Describing Analytical Sessions Using a Multidimensional Algebra

Romero, Óscar; Marcel, Patrick; Abelló, Alberto; Peralta, Verónika; Bellatreche, Ladjel

doi:10.1007/978-3-642-23544-3_17

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…In our more recent work, we used supervised learning to identify a set of query features allowing to characterize focus zones in OLAP explorations [5], or to identify queries that better contribute to an exploration [4]. The present work can be seen as a continuation of those previous works, since we have the same objective as [18] and use the same technique as [4]. The main differences with these previous works is that we make no assumption about the type of queries in the workload (particularly, they may not be multidimensional queries), and we have no ground truth (i.e., no human manual inspection of each query) on the workload.…”

Section: Workload Analysismentioning

confidence: 97%

Detecting coherent explorations in SQL workloads

Peralta

Marcel

Verdeaux

et al. 2020

Information Systems

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This paper presents a proposal aiming at better understanding a workload of SQL queries and detecting coherent explorations hidden within the workload. In particular, our work investigates SQLShare [11], a database-as-a-service platform targeting scientists and data scientists with minimal database experience, whose workload was made available to the research community. According to the authors of [11], this workload is the only one containing primarily ad-hoc hand-written queries over user-uploaded datasets. We analyzed this workload by extracting features that characterize SQL queries and we show how to use these features to separate sequences of SQL queries into meaningful explorations. We ran several tests over various query workloads to validate empirically our approach.

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Section: Workload Analysismentioning

confidence: 97%

Detecting coherent explorations in SQL workloads

Peralta

Marcel

Verdeaux

et al. 2020

Information Systems

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“…Analysis graphs may also be complemented with a representation of the interactive dynamics for visual analysis. 44 Other work 45 has proposed a multidimensional algebra which allows for the description of analytical sessions, providing operators similar to the navigation operators in analysis graphs. Selection corresponds to the slice conditions in analysis situations that can be expressed by dimensional, multidimensional, and measure predicates.…”

Section: Modeling Of Analytical Queries and Processesmentioning

confidence: 99%

Reference Modeling for Data Analysis: The BIRD Approach

Schuetz

Neumayr

Schrefl

et al. 2016

Int. J. Coop. Info. Syst.

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Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are bestpractice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs.

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“…Among these (see (Marcel and Negre, 2011) and (Negre, 2009) for a detailed study), some focused on exploiting user profiles and preferences (Bellatreche et al, 2005;Jerbi et al, 2009b), and others focused on the discoveries made during analyses (Cariou et al, 2008;Sarawagi, 2000) as well as on exploiting logs containing sequences of queries previously run by other users on the same cube (Chatzopoulou et al, 2009;Giacometti et al, 2008Giacometti et al, , 2009Giacometti et al, , 2011Sapia, 1999;Yang et al, 2009). More recently, Romero et al (2011) proposed a multidimensional algebra for describing analytical sessions.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of user satisfaction with OLAP recommender systems: an application to RecoOLAP on a agricultural energetic consumption datawarehouse

Bimonte¹,

Negre

2016

IJBIS

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OLAP and Datawarehouse (DW) systems are technologies intended to support the decision-making process, enabling the analysis of a substantial volume of data. Decision makers explore warehoused data using OLAP operators to discover new trends and/or confirm business hypotheses. In the era of Big Data, the size of warehoused data has increased substantially, and the data have become increasingly difficult to use. One of the goals of recommender systems is to help users navigate large amounts of data. OLAP recommender systems have recently been proposed in the literature because the multidimensional analysis process is often tedious because the user may not know what the forthcoming query should be. However, user satisfaction with these systems has not yet been investigated. Indeed, only time and space performances and classical information retrieval metrics (e.g., accuracy) have been studied on fictive DWs and users. Thus, this work is the first study of the usefulness of OLAP recommender systems from the decision maker's point of view. Indeed, to the best of our knowledge, although several works have proposed OLAP recommender systems, they did not evaluate them against real-world data and users. With our experiments on a spatial DW concerning agricultural energetic consummation issued from the Energetic French Project, we prove that OLAP recommendation is useful via a real-world case study and confirm the importance of these academic tools.

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Describing Analytical Sessions Using a Multidimensional Algebra

Cited by 11 publications

References 12 publications

Detecting coherent explorations in SQL workloads

Detecting coherent explorations in SQL workloads

Reference Modeling for Data Analysis: The BIRD Approach

Evaluation of user satisfaction with OLAP recommender systems: an application to RecoOLAP on a agricultural energetic consumption datawarehouse

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