Efficient Processing of Uncertain Events in Rule-Based Systems

Wasserkrug, Segev; Gal, Avigdor; Etzion, Opher; Turchin, Yulia

doi:10.1109/tkde.2010.204

“…Wasserkrug et. al [33] presented a generic and formal framework for handling the event derivation under uncertainty.…”

Section: Related Work 31 Event Recognition and Processingmentioning

confidence: 99%

Event Evolution Modeling for Efficient News Search

Bokhari¹,

Adhami²

2015

IJCA

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With the advancement in internet technology, users can now easily extract large number of news stories of any ongoing incidents from existing newswires. These hundreds and thousands of news stories causes the problem of information overload. Users find difficulty in capturing the blueprint of the incident as the volume of information is too large. So it becomes necessary to organize news stories into events and learn how these events developed or evolved within the topic. This paper discusses some of the efforts made to model and discover relationships between news events, which had been a less focused area as compared to TDT research which solely focused flat hierarchical structure. A real-world example is discussed for event evolution analysis and future extensions have been proposed.

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“…They include probability theory, Bayesian networks, fuzzy logics, and many other formalisms. The recent literature also presents proposals in the area of stream processing [125][126][127][128]123].…”

Section: Modeling Datamentioning

confidence: 99%

Streaming the Web: Reasoning over dynamic data

Margara

¹

,

Urbani

²

,

Harmelen

³

et al. 2014

Journal of Web Semantics

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a b s t r a c tIn the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.

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“…Efficiency in presence of uncertainty has been a main concern in stream processing systems and led to the design of promising techniques [125][126][127][128]123]. Finding a good balance between the advantages of a model in terms of accuracy, and simplicity, and the costs required for managing it constitutes a major area of investigation for future research.…”

Section: Modeling Operationsmentioning

confidence: 99%

Streaming the Web: Reasoning Over Dynamic Data

Margara¹,

Urbani²,

Harmelen³

et al. 2014

View full text Add to dashboard Cite

a b s t r a c tIn the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.

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Efficient Processing of Uncertain Events in Rule-Based Systems

Cited by 54 publications

References 22 publications

Event Evolution Modeling for Efficient News Search

Event Evolution Modeling for Efficient News Search

Streaming the Web: Reasoning over dynamic data

Streaming the Web: Reasoning Over Dynamic Data

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