Range Top/Bottom k Queries in OLAP Sparse Data Cubes

Luo, ZhongWei; Ling, Tok Wang; Ang, C. H.; Lee, Sin Yeung; Cui, Bin

doi:10.1007/3-540-44759-8_66

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…-Statistics returns general statistics on the overall result (e.g., the average value of the Quantity measure and its skewness). -Bottom-k/Top-k [20], applied to a single measure, returns the worst/best performing facts (e.g., sales with lowest/highest Quantity).…”

Section: A Closer Look At the Vool Modulesmentioning

confidence: 99%

Insight-Based Vocalization of OLAP Sessions

Francia

Gallinucci

Golfarelli

et al. 2022

Advances in Databases and Information Systems

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Carrying out OLAP analyses in hands-free scenarios requires lean forms of communication between the users and the system, based for instance on natural language. In this paper we introduce VOOL, a framework specifically devised for vocalizing the insights resulting from OLAP sessions. VOOL is self-configurable, extensible, and is aware of the user's intentions expressed by OLAP operators. To avoid overwhelming the user with very long descriptions, we pursue the vocalization of selected insights automatically extracted from query results. These insights are detected by a set of modules, each returning a set of independent insights that characterize data. After describing and formalizing our approach, we evaluate it in terms of efficiency and effectiveness.

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Section: A Closer Look At the Vool Modulesmentioning

confidence: 99%

Insight-Based Vocalization of OLAP Sessions

Francia

Gallinucci

Golfarelli

et al. 2022

Advances in Databases and Information Systems

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“…In Luo (2001), the authors propose a storage structure that answers both range top-K and bottom k queries in OLAP sparse cubes. The authors propose to pre-compute the top-K aggregate values in each partition, then used these values to compute the top-K results in query regions that cover multiple cells.…”

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%

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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“…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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Range Top/Bottom k Queries in OLAP Sparse Data Cubes

Cited by 11 publications

References 7 publications

Insight-Based Vocalization of OLAP Sessions

Insight-Based Vocalization of OLAP Sessions

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

Web 2.0 OLAP: From Data Cubes to Tag Clouds

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