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Patrick Marcel

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Eirini Mouselli

University of Ioannina/Greece

Panos Vassiliadis

University of Ioannina

Jean-François Boulicaut

Insa de Lyon

Wright State University

UNIVERSIDAD NACIONAL DE CHIMBORAZO

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Papers by Patrick Marcel

Causality based explanations in multi-stakeholder recommendations

This paper introduces two novel contributions in the context of multi-stakeholder recommender sys... more This paper introduces two novel contributions in the context of multi-stakeholder recommender system. First, we present a simple and intuitive method for multi-objective and multi-stakeholder recommendation that relies on preferences for each stakeholder. The second contribution is the adaptation of Halpern and Pearl's definition of causality to introduce definitions of causes and explanations for multi-stakeholder recommender systems. Experiments conducted on real data study the tractability of such explanation approach in the context of RS.

Interest-based recommendations for business intelligence users

Information Systems, 2019

It is quite common these days for experts, casual analysts, executives and data enthusiasts, to a... more It is quite common these days for experts, casual analysts, executives and data enthusiasts, to analyze large datasets through user-friendly interfaces on top of Business Intelligence (BI) systems. However, current BI systems do not adequately detect and characterize user interests, which may lead to tedious and unproductive interactions. In this paper, we propose a collaborative recommender system for BI interactions, specifically designed to take advantage of identified user interests. Such user interests are discovered by characterizing the intent of the interaction with the BI system. Building on user modeling for proactive search systems, we identify a set of features for an adequate description of intents, and a similarity measure for grouping intents into coherent clusters. On top of these automatically identified interests, we build a collaborative recommender system based on a Markov model that represents the probability for a user to switch from one interest to another. We validate our approach experimentally with an in-depth user study, where we analyze traces of BI navigation. Our results are twofold. First, we show that our similarity measure outperforms a state-of-the-art query similarity measure and yields a very good precision with respect to expressed user interests. Second, we compare our recommender system to two state-of-the-art systems to demonstrate the benefit of relying on user interests.

Log-driven user-centric OLAP

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014

Supporting exploration of large databases attracts lots of attention these days, with many approa... more

Recommending Multidimensional Queries

Lecture Notes in Computer Science, 2009

Interactive analysis of datacube, in which a user navigates a cube by launching a sequence of que... more Interactive analysis of datacube, in which a user navigates a cube by launching a sequence of queries is often tedious since the user may have no idea of what the forthcoming query should be in his current analysis. To better support this process we propose in this paper to apply a Collaborative Work approach that leverages former explorations of the cube to recommend OLAP queries. The system that we have developed adapts Approximate String Matching, a technique popular in Information Retrieval, to match the current analysis with the former explorations and help suggesting a query to the user. Our approach has been implemented with the open source Mondrian OLAP server to recommend MDX queries and we have carried out some preliminary experiments that show its efficiency for generating effective query recommendations.

Rule-based restructuring and summarizing in multidimensional data-bases

ABSTRACT

A framework for summarizing a log of OLAP queries

2010 International Conference on Machine and Web Intelligence, 2010

Leveraging query logs benefits the users analyzing large data warehouses. But so far nothing exis... more Leveraging query logs benefits the users analyzing large data warehouses. But so far nothing exists to allow the user to have concise and usable representation of what is in the log. In this paper, we propose a framework for summarizing OLAP query logs. This framework is based on the idea that a query can summarize another query and that a log can summarize another log. It includes a simple language to declaratively specify a summary, a measure to assess the quality of a summary and an algorithm for automatically computing a good quality summary of a query log.

Summarizing and Querying Logs of OLAP Queries

Studies in Computational Intelligence, 2013

Leveraging query logs benefits the users analyzing large data warehouses with OLAP queries. But s... more Leveraging query logs benefits the users analyzing large data warehouses with OLAP queries. But so far nothing exists to allow the user to have concise and usable representations of what is in the log. In this article, we present a framework for summarizing and querying OLAP query logs. The basic idea is that a query summarizes another query and that a log, which is a sequence of queries, summarizes another log. Our formal framework includes a language to declaratively specify a summary, and a language for querying and manipulating logs. We also propose a simple measure based on precision and recall, to assess the quality of summaries, and two strategies for automatically computing log summaries of good quality. Finally we show how some simple properties on the summaries can be used to query the log efficiently. The framework is implemented using the Mondrian open source OLAP engine. Its interest is illustrated with experiments on synthetic yet realistic MDX query logs.

