Supporting mobile decision making with association rules and multi-layered caching

Kumar, Navin; Gangopadhyay, Aryya; Karabatis, George

doi:10.1016/j.dss.2005.05.004

Cited by 26 publications

(18 citation statements)

References 16 publications

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“…Its authors have proposed a proactive approach to personalisation, where mining techniques (association rules) are applied to annotate queries with preferences. Furthermore, a set of works (Han, 1998;Chen, 1999;Han et al, 1998) has proposed OLAM approaches for desktop clients using refinement (Kumar et al, 2007), and discovery driven exploration of OLAP data cube (Sarawagi et al, 1998). To the best of our knowledge, in OLAM, only one work exists on mobile OLAP (Kumar et al, 2007).…”

Section: Data Mining and Olap Databases (Olam)mentioning

confidence: 99%

“…Furthermore, a set of works (Han, 1998;Chen, 1999;Han et al, 1998) has proposed OLAM approaches for desktop clients using refinement (Kumar et al, 2007), and discovery driven exploration of OLAP data cube (Sarawagi et al, 1998). To the best of our knowledge, in OLAM, only one work exists on mobile OLAP (Kumar et al, 2007). Kumar et al (2007) proposed a system which combines OLAP technology with associations' rules and mobile cache for mobile users.…”

Section: Data Mining and Olap Databases (Olam)mentioning

confidence: 99%

“…Consequently, we use online analytical mining (OLAM) which allows multi-dimensional data mining in large databases. Its goal is to guide the user navigation in complex datasets and therefore increases the research effectiveness by choosing the pertinent datasets (Kumar et al, 2007). This problem becomes more important if the decision-maker is a mobile user due to limits of resources and connectivity (Kumar et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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A knowledge-based framework for a mobile OLAP system

Nachida

Boussaïd²,

Nader

2015

IJDSS

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Abstract:We propose in this paper a knowledge-based framework for a mobile OLAP. Its main goal is to allow decision-makers to, efficiently, access datasets in OLAP system anywhere and anytime. The challenge of this work is to be able to improve decisional performances while overcoming capability context. To achieve this goal, the framework integrates in a systematic, generic and extensible way, knowledge in the context-aware recommender process, on one hand. On the other hand, it proposes contextual recommendations. For that purpose, the context-aware recommender system exploits the knowledge extracted (profile in a particular situation 'contextual profile') automatically and the current user's contextual profile to compute a list of contextual recommendations (analysis) adapted to the capability context. We conducted a set of experiments to evaluate the performance of our knowledge-based framework. The results are encouraging and show that our framework contributes significantly to improve mobile OLAP navigation.Keywords: OLAP; contextual recommender system; data-mining; K2; OLAM; CBR; knowledge-based system; decision support systems.Reference to this paper should be made as follows: Rezoug, N., Boussaid, O. and Nader, F. (2015) This paper is a revised and expanded version of a paper entitled 'A contextual personalized recommender system for mobile OLAP' presented at the Information Technology and E-service (ICITes), Sousse, Tunis, 24-26 March 2012.

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Section: Data Mining and Olap Databases (Olam)mentioning

confidence: 99%

Section: Data Mining and Olap Databases (Olam)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A knowledge-based framework for a mobile OLAP system

Nachida

Boussaïd²,

Nader

2015

IJDSS

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“…The Sk-navigation rules are stored in an Oracle database using a relational schema as suggested in Kumar, Gangopadhyay, and Karabatis (2005). Each rule contains the details on its antecedent, consequent, measure of skewness, α, the parent rule, and the interestingness.…”

Section: Space Overheadmentioning

confidence: 99%

Navigation Rules for Exploring Large Multidimensional Data Cubes

Kumar

Gangopadhyay

Karabatis

et al. 2008

Data Warehousing and Mining

Self Cite

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“…Association Rule (AR) Mining (Agrawal et al, 1993) is recognized as one of the fundamental data mining tasks and has a wide range of applications (Geurts et al, 2003;Yang et al, 2004;Fonseca et al, 2005;Thabtah and Cowling, 2007;Kumar et al, 2007). Over the last decade, many efficient algorithms have been proposed for mining ARs (see (Ceglar and Roddick, 2006) for a comprehensive survey on AR mining).…”

Section: Introductionmentioning

confidence: 99%

Effective elimination of redundant association rules

Cheng

2007

Data Min Knowl Disc

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Abstract. It is well-recognized that the main factor that hinders the applications of Association Rules (ARs) is the huge number of ARs returned by the mining process. In this paper, we propose an effective solution that presents concise mining results by eliminating the redundancy in the set of ARs. We adopt the concept of δ-Tolerance to define the set of δ-Tolerance ARs (δ-TARs), which is a concise representation for the set of ARs. δ-Tolerance is a relaxation on the closure defined on the support of frequent itemsets, thus allowing us to effectively prune the redundant ARs. We then devise a set of inference rules, with which we prove that the set of δ-TARs is a non-redundant representation of ARs. In addition, we prove that the set of ARs that is derived from the δ-TARs by the inference rules is sound and complete. We also develop a compact tree structure called the δ-TAR tree, which facilitates the efficient generation of the δ-TARs and derivation of other ARs. Experimental results verify the efficiency of using the δ-TAR tree to generate the δ-TARs and to query the ARs. The set of δ-TARs is also shown to be drastically smaller than the state-of-the-art concise representations of ARs.

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Supporting mobile decision making with association rules and multi-layered caching

Cited by 26 publications

References 16 publications

A knowledge-based framework for a mobile OLAP system

A knowledge-based framework for a mobile OLAP system

Navigation Rules for Exploring Large Multidimensional Data Cubes

Effective elimination of redundant association rules

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