Analysis of pre-computed partition top method for range top-k queries in OLAP data cubes

Loh, Zheng Xuan; Ling, Tok Wang; Ang, C. H.; Lee, Sin Yeung

doi:10.1145/584792.584806

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…The partition factors and the number of pre-stored values can be estimated through statistical analysis. In Loh et al (2002) propose an efficient approach for range top-K processing, called the adaptive pre-computed partition top (APPT) method. The APPT method pre-computes a set of maximum values for each partitioned sub-block.…”

Section: Related Workmentioning

confidence: 99%

“…Several research works have addressed the problem of top-K queries (Ding et al, 2010;Loh et al, 2002;Xin et al, 2006;Luo, 2001), queries recommendation (Giacometti et al, 2008;Golfarelli et al, 2011;Jerbi et al, 2009b;Khemiri and Bentayeb, 2013;Kozmina, 2013;Kuchmann-beauger and Aufaure, 2011) in DW retrieval information and cubes design (Abelló et al, 2002(Abelló et al, , 2013Bimonte et al, 2010;Boukraâ et al, 2010;Cheung et al, 1999;Parimala and Pahwa, 2006;Sabaini et al, 2015). In all these works, the user's need is accurate, i.e., the decision-maker knows the cubes comporting his needs.…”

Section: Introductionmentioning

confidence: 99%

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Data Cubes Retrieval and Design in OLAP Systems: From Query Analysis to Visualisation Tool

Djiroun

Boukhalfa

Alimazighi

2018

IJBIDM

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Business intelligence systems provide an effective solution from large volumes of data for multidimensional online computing and analysis. Usually, in a decision-making process, organisations and enterprises, require several internal and/or external cubes which are often heterogeneous. Most of the time, the structure of these cubes is unknown to the decision-makers. To analyse a phenomenon, the decision-maker seeks among sets of cubes, in a collection, the cube which responds better to his need. In this context, we propose an approach that enables decision-makers to express their needs via a query expressed in a natural language, returns top-K relevant cubes and designs/constructs new cubes when no, or few deployed cubes are relevant. We propose a tool called RD-cubes-query implementing our approach in a ROLAP architecture. We use this tool in some experiments to validate our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Cubes Retrieval and Design in OLAP Systems: From Query Analysis to Visualisation Tool

Djiroun

Boukhalfa

Alimazighi

2018

IJBIDM

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“…Various efficient techniques have been proposed for the related range MAX problem [28,29], but they do not necessarily generalize. Instead, for the range top-k problem, we can partition sparse data cubes into customized data structures to speed up queries by an order of magnitude [30,31,32]. We can also answer range top-k queries using RD-trees [33] or R-trees [34].…”

Section: Fast Computationmentioning

confidence: 99%

Web 2.0 OLAP: From Data Cubes to Tag Clouds

Aouiche

Lemire

Godin

2009

Lecture Notes in Business Information Processing

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Abstract. Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.

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“…Various efficient techniques have been proposed for the related range MAX problem (Chazelle, 1988;Poon, 2003), but they do not necessarily generalize. Instead, for the range top-k problem, we can partition sparse data cubes into customized data structures to speed up queries by an order of magnitude (Luo et al, 2001;Loh et al, 2002a;Loh et al, 2002b).…”

Section: Fast Computationmentioning

confidence: 99%

COLLABORATIVE OLAP WITH TAG CLOUDS - Web 2.0 OLAP Formalism and Experimental Evaluation

Aouiche¹,

Lemire

Godin

2008

Proceedings of the Fourth International Conference on Web Information Systems and Technologies

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Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.

show abstract

Analysis of pre-computed partition top method for range top-k queries in OLAP data cubes

Cited by 8 publications

References 17 publications

Data Cubes Retrieval and Design in OLAP Systems: From Query Analysis to Visualisation Tool

Data Cubes Retrieval and Design in OLAP Systems: From Query Analysis to Visualisation Tool

Web 2.0 OLAP: From Data Cubes to Tag Clouds

COLLABORATIVE OLAP WITH TAG CLOUDS - Web 2.0 OLAP Formalism and Experimental Evaluation

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