Manipulation de représentations de cubes de données

Materializing Baseline Views for Deviation Detection Exploratory OLAP

Lecture Notes in Computer Science, 2015

Alert-raising and deviation detection in OLAP and exploratory search concerns calling the user's ... more Alert-raising and deviation detection in OLAP and exploratory search concerns calling the user's attention to variations and nonuniform data distributions, or directing the user to the most interesting exploration of the data. In this paper, we are interested in the ability of a data warehouse to monitor continuously new data, and to update accordingly a particular type of materialized views recording statistics, called baselines. It should be possible to detect deviations at various levels of aggregation, and baselines should be fully integrated into the database. We propose Multi-level Baseline Materialized Views (BMV), including the mechanisms to build, refresh and detect deviations. We also propose an incremental approach and formula for refreshing baselines efficiently. An experimental setup proves the concept and shows its efficiency.

An Approach for Alert Raising in Real-Time Data Warehouses

This work proposes an approach for alert raising within a real-time data warehouse environment. I... more This work proposes an approach for alert raising within a real-time data warehouse environment. It is based on the calculation of confidence inter- vals for measures from historical facts. As new facts arrive to the data warehouse on a real-time basis, they are systematically compared with their appropriate confidence intervals and alerts are raised when anomalies are detected. The in- terest of this approach is illustrated using the particular real world use case of technical analysis of stock data.

A rule-based CQL for 2-dimensional tables

Lecture Notes in Computer Science, 1996

... mark group, grlllmathlPhyslavg kate 15 12 13.5 john 10 12 11 suzy 12 11 11.5 lan 15 16 Fig. .... more ... mark group, grlllmathlPhyslavg kate 15 12 13.5 john 10 12 11 suzy 12 11 11.5 lan 15 16 Fig. ... a(b,c):e e-a(Xb,c):e +- {a(b,c): e, a(b. b,c): e} is a finite model of the program since no valu-ation can map X to bb The infinite interpretation {a(b,c): e, a(b. b,c):e a(bb b,c): e, a(bbb b,c): e ...

A Holistic Approach to OLAP Sessions Composition

Proceedings of the 17th International Workshop on Data Warehousing and OLAP - DOLAP '14, 2014

OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in dat... more OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of information. Given the huge size of the data space, this exploration process is often tedious and may leave the user disoriented and frustrated. This paper presents an OLAP tool named Falseto (Former AnalyticaL Session for lEss Tedious Olap), that is meant to assist query and session composition, by letting the user summarize, browse, query, and reuse former analytical sessions. Falseto's implementation on top of a formal framework is detailed. We also report the experiments we run to obtain and analyze real OLAP sessions and assess Falseto with them. Finally, we discuss how Falseto can be seen as a starting point for bridging OLAP with exploratory search, a search paradigm centered on the user and the evolution of her knowledge.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

International Journal of Data Warehousing and Mining, 2013

In this vision paper, the authors discuss models and techniques for integrating, processing and q... more In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP ...

Predicting Your Next OLAP Query Based on Recent Analytical Sessions

Lecture Notes in Computer Science, 2013

In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries... more 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.

A framework for recommending OLAP queries

Proceedings of the ACM 11th international workshop on Data warehousing and OLAP, 2008

An OLAP analysis session can be defined as an interactive session during which a user launches qu... more An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.

A personalization framework for OLAP queries

Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, 2005

OLAP users heavily rely on visualization of query answers for their interactive analysis of massi... more OLAP users heavily rely on visualization of query answers for their interactive analysis of massive amounts of data. Very often, these answers cannot be visualized entirely and the user has to navigate through them to find relevant facts. In this paper, we propose a framework for personalizing OLAP queries. In this framework, the user is asked to give his (her) preferences and a visualization constraint, that can be for instance the limitations imposed by the device used to display the answer to a query. Given this, for each query, our method computes the part of the answer that respects both the user preferences and the visualization constraint. In addition, a personalized structure for the visualization is proposed.

