Incremental application of knowledge to continuously arriving time-oriented data
In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.
KeywordsIncremental temporal abstraction Continuous monitoring Complex events monitoring Situation recognition Temporal reasoning
Unable to display preview. Download preview PDF.
- ACT-NET (1996). The active database management system manifesto: A rulebase of ADBMS features. ACM SIGMOD Record, 25(3), 20–49, September.Google Scholar
- Adi, A., & Etzion, O. (2004). Amit—the situation manager. VLDB Journal.Google Scholar
- Boaz, D., Balaban, M., & Shahar, Y. (2003). A temporal-abstraction rule language for medical databases. Proceeding of the workshop on intelligent data analysis in medicine and pharmacology (IDAMAP). Protaras, Cyprus.Google Scholar
- Boaz, D., & Shahar, Y. (2005). A framework for distributed mediation of temporal-abstraction queries to clinical databases. Artificial Intelligence in Medicine 34(1): 3--24Google Scholar
- Chakravarthy, S. (1997). Sentinel: An object-oriented DBMS with event-based rules. ACM SIGMOD Record. Proceedings of the 1997 ACM SIGMOD international conference on management of data, Volume 26 Issue 2.Google Scholar
- Chakravarthy, S., Blaustein, B., Buchmann, A. P., Carey, M., Dayal, U., Goldhirsch, D., et al. (1989). HiPAC: A research project in active, time-constrained database management. Technical Report XAIT-89-02, Xerox Advanced Information Technology, Cambridge, Massachusetts.Google Scholar
- Dayal, U., Blaustein, B., Buchmann, A. P., Chakravarthy, U., Hsu, M., Ledin, R., et al. The HiPAC Project (1988). Combining active databases and timing constraints. ACM Sigmod Record, 17, 1.Google Scholar
- Gatziu, S., & Dittrich, K. R. (1992). SAMOS: An active object-oriented database system. Data Eng. Bull., 15, 1–4, December.Google Scholar
- Gatziu, S., & Dittrich, K. R. (1993). Events in an object-oriented database system. In Proceedings of the 1st international conference on rules in database systems, September 1993.Google Scholar
- Gatziu, S., & Dittrich, K. R. (1994). Detecting composite events in active databases using petri nets. In Proceedings of the 4th international workshop on research issues in data engineering, February 1994.Google Scholar
- Gehani, N. H., & Jagadish, H. V. (1991). Ode as an active database: Constraints and triggers. In Proceedings of VLDB. Barcelona, September.Google Scholar
- Lavrač, N., Kononenko, I., Keravnou, E., Kukar, M., & Zupan, B. (1999). Intelligent data analysis for medical diagnosis: Using machine learning and temporal abstraction. AI Communications, 11, 191–218.Google Scholar
- O’Connor, M. J., Grosso, W. E., Tu, S. W., & Musen, M. A. (2001). RASTA: A distributed temporal abstraction system to facilitate knowledge-driven monitoring of clinical databases. Proceedings of MEDINFO-2001, the tenth world congress on medical informatics (pp. 508–512). London, UK.Google Scholar
- Paton N. (Ed.) (1999). Active rules in database systems. Berlin Heidelberg New York: Springer.Google Scholar
- Rosenthal, A., Chakravarthy, S., & Blautein, B. (1989). Situation monitoring for active databases. VLDB, 455–464.Google Scholar
- Russ, T. A. (1989). Using hindsight in medical decision making. In L. C. Kingsland (Ed.), Proceedings of the thirteenth annual Symposium on Computer Applications in Medical Care (SCAMC-89) (pp. 38–44). IEEE Computer Society Press: Washington, DC.Google Scholar
- Spokoiny, A., & Shahar, Y. (2003). Momentum—an active time-oriented database for intelligent abstraction, exploration and analysis of clinical data. Proceeding of the workshop on intelligent data analysis in medicine and pharmacology-IDAMAP 2003, at 9th Conference on Artificial Intelligence in Medicine Europe-Europe (AIME) ’03, Protaras, Cyprus.Google Scholar
- Spokoiny, A., & Shahar, Y. (2004). A time-oriented active database approach for intelligent abstraction, monitoring and querying of clinical data from multiple sources. Proceedings of the 11th world congress on medical informatics-MEDINFO-04. San Francisco, US.Google Scholar
- Spokoiny, A., & Shahar, Y. (2007). An active database architecture for knowledge-based incremental abstraction of complex concepts from continuously arriving time-oriented raw data. Journal of Intelligent Information Systems, doi:10.1007/s10844-006-0008-x.
- Widom, J., & Ceri, S. (1996). Active database systems: Triggers and rules for advanced database processing. Morgan Kaufmann, San Mateo, CA.Google Scholar