Giuseppe Manco
Consiglio Nazionale delle Ricerche (CNR), ICAR, Faculty Member
A novel methodology for clustering XML documents is discussed. The underlying idea is grouping documents which exhibit structural similarities. To this purpose, a suitable technique for identifying meaningful matchings among the nodes of... more
A novel methodology for clustering XML documents is discussed. The underlying idea is grouping documents which exhibit structural similarities. To this purpose, a suitable technique for identifying meaningful matchings among the nodes of two XML document trees is investigated. The proposed technique also allows to associate to each set of related documents a single prototype XML document, i.e. a representative subsuming the most relevant features of the documents in the set. Suitable techniques for both building and refining cluster-specific representatives are analyzed. Some initial experimental results show the effectiveness of our approach.
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Research Interests: Mathematics, Computer Science, Clustering, Similarity, Graph, and 5 moreROAD NETWORK, Trajectories, S, Moving Objects, and arXiv
We consider in this paper an extension of Datalog with mechanisms for temporal, nonmonotonic and nondeterministic reasoning, which we refer to as Datalog++. We study its semantics, and show how iterated fixpoint and stable model semantics... more
We consider in this paper an extension of Datalog with mechanisms for temporal, nonmonotonic and nondeterministic reasoning, which we refer to as Datalog++. We study its semantics, and show how iterated fixpoint and stable model semantics can be combined to the purpose of clarifying the interpretation of Datalog++ programs, and supporting their efficient execution. On this basis, the design of appropriate optimization techniques for Datalog++ is also discussed.
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The process of analyzing and organizing e-mail messages is a challenging application of Web and Text mining techniques. In recent years, the increasing popularity of the Web as a mean for sharing information has generated a huge traffic... more
The process of analyzing and organizing e-mail messages is a challenging application of Web and Text mining techniques. In recent years, the increasing popularity of the Web as a mean for sharing information has generated a huge traffic of e-mail messages in various forms. ...
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... Fosca Giannotti 1, Giuseppe Manco 1, Mirco Nanni 2 and Dino Pedreschi 2 ... There-fore, if the base relation major is formed by the tuples {(smith, db), (gray, se)} and the base relation faculty is formed by the tuples {(brown, db),... more
... Fosca Giannotti 1, Giuseppe Manco 1, Mirco Nanni 2 and Dino Pedreschi 2 ... There-fore, if the base relation major is formed by the tuples {(smith, db), (gray, se)} and the base relation faculty is formed by the tuples {(brown, db), (scott, db), (miller, se)}, then there are two possible ...
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... In Proceedings of the Twentieth International Conference on Very Large Databases, pages 487499, Santiago, Chile, 1994. [2] R. Cooley, B. Mobasher, and J. Srivastava. Grouping web page references into transactions for mining world... more
... In Proceedings of the Twentieth International Conference on Very Large Databases, pages 487499, Santiago, Chile, 1994. [2] R. Cooley, B. Mobasher, and J. Srivastava. Grouping web page references into transactions for mining world wide web browsing patterns. In Proc. ...
Research Interests: Computer Science, Data Mining, Service Oriented Architecture, Data Warehousing, Modeling and Simulation, and 15 moreBenchmarking, Decision Trees, Decision Tree, Predictive models, Prediction Model, Association Rules, Design and Development, Knowledge Discovery Process Models, Association Rule, Data Warehouses, Proxy Server, Internet, Data Extraction, Application Software, and Prediction Algorithms
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We present a logic database language with elementary data mining mechanisms to model the relevant aspects of knowledge discovery, and to provide a support for both the iterative and interactive features of the knowledge discovery process.... more
We present a logic database language with elementary data mining mechanisms to model the relevant aspects of knowledge discovery, and to provide a support for both the iterative and interactive features of the knowledge discovery process. We adopt the notion of user-defined aggregate to model typical data mining tasks as operations unveiling unseen knowledge. We illustrate the use of aggregates to model specific data mining tasks, such as frequent pattern discovery, classification, data discretization and clustering, and show how the resulting data mining query language allows the modeling of typical steps of the knowledge discovery process, that range from data preparation to knowledge extraction and evaluation.
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Abstract We recently reported on the identification, over-expression and partial characterisation of a new esterase from the thermoacidophilic eubacterium Bacillus acidocaldarius homologous to the hormone sensitive lipase (HSL)-like... more
Abstract We recently reported on the identification, over-expression and partial characterisation of a new esterase from the thermoacidophilic eubacterium Bacillus acidocaldarius homologous to the hormone sensitive lipase (HSL)-like sub-group of the ...
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We propose a data warehousing architecture for effective risk analysis in a banking scenario. The core of the archi-tecture consists in two data mining tools for improving the quality of consolidated data during the acquisition process.... more
We propose a data warehousing architecture for effective risk analysis in a banking scenario. The core of the archi-tecture consists in two data mining tools for improving the quality of consolidated data during the acquisition process. Specifically, we deal with schema ...
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Abstract We propose a hierarchical, model-based co-clustering framework for handling high-dimensional datasets. The technique views the dataset as a joint probability distribution over row and column variables. Our approach starts by... more
Abstract We propose a hierarchical, model-based co-clustering framework for handling high-dimensional datasets. The technique views the dataset as a joint probability distribution over row and column variables. Our approach starts by clustering tuples in a dataset, where ...
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Research Interests: SEBD and Rule Based
We propose a data warehousing architecture for effective risk analysis in a banking scenario. The core of the archi-tecture consists in two data mining tools for improving the quality of consolidated data during the acquisition process.... more
We propose a data warehousing architecture for effective risk analysis in a banking scenario. The core of the archi-tecture consists in two data mining tools for improving the quality of consolidated data during the acquisition process. Specifically, we deal with schema ...