Describing analytical sessions using a multidimensional algebra

Recent efforts to support analytical tasks over relational sources have pointed out the necessity... more Recent efforts to support analytical tasks over relational sources have pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries and exploit them in decisionoriented processes (such as query recommendation or physical tuning). Issued queries should be decomposed, stored and manipulated in a dedicated subsystem. With this aim, we present a novel approach for representing SQL analytical queries in terms of a multidimensional algebra, which better characterizes the analytical efforts of the user. In this paper we discuss how an SQL query can be formulated as a multidimensional algebraic characterization. Then, we discuss how to normalize them in order to bridge (i.e., collapse) several SQL queries into a single characterization (representing the analytical session), according to their logical connections.

Query Recommendations for OLAP Discovery-Driven Analysis

International Journal of Data Warehousing and Mining, 2011

Recommending database queries is an emerging and promising field of research and is of particular... more Recommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments we...

Cube Based Summaries of Large Association Rule Sets

Lecture Notes in Computer Science, 2010

A Generic Framework for Rule-Based Classication

Classification is an important field of data mining problems. Given a set of labeled training exa... more Classification is an important field of data mining problems. Given a set of labeled training examples the classification task constructs a classifier. A classifier is a global model which is used to predict the class label for data objects that are unlabeled. Many approaches have been proposed for the classification problem. Among them, rule-induction, associative and instance-centric approaches have been closely integrated with constraint-based data mining. There also exist several classification methods based on each of these approaches, e.g. AQ, CBA and HARMONY respectively. Moreover, each classification method may provide one or more algorithms that exploit particular local pattern extraction techniques to construct a classifier. In this paper, we proposed a generic classification framework that encompasses all the mentioned approaches. Based on our framework we present a formal context to define basic concepts, operators for classifier construction, composition of classifiers, and class prediction. Moreover, we proposed a generic classifier construction algorithm (ICCA) that incrementally constructs a classifier using the proposed operators. This algorithm is generic in the sense that it can uniformly represent a large class of existing classification algorithms. We also present the properties under which different optimization possibilities are provided in the generic algorithm.

Causality based explanations in multi-stakeholder recommendations

This paper introduces two novel contributions in the context of multi-stakeholder recommender sys... more This paper introduces two novel contributions in the context of multi-stakeholder recommender system. First, we present a simple and intuitive method for multi-objective and multi-stakeholder recommendation that relies on preferences for each stakeholder. The second contribution is the adaptation of Halpern and Pearl's definition of causality to introduce definitions of causes and explanations for multi-stakeholder recommender systems. Experiments conducted on real data study the tractability of such explanation approach in the context of RS.

Interest-based recommendations for business intelligence users

Information Systems, 2019

It is quite common these days for experts, casual analysts, executives and data enthusiasts, to a... more It is quite common these days for experts, casual analysts, executives and data enthusiasts, to analyze large datasets through user-friendly interfaces on top of Business Intelligence (BI) systems. However, current BI systems do not adequately detect and characterize user interests, which may lead to tedious and unproductive interactions. In this paper, we propose a collaborative recommender system for BI interactions, specifically designed to take advantage of identified user interests. Such user interests are discovered by characterizing the intent of the interaction with the BI system. Building on user modeling for proactive search systems, we identify a set of features for an adequate description of intents, and a similarity measure for grouping intents into coherent clusters. On top of these automatically identified interests, we build a collaborative recommender system based on a Markov model that represents the probability for a user to switch from one interest to another. We validate our approach experimentally with an in-depth user study, where we analyze traces of BI navigation. Our results are twofold. First, we show that our similarity measure outperforms a state-of-the-art query similarity measure and yields a very good precision with respect to expressed user interests. Second, we compare our recommender system to two state-of-the-art systems to demonstrate the benefit of relying on user interests.

