Amit - the situation manager

Adi, Asaf; Etzion, Opher

doi:10.1007/s00778-003-0108-y

“…This has been widely investigated in stream processing systems [37,34,41,36], proposing techniques for rewriting the deployed queries to optimize their execution. These solutions focused on re-defining the structure of single queries, but also on sharing operators between multiple queries, as proposed in the field of multiquery optimization [150].…”

Section: Efficient Query Evaluationmentioning

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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“…Several CEP systems have been developed in the last few years [35,36,34,[37][38][39][40][41]. One of the main goals of these systems is to execute the rules efficiently; accordingly, most of them trade expressiveness for response time.…”

Section: Complex Event Processing Systemsmentioning

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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“…AMIT [2] is an event stream engine whose goal is to provide high-performance situation detection mechanisms. AMIT offers a sophisticated user interface for modelling business situations based on the following four types of entities: events, situations, lifespans and keys.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Correlating Business Events for Event-Triggered Rules

Schiefer¹,

Obweger²,

Suntinger³

2009

Lecture Notes in Computer Science

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Abstract. Event processing rules may be prescribed in many different ways, including by finite state machines, graphical methods, ECA (event-conditionaction) rules or reactive rules that are triggered by event patterns. In this paper, we present a model for defining event relationships for event processing rules. We propose a so-called correlation set allowing users to graphically model the event correlation aspects of a rule. We illustrate our approach with the eventbased system SARI (Sense and Respond Infrastructure) which uses correlation sets as part of rule definitions for the discovery of event patterns. We have fully implemented the proposed approach and compare it with alternative correlation approaches.

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Amit - the situation manager

Cited by 203 publications

References 29 publications

Streaming the Web: Reasoning over dynamic data

Streaming the Web: Reasoning over dynamic data

Streaming the Web: Reasoning Over Dynamic Data

Correlating Business Events for Event-Triggered Rules

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