowledge, such performances have not been achieved by any existing RSP engines. When both original and rewritten query shapes are relatively simple, \textit{e}.\textit{g}.,  Q1 and Q2, LM has thirty percent gain over SAM on throughput and latency. The improvement gain of LM over SAM is increasing for queries involving  multiple inferences: Q4 is 75\% faster than SAM. For Q5, SAM does not even terminate. This is mainly due to the insufficient computing resources (\textit{e}.\textit{g}.,  number of CPU cores, memories) on Spark driver nodes, which is not capable of handling such intensive overheads for jobs/tasks scheduling and memory management. Nevertheless, SAM is more efficient than LM on Q3 which contains a single triple pattern. This is due to the automatic parallelism provided by Spark on the execution of the \texttt{UNION} queries, thus benefiting from a good usage of cluster resources. With its \texttt{FILTER} clause, LM on Q5 does not benefit from such a parallel execution. Under this circumstance, a filter operator with numeric range determination seems to be more costly than the union of three selections. Such a use case motivates our configuration (Section \ref{config}) where the end-user can force a reasoning approach.

Figure \ref{fig:q6} and Figure \ref{fig:q7-q8} illustrate the impact on engine throughput and latency of Q6 to Q8 with varying sameAs clique sizes. As noted previously, ``1K-10" means 1000 cliques, and 10 individuals per clique.
Table \ref{table:matTrip} summarizes the number of materialized triples for sameAs reasoning support. The number of materialized triples obviously increases with greater number of cliques and/or number of individuals per clique.
The data throughput and latency can only be compared on Q6 since on Q7 and Q8, SAM does not terminate. The same non termination issue than on Q5 is observed (Q7 and Q8 respectively have 45 joins and 180 joins). Although stream rate is controlled at a low level, the system quickly fails after the query execution is triggered.
Data throughput and latency is always better for LM than SAM by up to respectively an order of magnitude of 2.5 to 3. For the same computing setting, when the number of individuals per clique increases for a given number of cliques or when the number of cliques increases for the  same number of individuals per clique, the performances of the LM approach decreases.

If LM approach of Strider\textsuperscript{lsa}  is always better than the SAM approach
On Q6, the latency of SAM substantially increases when the size of the clique is raised to 1K-100, as SAM requires $10^7$ extra triples to be materialized.
The same evolution is witnessed for the more complex Q7 and Q8 queries. Nevertheless, for these queries, throughput of over 800.000 triples per second can be achieved. Given our computing setting of 11 machines, this is still a major breakthrough compared to existing RSP engines which are not able to support important constructors such as \texttt{owl:sameAs} .

\section{Related work}
This section considers two related work fields: reasoning over ontologies supporting \texttt{owl:sameAs}  and RSP.
Most RDF stores are using a more or less advanced form of encoding and do not adopt a full materialization approach due to its inefficiency. Concerning the support of \texttt{owl:sameAs}  inferences, GraphDB Enterprise Edition \footnote{http://graphdb.ontotext.com/} and RDFox \cite{DBLP:conf/semweb/NenovPMHWB15} are using a representative-based approach but do not handle stream processing. For instance, RDFox elects a representative among elements of a \texttt{owl:sameAs}  clique using a naive lexicographic order. Then all occurrences of individuals of a clique are replaced by the representative. This presents drawbacks when updates are performed on the clique, \textit{e}.\textit{g}.,  removing the representative from the clique. In such situations, the original dataset has to be processed again. In the context of a streaming, where data streams are ephemData throughput and latency is always better all, and thus not persisted, update operations on the clique are easily handled.
\cite{DBLP:conf/semweb/PotterMNH16} follows on the work of RDFox but considers a distributed approach. Nevertheless, the system is not fault tolerant and does not addresses stream processing.
The Kognac system\cite{kognac} proposes an intelligent encoding of RDF terms for large KBs. It is designed on a combination of estimated frequency-based encoding and semantic clustering. Nevertheless, Kognac is not designed with inferences in mind and its implementation is not distributed and thus can not scale to very large KBs.
Laser\cite{laser} is a stream reasoning system based on a tractable fragment of  LARS\cite{lars}, \textit{i}.\textit{e}.,  an extension of Answer Set Programming for stream processing). The reasoning services of Laser are supported by a set of rules which are expressed in a specific syntax. We consider that this may prevent Laser's adoption by end-users. Moreover, Laser is not distributed and is thus not able to process very large data streams.