Log-driven user-centric OLAP

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014

Supporting exploration of large databases attracts lots of attention these days, with many approa... more

Recommending Multidimensional Queries

Lecture Notes in Computer Science, 2009

Interactive analysis of datacube, in which a user navigates a cube by launching a sequence of que... more Interactive analysis of datacube, in which a user navigates a cube by launching a sequence of queries is often tedious since the user may have no idea of what the forthcoming query should be in his current analysis. To better support this process we propose in this paper to apply a Collaborative Work approach that leverages former explorations of the cube to recommend OLAP queries. The system that we have developed adapts Approximate String Matching, a technique popular in Information Retrieval, to match the current analysis with the former explorations and help suggesting a query to the user. Our approach has been implemented with the open source Mondrian OLAP server to recommend MDX queries and we have carried out some preliminary experiments that show its efficiency for generating effective query recommendations.

Rule-based restructuring and summarizing in multidimensional data-bases

ABSTRACT

A framework for summarizing a log of OLAP queries

2010 International Conference on Machine and Web Intelligence, 2010

Leveraging query logs benefits the users analyzing large data warehouses. But so far nothing exis... more Leveraging query logs benefits the users analyzing large data warehouses. But so far nothing exists to allow the user to have concise and usable representation of what is in the log. In this paper, we propose a framework for summarizing OLAP query logs. This framework is based on the idea that a query can summarize another query and that a log can summarize another log. It includes a simple language to declaratively specify a summary, a measure to assess the quality of a summary and an algorithm for automatically computing a good quality summary of a query log.

Summarizing and Querying Logs of OLAP Queries

Studies in Computational Intelligence, 2013

Leveraging query logs benefits the users analyzing large data warehouses with OLAP queries. But s... more Leveraging query logs benefits the users analyzing large data warehouses with OLAP queries. But so far nothing exists to allow the user to have concise and usable representations of what is in the log. In this article, we present a framework for summarizing and querying OLAP query logs. The basic idea is that a query summarizes another query and that a log, which is a sequence of queries, summarizes another log. Our formal framework includes a language to declaratively specify a summary, and a language for querying and manipulating logs. We also propose a simple measure based on precision and recall, to assess the quality of summaries, and two strategies for automatically computing log summaries of good quality. Finally we show how some simple properties on the summaries can be used to query the log efficiently. The framework is implemented using the Mondrian open source OLAP engine. Its interest is illustrated with experiments on synthetic yet realistic MDX query logs.

Manipulation de représentations de cubes de données

Materializing Baseline Views for Deviation Detection Exploratory OLAP

Lecture Notes in Computer Science, 2015

Alert-raising and deviation detection in OLAP and exploratory search concerns calling the user's ... more Alert-raising and deviation detection in OLAP and exploratory search concerns calling the user's attention to variations and nonuniform data distributions, or directing the user to the most interesting exploration of the data. In this paper, we are interested in the ability of a data warehouse to monitor continuously new data, and to update accordingly a particular type of materialized views recording statistics, called baselines. It should be possible to detect deviations at various levels of aggregation, and baselines should be fully integrated into the database. We propose Multi-level Baseline Materialized Views (BMV), including the mechanisms to build, refresh and detect deviations. We also propose an incremental approach and formula for refreshing baselines efficiently. An experimental setup proves the concept and shows its efficiency.

An Approach for Alert Raising in Real-Time Data Warehouses

This work proposes an approach for alert raising within a real-time data warehouse environment. I... more This work proposes an approach for alert raising within a real-time data warehouse environment. It is based on the calculation of confidence inter- vals for measures from historical facts. As new facts arrive to the data warehouse on a real-time basis, they are systematically compared with their appropriate confidence intervals and alerts are raised when anomalies are detected. The in- terest of this approach is illustrated using the particular real world use case of technical analysis of stock data.

A rule-based CQL for 2-dimensional tables

Lecture Notes in Computer Science, 1996

... mark group, grlllmathlPhyslavg kate 15 12 13.5 john 10 12 11 suzy 12 11 11.5 lan 15 16 Fig. .... more ... mark group, grlllmathlPhyslavg kate 15 12 13.5 john 10 12 11 suzy 12 11 11.5 lan 15 16 Fig. ... a(b,c):e e-a(Xb,c):e +- {a(b,c): e, a(b. b,c): e} is a finite model of the program since no valu-ation can map X to bb The infinite interpretation {a(b,c): e, a(b. b,c):e a(bb b,c): e, a(bbb b,c): e ...

