Definitions
Models and algorithms that support a stream of events that may demonstrate uncertainty in event occurrence, as well as in values assigned to events in a stream.
Overview
Many contemporary applications depend on the ability to monitor efficiently streams of events (e.g., application messages or business events) to detect and react in a timely manner to situations. Some events are generated exogenously by devices such as sensors and flow across distributed systems. Other events (and their content) are inferred by complex event processing (CEP) systems. The first generation of CEP systems was built as stand-alone prototypes or as extensions of existing database engines. These systems were diversified into products with various approaches toward event processing, including stream-oriented, rule-oriented, imperative, and publish-subscribe paradigms. Common to all of these approaches is the assumption that received events have occurred and that the CEP system is complete. In...
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References
Arroyo-Figueroa G, Sucar LE (1999) A temporal bayesian network for diagnosis and prediction. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence (UAI’99). Morgan Kaufmann Publishers Inc., pp 13–20
Bacchus F (1990) Representing and reasoning with probabilistic knowledge: a logical approach to probabilities. MIT Press, Cambridge
Cugola G, Margara A, Matteucci M, Tamburrelli G (2015) Introducing uncertainty in complex event processing: model, implementation, and validation. Computing 97(2):103–144
Drakopoulos J (1994) Probabilities, possibilities, and fuzzy sets. Fuzzy Set Syst 75:1–15
Dubois D, Prade H (1988) Possibility theory: an approach to computerized processing of uncertainty. Plenum press, New York
Etzion O, Niblett P (2010) Event processing in action. Manning Publications Co. Shelter Island, New York, United States
Gal A, Anaby-Tavor A, Trombetta A, Montesi D (2005) A framework for modeling and evaluating automatic semantic reconciliation. VLDB J 14(1):50–67
Green TJ, Tannen V (2006) Models for incomplete and probabilistic information. Springer, Berlin/Heidelberg, pp 278–296
Hajek P (1998) Metamathematics of Fuzzy logic. Kluwer Academic Publishers, Dordrecht
Halpern JY (1990) An analysis of first-order logics of probability. Artif Intell 46(3):311–350
Halpern JY (2003) Reasoning about uncertainty. MIT Press, Cambridge
Heinze T, Aniello L, Querzoni L, Jerzak Z (2014) Cloud-based data stream processing. In: Proceedings of the 8th ACM international conference on distributed event-based systems, DEBS
Kanazawa K (1991) A logic and time nets for probabilistic inference. In: Proceedings of the ninth national conference on artificial intelligence (AAAI’91), vol 1. AAAI Press, pp 360–365
Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Upper Saddle River
Liu H, Jacobsen HA (2004) Modeling uncertainties in publish/subscribe systems. In: Proceedings of 20th international conference on data engineering, pp 510–521
Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco
Raiffa H (1997) Decision analysis: introductory lectures on choices under uncertainty. Mcgraw-Hill College, New York
Ré C, Letchner J, Balazinksa M, Suciu D (2008) Event queries on correlated probabilistic streams. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data (SIGMOD’08). ACM, pp 715–728
Turchin Y, Gal A, Wasserkrug S (2009) Tuning complex event processing rules using the prediction-correction paradigm. In: Proceedings of the third ACM international conference on distributed event-based systems (DEBS’09), New York. ACM, pp 10:1–10:12
Wasserkrug S, Gal A, Etzion O, Turchin Y (2008) Complex event processing over uncertain data. In: Proceedings of the second international conference on distributed event-based systems (DEBS’08). ACM, pp 253–264
Wasserkrug S, Gal A, Etzion O (2012a) A model for reasoning with uncertain rules in event composition systems. CoRR, abs/1207.1427
Wasserkrug S, Gal A, Etzion O, Turchin Y (2012b) Efficient processing of uncertain events in rule-based systems. IEEE Trans Knowl Data Eng 24(1): 45–58
Widom J, Ceri S (eds) (1994) Active database systems: triggers and rules for advanced database processing. Morgan Kaufmann Publishers Inc. San Francisco, California
Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhang H, Diao Y, Immerman N (2013) Recognizing patterns in streams with imprecise timestamps. Inf Syst 38(8):1187–1211
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Gal, A., Rivetti, N. (2019). Uncertainty in Streams. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_332
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DOI: https://doi.org/10.1007/978-3-319-77525-8_332
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