ABSTRACT
Event driven architecture is a paradigm shift from traditional computing architectures which employ synchronous, request-response interactions. In this paper we introduce a conceptual architecture for what can be considered the next phase of that evolution: proactive event-driven computing. Proactivity refers to the ability to mitigate or eliminate undesired future events, or to identify and take advantage of future opportunities, by applying prediction and automated decision making technologies. We investigate an extension of the event processing conceptual model and architecture to support proactive event-driven applications, and propose the main building blocks of a novel architecture. We first describe several extensions to the existing event processing functionality that is required to support proactivity; next, we extend the event processing agent model to include two more type of agents: predictive agents that may derive future uncertain events based on prediction models, and proactive agents that compute the best proactive action that should be taken. Those building blocks are demonstrated through a comprehensive scenario that deals with proactive decision making, ensuring timely delivery of critical material for a production plant.
- A. Arnold, Y. Liu, and N. Abe. Temporal causal modeling with graphical granger methods. In ACM SIGKDD, 2007. Google ScholarDigital Library
- C. Boutilier, T. Dean, and S. Hanks. Decision theoretic planning: Structural assumptions and computational leverage. Journal of AI Research, 11:1--94, 1999.Google ScholarDigital Library
- Ronen Brafman, Carmel Domshlak, Yagil Engel, and Zohar Feldman. Planning for operational control systems with predictable exogenous events. In AAAI, to appear, 2011.Google Scholar
- D.M. Chickering, D. Heckerman, and C. Meek. A bayesian approach to learning bayesian networks with local structure. In UAI, 1997. Google ScholarDigital Library
- S. Dolev, M. Kopeetsky, and A. Shamir. RFID authentication efficient proactive information security within computational security. Theory of Computing Systems, pages 1--18, 2011. Google ScholarDigital Library
- O. Etzion, Y. Magid, E. Rabinovich, I. Skarbovsky, and N. Zolotorevsky. Context aware computing and its utilization in event-based systems. In DEBS, 2010. Google ScholarDigital Library
- O. Etzion and P. Niblett. Event Processing in Action. Manning Publications, 2010. Google ScholarDigital Library
- S. Fu and C.Z. Xu. Exploring event correlation for failure prediction in coalitions of clusters. In ICS, 2007. Google ScholarDigital Library
- C. Guestrin, D. Koller, R. Parr, and S. Venkataraman. Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research, 19(1):399--468, 2003. Google ScholarCross Ref
- J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1):55--86, 2007. Google ScholarDigital Library
- J.L. Hellerstein, S. Ma, and C.S. Perng. Discovering actionable patterns in event data. IBM Systems Journal, 41(3):475--493, 2010. Google ScholarDigital Library
- Michael J. Kearns, Yishay Mansour, and Andrew Y. Ng. A sparse sampling algorithm for near-optimal planning in large markov decision processes. Machine Learning, 49(2-3):193--208, 2002. Google ScholarDigital Library
- M. Kohler and R. Fies. Proactive caching-a framework for performance optimized access control evaluations. In IEEE POLICY, 2009. Google ScholarDigital Library
- T. Kunz and R. Alhalimi. Energy-efficient proactive routing in MANET: Energy metrics accuracy. Ad Hoc Networks, 8(7):755--766, 2010. Google ScholarDigital Library
- D.C. Luckham. The power of events. Addison-Wesley, 2002.Google Scholar
- S. Mahadevan. Average reward reinforcement learning: Foundations, algorithms, and empirical results. Recent Advances in Reinforcement Learning, pages 159--195, 1996. Google ScholarDigital Library
- K. Mahbub and G. Spanoudakis. Proactive SLA negotiation for service based systems. In 6th World Congress on Services, 2010. Google ScholarDigital Library
- H. Mannila, H. Toivonen, and A. Inkeri Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259--289, 1997. Google ScholarDigital Library
- AE Nicholson and JM Brady. Dynamic belief networks for discrete monitoring. IEEE Transactions on Systems, Man and Cybernetics, 24(11):1593--1610, 2002.Google ScholarCross Ref
- U. Nodelman, C.R. Shelton, and D. Koller. Continuous time bayesian networks. In UAI, 2002. Google ScholarDigital Library
- J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988. Google ScholarDigital Library
- W.B. Powell. Approximate Dynamic Programming: Solving the curses of dimensionality. Wiley-Interscience, 2007. Google ScholarDigital Library
- C. Ré, J. Letchner, M. Balazinksa, and D. Suciu. Event queries on correlated probabilistic streams. In ACM SIGMOD, 2008. Google ScholarDigital Library
- A. Robinson, J. Levis, and G. Bennett. INFORMS news: INFORMS to officially join analytics movement. INFORMS, OR/MS Today, 37(5), 2010.Google Scholar
- S.J. Russell and P. Norvig. Artificial intelligence: a modern approach. Prentice hall, 2009. Google ScholarDigital Library
- D. Sarma, M. Theobald, and J. Widom. Exploiting lineage for confidence computation in uncertain and probabilistic databases. In ICDE, 2008. Google ScholarDigital Library
- J. Vennekens, M. Denecker, and M. Bruynooghe. CP-logic: A language of causal probabilistic events and its relation to logic programming. Theory and Practice of Logic Programming, 9(03):245--308, 2009. Google ScholarDigital Library
- S. Wasserkrug, A. Gal, and O. Etzion. A model for reasoning with uncertain rules in event composition. In UAI, 2005.Google Scholar
- S. Wasserkrug, A. Gal, O. Etzion, and Y. Turchin. Efficient processing of uncertain events in rule-based systems. IEEE Transactions on Knowledge and Data Engineering, 2010. Google ScholarDigital Library
- J. Xu and C.R. Shelton. Intrusion detection using continuous time bayesian networks. Journal of Artificial Intelligence Research, 39:745--774, 2010. Google ScholarCross Ref
- H. Zhang, Y. Diao, and N. Immerman. Recognizing patterns in streams with imprecise timestamps. Proceedings of the VLDB Endowment, 3(1), 2010. Google ScholarDigital Library
Index Terms
- Towards proactive event-driven computing
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