Design and implementation of TEMPO fuzzy triggers

Bouaziz, T.; Karvonen, Janne; Pesonen, Anton; Wolski, Antoni

doi:10.1007/bfb0022021

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…That is, attributes, objects, classes and rules are defined with a high degree of crispness. Third, although Bouaziz et al [6][7][8] introduce fuzziness in their ECA rules via using linguistic terms and provide a fuzzy inference mechanism, the way they associate fuzzy events with fuzzy CA rules in the inference mechanism is different from ours. That is, they use a technique called squeezing to associate fuzzy events with fuzzy CA rules by squeezing the result of the fuzzy CA rule set with the event match factor.…”

Section: Introductionmentioning

confidence: 94%

Incorporating Fuzziness Into Active Rules

Bostan-Korpeoglu

Yazıcı

2008

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a fuzzy active object-oriented database for modeling knowledge intensive applications. In that, we incorporate fuzziness within the event, condition and action parts of an active rule. We consider deductive rules as special cases of active rules so that deductive queries are handled using abstract kind of events. We also introduce a model for fuzzy inferencing of fuzzy active rules where we develop a model for scenario concept. We use a Fuzzy Petri Net model for fuzzy rule-based inference.

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Section: Introductionmentioning

confidence: 94%

Incorporating Fuzziness Into Active Rules

Bostan-Korpeoglu

Yazıcı

2008

Int. J. Unc. Fuzz. Knowl. Based Syst.

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“…To the best of our knowledge, only a research group in VTT (Finland) has worked on fuzzy triggers [27], [15], [28], [14]. In [28], a condition-action (CA) fuzzy trigger was proposed which means that fuzziness was introduced to the CA part of an event-condition-action (ECA) rule. In a later work [15], the concept of CA trigger was extended to a fuzzy ECA rule by introducing the notion of fuzzy events.…”

Section: A Fuzzy Rules In Admssmentioning

confidence: 99%

Automated construction of fuzzy event sets and its application to active databases

Saygın

Ulusoy

2001

IEEE Trans. Fuzzy Syst.

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Abstract-Fuzzy sets and fuzzy logic research aims to bridge the gap between the crisp world of math and the real world. Fuzzy set theory was applied to many different areas, from control to databases. Sometimes the number of events in an event-driven system may become very high and unmanageable. Therefore, it is very useful to organize the events into fuzzy event sets also introducing the benefits of the fuzzy set theory. All the events that have occurred in a system can be stored in event histories which contain precious hidden information. In this paper, we propose a method for automated construction of fuzzy event sets out of event histories via data mining techniques. The useful information hidden in the event history is extracted into a matrix called sequential proximity matrix. This matrix shows the proximities of events and it is used for fuzzy rule execution via similarity based event detection and construction of fuzzy event sets. Our application platform is active databases. We describe how fuzzy event sets can be exploited for similarity based event detection and fuzzy rule execution in active database systems.Index Terms-Active databases, fuzzy event sets, fuzzy rule execution, fuzzy triggers, similarity based event detection.

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“…The same principle applies to fuzzy triggers. The execution timc of a fuzzy triggcr with at most 32 fuzzy rules is within 0.5 ms and scales linearly with the number of rules (for more on fuzzy trigger performance, scc [Bou+97]). The stress on trigger performance reflects the idea that the primary responsibility of RapidBase is intended to be active monitoring of industrial processes.…”

Section: Implementation Performance and Utilizationmentioning

confidence: 99%

“…The power of fuzzy inference is utilized in fuzzy triggers [WB98,Bou+97]. In C-fuzzy (condition-fuzzy) triggers, fuzzy rule sets may he used in the condition evaluation.…”

Section: Fuzzy Triggersmentioning

confidence: 99%

Design of RapidBase-an active measurement database system

Wolski

Kuha²,

Luukkanen³

et al.

Proceedings 2000 International Database Engineering and Applications Symposium (Cat. No.PR00789)

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In datu-intensive industrial on-line applications utilizing live process data, one faces an unusual set of dutubase requirements. The process measurement &a need to be acquired at great speed, organized in time series and made available for time-based retrieval. Active capabilities old functional extensibility are needed to implement a jlexible data-driven processing pardigm. An efficient transaction logging and recoverj mechanism is needed in order not to impede the dccta acquisition flow. RupidBuse is a system that meets these requirements. It utilizes a main-menior)i database, a unique temporal-relational dufa model for hundling time series, and an elaborate trigger subsystem. It is implemented as a server program equipped with interfaces of high power of expression.

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Design and implementation of TEMPO fuzzy triggers

Cited by 12 publications

References 5 publications

Incorporating Fuzziness Into Active Rules

Incorporating Fuzziness Into Active Rules

Automated construction of fuzzy event sets and its application to active databases

Design of RapidBase-an active measurement database system

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