The first RSP engines \cite{CSPARQL,CQELS,SPARQLStream,ETALIS} have emerged around 2009. Their original focus was on the design of continuous query languages based on SPARQL. Scalability and reasoning are now considered as primordial features.  \cite{S4SR} was among the first systems to concentrate on the scalability of RDFS stream reasoning. The engine is able to reach throughputs around hundreds of thousand triples/seconds within 32 computing nodes. However, \cite{S4SR} does not include \texttt{owl:sameAs}  in the scope of consideration. Using standards such Apache Kafka and Spark streaming enabled Strider\textsuperscript{lsa}  to increase throughput with less computation power by an order of magnitude while being able to reason over RDFS + \texttt{owl:sameAs} .

\section{Conclusion}
In this paper, we have presented the integration of our LiteMat reasoner for RDFS plus \texttt{owl:sameAs}  within our Strider RSP engine. To the best of our knowledge, this is the first scalable, production-ready RSP system to support such an ontology expressiveness. Via a thorough evaluation, we have demonstrated the pertinence of our system to reason with low latency over high throughput data streams. One of the limitations of our system corresponds to the potential large memory footprint of the generated dictionaries.
Therefore, as future work, we will introduce an efficient partitioning solution of these dictionaries across a cluster of machines. An improvement of the FILTER  operator in Strider\textsuperscript{lsa}  is also in the scope of consideration.
To reach its full potential, this approach will be combined with a partitioning of the streaming data. Finally, we are also working on increasing the expressiveness of supported ontologies, \textit{e}.\textit{g}.,  including transitive properties.

