Preference queries in deductive databases

Govindarajan, K.; Jayaraman, Bharat; Mantha, Surya

doi:10.1007/bf03037534

Cited by 27 publications

(34 citation statements)

References 14 publications

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“…Several logical approaches to preferences are proposed in the context of deductive databases, including (Govindarajan et al, 1995;K. Govindarajan and B. Jayaraman and S. Mantha, 2001;Kostler et al, 1995).…”

Section: Related Workmentioning

confidence: 99%

Preference-Based Frequent Pattern Mining

Cho

Pei

Wang

et al. 2005

International Journal of Data Warehousing and Mining

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Frequent pattern mining is an important data mining problem with broad applications.Although there are many in-depth studies on efficient frequent pattern mining algorithms and constraint pushing techniques, the effectiveness of frequent pattern mining remains a serious concern: it is non-trivial and often tricky to specify appropriate support thresholds and proper constraints.In this paper, we propose a novel theme of preference-based frequent pattern mining. A user can simply specify a preference instead of setting detailed parameters in constraints. We identify the problem of preference-based frequent pattern mining and formulate the preferences for mining. We develop an efficient framework to mine frequent patterns with preferences.Interestingly, many preferences can be pushed deep into the mining by properly employing the existing efficient frequent pattern mining techniques. We conduct an extensive performance study to examine our method. The results indicate that preference-based frequent pattern mining is effective and efficient. Furthermore, we extend our discussion from pattern-based frequent pattern mining to preference-based data mining in principle and draw a general framework.

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Section: Related Workmentioning

confidence: 99%

Preference-Based Frequent Pattern Mining

Cho

Pei

Wang

et al. 2005

International Journal of Data Warehousing and Mining

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“…This semantics requires preferences to be reflexive and transitive. Also, the operational semantics of Preference Datalog uses specialized versions of the standard logic program evaluation methods: bottom-up [11] or top-down [10]. In the context of database queries, the approach proposed in the present paper achieves similar goals to that of [10] and [11], remaining, however, entirely within the relational data model and classical first-order logic.…”

Section: Preference Queriesmentioning

confidence: 99%

“…Also, the operational semantics of Preference Datalog uses specialized versions of the standard logic program evaluation methods: bottom-up [11] or top-down [10]. In the context of database queries, the approach proposed in the present paper achieves similar goals to that of [10] and [11], remaining, however, entirely within the relational data model and classical first-order logic. Finally, [10,11] do not address some of the issues we deal with in the present paper like transitive closure of preferences, prioritized composition or iterated preferences (a similar concept to the last one is presented under the name of "relaxation").…”

Section: Preference Queriesmentioning

confidence: 99%

“…It encompasses preference logics [23,19,13], preference reasoning [24,22,4], prioritized nonmonotonic reasoning and logic programming [5,6,21] and decision theory [7,8] (the list is by no means exhaustive). However, only a few papers [18,3,11,2,14,17] address the issue of user preferences in the context of database queries. Two different approaches are pursued: qualitative and quantitative.…”

Section: Introductionmentioning

confidence: 99%

“…Two different approaches are pursued: qualitative and quantitative. In the qualitative approach [18,3,11,17], the preferences between tuples in the answer to a query are specified directly, typically using binary preference relations. Example 1.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Querying with Intrinsic Preferences

Chomicki

2002

Lecture Notes in Computer Science

113

107

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Abstract. The handling of user preferences is becoming an increasingly important issue in present-day information systems. Among others, preferences are used for information filtering and extraction to reduce the volume of data presented to the user. They are also used to keep track of user profiles and formulate policies to improve and automate decision making. We propose a logical framework for formulating preferences and its embedding into relational query languages. The framework is simple, and entirely neutral with respect to the properties of preferences. It makes it possible to formulate different kinds of preferences and to use preferences in querying databases. We demonstrate the usefulness of the framework through numerous examples.

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Frequent Pattern Mining with Preferences–Utility Functions Approach

Braynova

Pendharkar

2005

Lecture Notes in Computer Science

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Preference queries in deductive databases

Cited by 27 publications

References 14 publications

Preference-Based Frequent Pattern Mining

Preference-Based Frequent Pattern Mining

Querying with Intrinsic Preferences

Frequent Pattern Mining with Preferences–Utility Functions Approach

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