Recommending Multidimensional Queries

Giacometti, Arnaud; Marcel, Patrick; Negre, Elsa

doi:10.1007/978-3-642-03730-6_36

Cited by 42 publications

(57 citation statements)

References 12 publications

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“…The preferences generated in these works are also explicitly expressed. In the context of the history-based personalisation (Stefanidis et al, 2009), the approaches proposed in Giacometti et al (2009Giacometti et al ( , 2011 aimed at recommending, to the current user, data found in previous sessions similar to the one requested in the current session.…”

Section: Context-aware Recommender Systemsmentioning

confidence: 99%

“…We compared our recommender-based exploration with simple collaborative filtering methods (Herloker et al, 2004), that is named personalised KNN in the following figures and with two of recommender multidimensional systems named respectively Custer H (Giacometti et al, 2008) and EdH (Giacometti et al, 2009). The first one uses K-medoid clustering and collaborative filtering; the second one combines the edit distance with simple Hamming distance for comparing references (multidimensional components) and uses collaborative filtering.…”

Section: A Recommender-based Exploration Of Data Cubesmentioning

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: Context-aware Recommender Systemsmentioning

confidence: 99%

Section: A Recommender-based Exploration Of Data Cubesmentioning

confidence: 99%

A knowledge-based framework for a mobile OLAP system

Nachida

Boussaïd²,

Nader

2015

IJDSS

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“…On the other hand, multidimensional databases store huge volumes of data, and as a consequence OLAP queries can return large result sets. Extensional computation of query similarity (i.e., made by comparing query results like in [16] and [17]) can thus pose serious efficiency problems. For this reason we use a metric that computes query similarity at the intensional level, i.e., by only looking at the query expressions.…”

Section: Query Similarity Metricmentioning

confidence: 99%

Predicting Your Next OLAP Query Based on Recent Analytical Sessions

Aufaure

Kuchmann-Beauger

Marcel

et al. 2013

Data Warehousing and Knowledge Discovery

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Abstract. In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments.

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“…We focus in this paper on multi-dimensional queries (structured queries) and data warehouses (data sources), without restricting the generality of our approach. A simple multi-dimensional query (see [4] for a more detailed definition) is usually represented by a number of dimensions (organized in hierarchies) or their attributes, measures (aggregated KPIs with respect to the top 5 middle-aged customers in my city top -unknown artifact (a) A user's question and derived semantic units (comparable to a parse tree in natural language processing). Successive tokens that satisfy some constraints (e.g.…”

Section: Problem Statementmentioning

confidence: 99%

QUASL: A framework for question answering and its Application to business intelligence

Kuchmann-Beauger¹,

Brauer

Aufaure

2013

IEEE 7th International Conference on Research Challenges in Information Science (RCIS)

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Abstract-Question Answering (Q&A) from structured data is a technique that may revolutionize enterprise search. A very promising use-case for such technology is Business Intelligence (BI). In order to make BI more accessible to end-users, some efforts have been made in the field of search for existing reports. However, the problem of converting an end-user's natural language input to a valid structured query in an ad-hoc fashion hasn't been sufficiently solved yet. In this paper we present a framework for Q&A systems that operate on structured data. The main innovation is that the framework allows defining a mapping between recognized semantics of a user's questions to a structured query model that can be executed on arbitrary data sources. It bases on popular standards like RDF and SparQL and is therefore very easy to adapt to other domains or usecases. We will describe the application of this framework at hand of a BI question answering use-case, which also includes the personalization of generated queries, demonstrating the realworld applicability of our approach. In our experiments, we demonstrate that with our approach one can easily achieve a similar answering quality as one of the most popular Q&A systems on the Web.

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Recommending Multidimensional Queries

Cited by 42 publications

References 12 publications

A knowledge-based framework for a mobile OLAP system

A knowledge-based framework for a mobile OLAP system

Predicting Your Next OLAP Query Based on Recent Analytical Sessions

QUASL: A framework for question answering and its Application to business intelligence

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