\bibliographystyle{abbrv}

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text:
Deed of Absolute Sale With Assumption of Mortgage
Uploaded by CarolinaDonsol
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Santos vs. Santos Digeest
San Lorenzo Development Corporation vs. CA g.r. No. 124242, January 21, 2005
EXTRA JUDICIAL FORECLOSURE.docx
Site No.17 Agreement
Acknowledgment Receipt of Partial Payment
DEED OF ABSOLUTE SALE
WITH ASSUMPTION OF MORTGAGE
KNOW ALL MEN BY THESE PRESENTS:
This Deed of Absolute Sale (the Deed) is made and entered
into this ___ day of ________________ at ____________ City, by and
________________________________, a corporation
duly organized and existing under Philippine
laws,
___________________________, represented in this
________________,
(SELLER);
- and ________________, Filipino, of legal, married, with
address at ________________________________,
(BUYER).
WITNESSETH: ThatWHEREAS, the SELLER is the true and lawful registered owner of
______________________________________________ (________ s.q.m), more or
less, including all improvements found thereon, located along
________________________________ and covered by Transfer Certificate of
Title No. ________________ issued by the Register of Deeds of
________________ and more particularly described in the copy of the TCT
attached as Annex A (the Property);
WHEREAS, the Property is currently mortgaged by the SELLER
to the ________________________________ (Bank) in the amount of
(insert amount) (P ________________).
WHEREAS, ________________ is authorized by the Board of
Directors of the SELLER to enter into this act as evidenced by the
attached Corporate Secretary Certificate (Annex B).
WHEREAS, the SELLER has offered to sell the Property to the
BUYER and the BUYER is willing to buy the same, subject to the
conditions herein contained;
NOW THEREFORE, for and in consideration of the premises and
the covenants herein contained, the SELLER does hereby sell, cede,
assign, transfer, convey and deliver, unto the BUYER, its nominee,
successors and assigns, in a manner absolute and forever, free and
clear of and from any and all liens and encumbrances of whatever kind
or nature, except those annotated thereon, if any, by operation of law,
The Property is sold for and in consideration of the sum of
________________ (P ________________) payable to the SELLER as follows:
1.1. The BUYER shall deliver to the SELLER the amount of
________________ (________________) representing an advance
of the purchase price of the Property on ________________.
1.2. The BUYER agrees to pay and deliver to the SELLER
the partial payment of ____________________, upon signing
and execution of this Contract.
1.3. The BUYER will retain ________________________________
(________________) from the total purchase price of
________________, to be paid to SELLER only after it vacates
the property and turns over possession thereof to BUYER.
SELLER acknowledges that this instrument serves as the
authority for the Register of Deeds of ________________to register and
transfer the title of the Property in the BUYERs name.
BUYER that:
hereby represents and warrants to the
The SELLER is the lawful and registered owner of the
The SELLER has the full power, capacity
authority to enter into and perform this Deed,
has taken all necessary action to authorize
execution and delivery of this Deed and
performance of the terms and conditions hereof;
This Deed constitutes the legal, valid and binding
obligations of the SELLER, enforceable in accordance
with its terms; and
The SELLER shall execute and deliver to the BUYER
all such documents and perform all such acts and
deeds as may be necessary or desirable for the
purpose of carrying out the objectives of this Deed.
The SELLER shall undertake to vacate the subject
property and deliver possession thereof to the BUYER
within ________________ from receipt of the partial
payment stated under paragraph 1.2. Should the
SELLER fail to comply with the aforementioned
undertaking, it shall pay the BUYER penalty in the
amount of ________________ (________________) for each
month of delay. Further, the said accumulated
penalty shall be deducted from the balance of the
purchase price stated under paragraph 1.3.
The SELLER shall undertake to execute and sign
whatever instrument may be required by the Bank
and other relevant Government Agencies as regards
the release and of the mortgage and the transfer of
tile to the BUYER.
Should the SELLER withdraw from this agreement
after the receipt of partial payment stated in
paragraph 1.2, the SELLER shall reimburse to the
BUYER all payments made, plus ________________
(_____%) interest per month from the date of receipt
under the said amount has been fully reimbursed by
the SELLER.
BUYER shall have the right to take possession of the
Property upon execution of this Deed.
4.1. The BUYER agrees and bind itself to assume the
payment of the SELLERs outstanding mortgage balance directly
_____________________________________________ and perform such
other acts as the Bank may require in order to cause the
cancellation of the Real Estate Mortgage over the Property.
4.2. The amount to be paid by the BUYER to the Bank
shall be credited as part of the purchase price of the Property.
The SELLER shall pay the capital gains tax as regards to
the sale of the Property.
The Buyer shall pay for the transfer tax, registration fees,
documentary stamp tax, and the brokers commission as regards to the
sale of the Property.
Any failure or delay of either party to insist, once or in
several instances, on the strict performance by the other party of any
stipulation or condition of this Deed or to exercise any right or option
herein shall not be construed as abandonment, withdrawal, waiver or
cancellation of such stipulation, condition, right or option.
The written consent of the parties hereto shall be required
for any amendment or waiver of the terms and conditions of this Deed.
This Deed shall be governed by and construed in
accordance with the laws of the Republic of the Philippines.
10. Should one or more of the provisions of this Deed be
declared null and void, inefficacious or ineffectual for any reason
whatsoever, the other provisions shall remain in full force and effect
notwithstanding such defect or flaw, provided the rights of the parties
are not adversely affected thereby.
11. Each of the parties irrevocably consents to the exclusive
jurisdiction of the competent courts of ________________with respect to
any action or proceeding relating to this Deed.
12. This Deed constitutes the entire agreement between the
parties in respect of the matters contained herein.
All prior
agreements, understandings and commitments, formal or informal,
oral or written, shall be superseded and cancelled by the execution of
this Deed.
13. All the terms, covenants, conditions and provisions of this
Deed shall be binding and enforceable upon the parties, their
successors-in-interest and assigns.
IN WITNESS WHEREOF, the parties have hereunto signed this
Deed on the date specified above.
SIGNED IN THE PRESENCE OF:
REPUBLIC OF THE PHILIPPINES)
) S.S.
BEFORE ME, a Notary Public for and in the City of ____________,
this ______ day of _________________, personally appeared the following
persons exhibiting to me their respective Community Tax Certificate
Nos., to wit:
CTC No.
Place/Date Issued
all known to me and to me known to be the same persons who
executed the foregoing instrument and acknowledged to me that the
same is their free act and deed as well as of the corporations herein
represented.
This instrument refers to a Deed of Absolute
No. ________________ of the Register of Deeds
consisting of ___ (___) pages, including this
acknowledgment is written, signed by the
instrumental witnesses.
Sale covered by TCT
of ________________,
page wherein this
parties and their
WITNESS MY HAND AND NOTARIAL SEAL.
Doc. No.
Book No.
Series of ___________.
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