A Holistic Approach to OLAP Sessions Composition

Proceedings of the 17th International Workshop on Data Warehousing and OLAP - DOLAP '14, 2014

OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in dat... more OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of information. Given the huge size of the data space, this exploration process is often tedious and may leave the user disoriented and frustrated. This paper presents an OLAP tool named Falseto (Former AnalyticaL Session for lEss Tedious Olap), that is meant to assist query and session composition, by letting the user summarize, browse, query, and reuse former analytical sessions. Falseto's implementation on top of a formal framework is detailed. We also report the experiments we run to obtain and analyze real OLAP sessions and assess Falseto with them. Finally, we discuss how Falseto can be seen as a starting point for bridging OLAP with exploratory search, a search paradigm centered on the user and the evolution of her knowledge.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

International Journal of Data Warehousing and Mining, 2013

In this vision paper, the authors discuss models and techniques for integrating, processing and q... more In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP ...

Predicting Your Next OLAP Query Based on Recent Analytical Sessions

Lecture Notes in Computer Science, 2013

In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries... more 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.

A framework for recommending OLAP queries

Proceedings of the ACM 11th international workshop on Data warehousing and OLAP, 2008

An OLAP analysis session can be defined as an interactive session during which a user launches qu... more An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.

A personalization framework for OLAP queries

Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, 2005

OLAP users heavily rely on visualization of query answers for their interactive analysis of massi... more OLAP users heavily rely on visualization of query answers for their interactive analysis of massive amounts of data. Very often, these answers cannot be visualized entirely and the user has to navigate through them to find relevant facts. In this paper, we propose a framework for personalizing OLAP queries. In this framework, the user is asked to give his (her) preferences and a visualization constraint, that can be for instance the limitations imposed by the device used to display the answer to a query. Given this, for each query, our method computes the part of the answer that respects both the user preferences and the visualization constraint. In addition, a personalized structure for the visualization is proposed.

Describing analytical sessions using a multidimensional algebra

Recent efforts to support analytical tasks over relational sources have pointed out the necessity... more Recent efforts to support analytical tasks over relational sources have pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries and exploit them in decisionoriented processes (such as query recommendation or physical tuning). Issued queries should be decomposed, stored and manipulated in a dedicated subsystem. With this aim, we present a novel approach for representing SQL analytical queries in terms of a multidimensional algebra, which better characterizes the analytical efforts of the user. In this paper we discuss how an SQL query can be formulated as a multidimensional algebraic characterization. Then, we discuss how to normalize them in order to bridge (i.e., collapse) several SQL queries into a single characterization (representing the analytical session), according to their logical connections.

Query Recommendations for OLAP Discovery-Driven Analysis

International Journal of Data Warehousing and Mining, 2011

Recommending database queries is an emerging and promising field of research and is of particular... more Recommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments we...

Cube Based Summaries of Large Association Rule Sets

Lecture Notes in Computer Science, 2010

A Generic Framework for Rule-Based Classication

Classification is an important field of data mining problems. Given a set of labeled training exa... more Classification is an important field of data mining problems. Given a set of labeled training examples the classification task constructs a classifier. A classifier is a global model which is used to predict the class label for data objects that are unlabeled. Many approaches have been proposed for the classification problem. Among them, rule-induction, associative and instance-centric approaches have been closely integrated with constraint-based data mining. There also exist several classification methods based on each of these approaches, e.g. AQ, CBA and HARMONY respectively. Moreover, each classification method may provide one or more algorithms that exploit particular local pattern extraction techniques to construct a classifier. In this paper, we proposed a generic classification framework that encompasses all the mentioned approaches. Based on our framework we present a formal context to define basic concepts, operators for classifier construction, composition of classifiers, and class prediction. Moreover, we proposed a generic classifier construction algorithm (ICCA) that incrementally constructs a classifier using the proposed operators. This algorithm is generic in the sense that it can uniformly represent a large class of existing classification algorithms. We also present the properties under which different optimization possibilities are provided in the generic algorithm.