Application Model An application model is an abstract description Traditional waterfall or agile Advanced application design and In addition to of a system element or application, including Big monitoring tuned to Big Data APM, additional artifacts, milestone checks with its Big Data SnP components. Some version of needs (streaming, IoT, workflow implemented as Data support an application model is a requirement for the organization bleed-through) code with explicit model. Full BDSQ safety framework. App models can audit and logging to domain foster transparency and interoperability, model. Model artifacts are essential for long-lived Big Data and produced and consumed inside potentially, Big Data systems. the Big Data system. Selected References M. Borek, K. Stenzel, K. Katkalov, and W. Reif, "Abstracting security-critical applications for model checking in a model-driven approach," in 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), Sep. 2015, pp. 11-14. [Online]. Available: http://dx.doi.org/10.1109/ICSESS.2015.7338996 Authority to collect data Long-lived or PII-intensive Big Data systems XML or equivalent for authority, Use digital cert associated with Same as level 1, but with must capture, maintain and maintain capture terms of service, legal collection. Written policies controls designed for transparency for data collection authority. This authorities, versioning information surrounding enterprise handling transferability to third parties, may be point in time. within overall enterprise for PII, but tend to be limited to especially in supply chain governance. a single enterprise. settings. Authority data is tracked using Big Data technologies, detail, audit, traceability.
Application Model An application model is an abstract description of a system element or application, including its Big Data SnP components. Some version of an application model is a requirement for the BDSQ safety framework. App models can foster transparency and interoperability, essential for long-lived Big Data and potentially, Big Data systems. Traditional waterfall or agile artifacts, milestone checks with Big Data support Advanced application design and monitoring tuned to Big Data needs (streaming, IoT, organization bleed-through) In addition to APM, additional workflow implemented as code with explicit model. Full audit and logging to domain model. Model artifacts are produced and consumed inside the Big Data system. Selected References M. Borek, K. Stenzel, K. Katkalov, and W. Reif, "Abstracting security-critical applications for model checking in a model-driven approach," in 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), Sep. 2015, pp. 11-14. [Online]. Available: http://dx.doi.org/10.1109/ICSESS.2015.7338996 Authority to collect data Long-lived or PII-intensive Big Data systems must capture, maintain and maintain transparency for data collection authority. This may be point in time. XML or equivalent for authority, capture terms of service, legal authorities, versioning information within overall enterprise governance. Use digital cert associated with collection. Written policies surrounding enterprise handling for PII, but tend to be limited to a single enterprise. Same as level 1, but with controls designed for transferability to third parties, especially in supply chain settings. Authority data is tracked using Big Data technologies, detail, audit, traceability.