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2. Cubes convexes

Author

Nedjar, Sebastien, Casali, Alain, Cicchetti, Rosine, and Lakhal, Lotfi

Subjects
Computer Science - Databases
Abstract

In various approaches, data cubes are pre-computed in order to answer efficiently OLAP queries. The notion of data cube has been declined in various ways: iceberg cubes, range cubes or differential cubes. In this paper, we introduce the concept of convex cube which captures all the tuples of a datacube satisfying a constraint combination. It can be represented in a very compact way in order to optimize both computation time and required storage space. The convex cube is not an additional structure appended to the list of cube variants but we propose it as a unifying structure that we use to characterize, in a simple, sound and homogeneous way, the other quoted types of cubes. Finally, we introduce the concept of emerging cube which captures the significant trend inversions. characterizations.

Published
2010
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4. Extracting Correlated Patterns on Multicore Architectures

Author

Casali, Alain, Ernst, Christian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cuzzocrea, Alfredo, editor, Kittl, Christian, editor, Simos, Dimitris E., editor, Weippl, Edgar, editor, and Xu, Lida, editor

Published
2013
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5. Constrained Closed Datacubes

Author

Nedjar, Sébastien, Casali, Alain, Cicchetti, Rosine, Lakhal, Lotfi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Kwuida, Léonard, editor, and Sertkaya, Barış, editor

Published
2010
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6. Closed Cube Lattices

Author

Casali, Alain, Nedjar, Sebastien, Cicchetti, Rosine, Lakhal, Lotfi, Kozielski, Stanislaw, editor, and Wrembel, Robert, editor

Published
2009
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7. Extracting Decision Correlation Rules

Author

Casali, Alain, Ernst, Christian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bhowmick, Sourav S., editor, Küng, Josef, editor, and Wagner, Roland, editor

Published
2009
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8. Upper Borders for Emerging Cubes

Author

Nedjar, Sébastien, Casali, Alain, Cicchetti, Rosine, Lakhal, Lotfi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Song, Il-Yeol, editor, Eder, Johann, editor, and Nguyen, Tho Manh, editor

Published
2008
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9. Emerging Cubes for Trends Analysis in Olap Databases

Author

Nedjar, Sébastien, Casali, Alain, Cicchetti, Rosine, Lakhal, Lotfi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Song, Il Yeal, editor, Eder, Johann, editor, and Nguyen, Tho Manh, editor

Published
2007
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10. Convex Cube: Towards a Unified Structure for Multidimensional Databases

Author

Casali, Alain, Nedjar, Sébastien, Cicchetti, Rosine, Lakhal, Lotfi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wagner, Roland, editor, Revell, Norman, editor, and Pernul, Günther, editor

Published
2007
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11. Lossless Reduction of Datacubes

Author

Casali, Alain, Cicchetti, Rosine, Lakhal, Lotfi, Novelli, Noël, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bressan, Stéphane, editor, Küng, Josef, editor, and Wagner, Roland, editor

Published
2006
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12. Essential Patterns: A Perfect Cover of Frequent Patterns

Author

Casali, Alain, Cicchetti, Rosine, Lakhal, Lotfi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tjoa, A Min, editor, and Trujillo, Juan, editor

Published
2005
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13. Mining Borders of the Difference of Two Datacubes

Author

Casali, Alain, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kambayashi, Yahiko, editor, Mohania, Mukesh, editor, and Wöß, Wolfram, editor

Published
2004
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18. Closed Cube Lattices

Author

Casali, Alain, primary, Nedjar, Sebastien, additional, Cicchetti, Rosine, additional, and Lakhal, Lotfi, additional

Published
2008
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24. A Causality Based Feature Selection Approach for Multivariate Time Series Forecasting

Author

Hmamouche, Youssef, Casali, Alain, Lakhal, Lotfi, Laboratoire d'informatique Fondamentale de Marseille ( LIF ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'informatique Fondamentale de Marseille (LIF), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Data Mining at scale (DANA), Laboratoire d'Informatique et Systèmes (LIS), Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Multivariate Time Series Forecasting, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Granger Causality, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], –Multivariate Time Series Forecasting, Feature selection, Forecasting using Feature Selection, [ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]
Abstract

