A personalization framework for OLAP queries

Bellatreche, Ladjel; Giacometti, Arnaud; Marcel, Patrick; Mouloudi, Hassina; Laurent, Dominique

doi:10.1145/1097002.1097005

Cited by 81 publications

(66 citation statements)

References 13 publications

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“…Indeed, the number of member values on each displayed dimension is generally very high and so, might not fit on the device. Therefore, constraints have to be taken into account in our visualization technique, and we think that the work reported in [2] provides an appropriate framework to deal with this problem.…”

Section: Resultsmentioning

confidence: 99%

“…This model is based on the geometrical representation of a cube and the human perception in the space, and it is shown how the logical and the presentation models can be integrated. In [2], the authors present an approach to visualizing multidimensional data in the presence of constraints and user preferences. In this approach, constraints refer to the fact that the device on which data are displayed imposes restrictions (e.g.…”

Section: Data Warehouse Visualizationmentioning

confidence: 99%

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Pixelizing Data Cubes: A Block-Based Approach

Choong

Laurent

2007

Pixelization Paradigm

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Abstract. Multidimensional databases are commonly used for decision making in the context of data warehouses. Considering the multidimensional model, data are presented as hypercubes organized according to several dimensions. However, in general, hypercubes have more than three dimensions and contain a huge amount of data, and so cannot be easily visualized. In this paper, we show that data cubes can be visualized as images by building blocks that contain mostly the same value. Blocks are built up using an APriori-like algorithm and each block is considered as a set of pixels which colors depend on the corresponding value. The key point of our approach is to set how to display a given block according to its corresponding value while taking into account that blocks may overlap. In this paper, we address this issue based on the Pixelization paradigm.

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Section: Resultsmentioning

confidence: 99%

Section: Data Warehouse Visualizationmentioning

confidence: 99%

Pixelizing Data Cubes: A Block-Based Approach

Choong

Laurent

2007

Pixelization Paradigm

Self Cite

View full text Add to dashboard Cite

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“…Jerbi et al (2008) modelled a context as a tree of multidimensional analysis (fact, measure, dimensions, and parameters). In the same context [a profile-based personalisation (Stefanidis et al, 2009)], the work of Bellatreche et al (2006) presents a framework which allowed the OLAP system to adapt the data displayed, in data-cube, based on constrains (limits imposed by the device used to regulate the display format). But in both works (Jerbi et al, 2008;Bellatreche et al, 2006), the user profile and context are given (without indicating how they are constructed).…”

Section: Context-aware Recommender Systemsmentioning

confidence: 99%

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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“…The first ones include user profiles (e.g., preferences) in the recommendation process to maximize the user's interest for suggested queries [6,9]. Interestingly, recommendations may integrate visualization-related constraints [2]. Second, methods based on query logs mainly address predictive recommendations of forthcoming queries and position analysis sessions as first-class citizens [4].…”

Section: Related Workmentioning

confidence: 99%

Semantics and Usage Statistics for Multi-dimensional Query Expansion

Thollot¹,

Kuchmann-Beauger²,

Aufaure

2012

Database Systems for Advanced Applications

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Abstract. As the amount and complexity of data keeps increasing in data warehouses, their exploration for analytical purposes may be hindered. Recommender systems have grown very popular on the Web with sites like Amazon, Netflix, etc. These systems proved successful to help users explore available content related to what they are currently looking at. Recent systems consider the use of recommendation techniques to suggest data warehouse queries and help an analyst pursue its exploration. In this paper, we present a personalized query expansion component which suggests measures and dimensions to iteratively build consistent queries over a data warehouse. Our approach leverages (a) semantics defined in multi-dimensional domain models, (b) collaborative usage statistics derived from existing repositories of Business Intelligence documents like dashboards and reports and (c) preferences defined in a user profile. We finally present results obtained with a prototype implementation of an interactive query designer.

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A personalization framework for OLAP queries

Cited by 81 publications

References 13 publications

Pixelizing Data Cubes: A Block-Based Approach

Pixelizing Data Cubes: A Block-Based Approach

A knowledge-based framework for a mobile OLAP system

Semantics and Usage Statistics for Multi-dimensional Query Expansion

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