International audience; —The field of time series forecasting has progressed significantly in recent decades, specially in regards to the need of forecasting economic data. That said, some issues still arise. In particular when we are working with a set of time series that have a large number of variables. Hence, a selection step is usually needed in order to reduce the number of variables that will contribute to forecast each target time series. In this paper, we propose a feature selection and / or dimension reduction algorithm for forecasting multivariate time series, based on (i) the notion of the Granger causality, and (ii) on a selection step based on a clustering strategy. Finally, we carry out experiments on different real data sets, by comparing our proposal and some of the most used feature selection methods. Experiments show that we improved the forecasting accuracy compared with the evaluated methods.

Published
2017

25. GFSM: a Feature Selection Method for Improving Time Series Forecasting

Author

Hmamouche, Youssef, Przymus, Piotr, Casali, Alain, Lakhal, Lotfi, Laboratoire d'informatique Fondamentale de Marseille (LIF), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Data Mining at scale (DANA), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Marseille (LIM), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Multi- variate prediction models, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Time Series Forecasting, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Feature Selection, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Artificial Neural Networks
Abstract

International audience; Handling time series forecasting with many predictors is a popular topic in the era of "Big data", where wast amounts of observed variables are stored and used in analytic processes. Classical prediction models face some limitations when applied to large-scale data. Using all the existing predictors increases the computational time and does not necessarily improve the forecast accuracy. The challenge is to extract the most relevant predictors contributing to the forecast of each target time series. We propose a causal-feature selection algorithm specific to multiple time series forecasting based on a clustering approach. Experiments are conducted on US and Australia macroeconomic datasets using different prediction models. We compare our method to some widely used dimension reduction and feature selection methods including principal component analysis PCA, Kernel PCA and factor analysis. The proposed algorithm improves the forecast accuracy compared to the evaluated methods on the tested datasets.

Published
2017

27. Reduced representations of Emerging Cubes for OLAP database mining

Author

Nedjar, Sebastien, Casali, Alain, Cicchetti, Rosine, and Lakhal, Lotfi

Subjects
Data mining -- Analysis, Data warehousing/data mining, OLAP, Business, international
Abstract

Byline: Sebastien Nedjar, Alain Casali, Rosine Cicchetti, Lotfi Lakhal In this paper, we investigate reduced representations for the Emerging Cube. We use the borders, classical in data mining, for the Emerging Cube. These borders can support classification tasks to know whether a trend is emerging or not. However, the borders do not make possible to retrieve the measure values. This is why we introduce two new and reduced representations without measure loss: the L-Emerging Closed Cube and Emerging Quotient Cube. We state the relationship between the introduced representations. Experiments performed on various data sets are intended to measure the size of the three reduced representations.

Published
2009

28. Extracting correlated parameters on multicore architectures

Author

Ernst, Christian, Casali, Alain, Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, Laboratoire d'informatique Fondamentale de Marseille (LIF), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Ernst, Christian, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Abstract

16 pages; International audience; In this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We nally present rst results on the reached performance gains.

Published
2013

29. Multicore Mining of Correlated Patterns

Author

Ernst, Christian, Casali, Alain, Ernst, Christian, IARIA, Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, Laboratoire d'informatique Fondamentale de Marseille (LIF), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), LIF Marseille, and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Multicore Architecture, Decision Correlation Rule, Parallel Pattern Library, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Data Mining
Abstract

6 pages; International audience; In this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We finally present first results on the reached performance gains.

Published
2013

30. Data Preparation in the MineCor KDD Framework

Author

Ernst, Christian, Casali, Alain, Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), LIF Marseille, and Ernst, Christian

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Decision Correlation Rule, Semiconductor Manufacturing, Data Preparation, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Data Mining, IARIA, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
Abstract

7 pages; International audience; Yield enhancement is a key issue in semiconductor manufacturing. Data mining tools can therefore be helpful, by extracting hidden links between numerous complex process control parameters. In order to highlight correlations between such parameters, we developed a complete Knowledge Discovery in Databases (KDD) model, called MineCor. Its mining heart uses a new method derived from association rules programming, based on lectic search and contingency vectors. After recalling these concepts, this paper focuses on data preprocessing and transformation functions, which have an important impact on final results. An overall presentation of these functions, of some significant experimental results and of associated performances are provided and finally discussed.

Published
2011

31. Mining Literal Correlation Rules from Itemsets

Author

Casali, Alain, Ernst, Christian, Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, and Ernst, Christian

Subjects
Literal Pattern, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Data Mining, IARIA, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Chi2 Correlation Statistic
Abstract

6 pages; International audience; Nowadays, data mining tools are becoming more and more popular to extract knowledge from a huge volume of data. In this paper, our aim is to extract Literal Correlation Rules: Correlation Rules admitting literal patterns given a set of items and a binary relation. If a pattern represents a valid Correlation Rule, then any literal belonging to its Canonical Base represents a valid Literal Correlation Rule. Moreover, in order to highlight only relevant Literal Correlation Rules, we add a pruning step based on a support threshold. To extract such rules, we modify the LHS-CHI2 Algorithm and perform some experiments.

Published
2011

32. A KDD Model to Discover Correlated Parameters in Semiconductor Manufacturing Processes

Author

Casali, Alain, Ernst, Christian, Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, and Ernst, Christian

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Published
2010

34. Extraction de règles de corrélation décisionnelles

Author

Casali, Alain, Ernst, Christian, Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Département Sciences de la Fabrication et Logistique (SFL-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CMP-GC, Ganascia, J.G., Gançarski P., and Salmeron, Sabine

Subjects
Statistique du Khi2, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Règles décisionnelles, Fouille de données, Ordre lectique
Abstract

Actes des neuvièmes Journées Extractions et gestion des connaissances EGC'2009, Strasbourg du 27 au 30 janvier 2009. - Numéro de : "Revue des nouvelles technologies de l'information", ISSN 1764-1667, RNTI-E-15, 2009; International audience; Dans cet article, nous introduisons deux nouveaux concepts : les règles de corrélation décisionnelles et les vecteurs de contingence. Le premier résulte d'un couplage entre les règles de corrélation et les règles de décision. Il permet de mettre en évidence des liens pertinents entre certains ensembles de motifs d'une relation binaire et les valeurs d'un attribut cible (appartenant à cette même relation) en se basant à la fois sur la mesure du Khi-carré et sur le support des motifs extraits. De par la nature du problème, les algorithmes par niveaux font que l'extraction des résultats a lieu avec des temps de réponse élevés et une occupation mémoire importante. Afin de palier à ces deux inconvénients, nous proposons un algorithme basé sur l'ordre lectique et les vecteurs de contingence.

Published
2009

35. Motifs essentiels et inférence des fréquences

Author

Casali, Alain, Cicchetti, Rosine, Lakhal, Lotfi, Lopes, Stéphane, Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Parallélisme, Réseaux, Systèmes, Modélisation (PRISM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Lopes, Stéphane

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2004

38. Summarizing Datacubes

Author

Cicchetti, Rosine, primary, Lakhal, Lotfi, additional, Nedjar, Sébastien, additional, Novelli, Noël, additional, and Casali, Alain, additional

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44. Cubes convexes

Author

Nedjar, Sébastien, primary, Casali, Alain, additional, Cicchetti, Rosine, additional, and Lakhal, Lofti, additional

Published
2006
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50. Convex Cube: Towards a Unified Structure for Multidimensional Databases.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Wagner, Roland, Revell, Norman, Pernul, Günther, Casali, Alain, and Nedjar, Sébastien

Abstract

In various approaches, data cubes are pre-computed in order to efficiently answer Olap queries. Such cubes are also successfully used for multidimensional analysis of data streams. The notion of data cube has been explored in various ways: iceberg cubes, range cubes, differential cubes or emerging cubes. In this paper, we introduce the concept of convex cube which captures all the tuples satisfying a monotone and/or antimonotone constraint combination. It can be represented in a very compact way in order to optimize both computation time and required storage space. The convex cube is not an additional structure appended to the list of cube variants but we propose it as a unifying structure that we use to characterize, in a simple, sound and homogeneous way, the other quoted types of cubes. [ABSTRACT FROM AUTHOR]

Published
2007
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