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Your search keyword '"Mazzeo, Giuseppe M."' showing total 27 results
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27 results on '"Mazzeo, Giuseppe M."'

2. A Probabilistic Framework for Building Privacy-Preserving Synopses of Multi-dimensional Data

Author

Furfaro, Filippo, Mazzeo, Giuseppe M., Saccà, Domenico, 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, Ludäscher, Bertram, editor, and Mamoulis, Nikos, editor

Published
2008
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3. A Probabilistic Approach for Computing Approximate Iceberg Cubes

Author

Cuzzocrea, Alfredo, Furfaro, Filippo, Mazzeo, Giuseppe M., 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, Bhowmick, Sourav S., editor, Küng, Josef, editor, and Wagner, Roland, editor

Published
2008
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4. Exploiting Cluster Analysis for Constructing Multi-dimensional Histograms on Both Static and Evolving Data

Author

Furfaro, Filippo, Mazzeo, Giuseppe M., Sirangelo, Cristina, 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, Ioannidis, Yannis, editor, Scholl, Marc H., editor, Schmidt, Joachim W., editor, Matthes, Florian, editor, Hatzopoulos, Mike, editor, Boehm, Klemens, editor, Kemper, Alfons, editor, Grust, Torsten, editor, and Boehm, Christian, editor

Published
2006
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5. Clustering-Based Histograms for Multi-dimensional Data

Author

Furfaro, Filippo, Mazzeo, Giuseppe M., Sirangelo, Cristina, 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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6. A Grid Framework for Approximate Aggregate Query Answering on Summarized Sensor Network Readings

Author

Cuzzocrea, Alfredo, Furfaro, Filippo, Mazzeo, Giuseppe M., Saccà, Domenico, 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, Meersman, Robert, editor, Tari, Zahir, editor, and Corsaro, Angelo, editor

Published
2004
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15. QA3 : A natural language approach to question answering over RDF data cubes.

Author

Dasiopoulou, Stamatia, Meditskos, Georgios, Wanner, Leo, Vrochidis, Stefanos, Cimiano, Philipp, Atzori, Maurizio, Mazzeo, Giuseppe M., and Zaniolo, Carlo

Subjects
NATURAL languages, KNOWLEDGE base, DATA structures, LANGUAGE arts
Abstract

In this paper we present QA3, a question answering (QA) system over RDF data cubes. The system first tags chunks of text with elements of the knowledge base, and then leverages the well-defined structure of data cubes to create a SPARQL query from the tags. For each class of questions with the same structure a SPARQL template is defined, to be filled in with SPARQL fragments obtained by the interpretation of the question. The correct template is chosen by using an original set of regex-like patterns, based on both syntactical and semantic features of the tokens extracted from the question. Preliminary results obtained using a limited set of templates are encouraging and suggest a number of improvements. QA3 can currently provide a correct answer to 27 of the 50 questions of the test set of the task 3 of QALD-6 challenge, remarkably improving the state of the art in natural language question answering over data cubes. [ABSTRACT FROM AUTHOR]

Published
2019
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16. Privacy or Security?

Author

Fazzinga, Bettina, primary, Furfaro, Filippo, additional, Masciari, Elio, additional, and Mazzeo, Giuseppe M., additional

Published
2016
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17. QA3: A natural language approach to question answering over RDF data cubes

Author

Atzori, Maurizio, Mazzeo, Giuseppe M., and Zaniolo, Carlo

Abstract

In this paper we present QA3, a question answering (QA) system over RDF data cubes. The system first tags chunks of text with elements of the knowledge base, and then leverages the well-defined structure of data cubes to create a SPARQL query from the tags. For each class of questions with the same structure a SPARQL template is defined, to be filled in with SPARQL fragments obtained by the interpretation of the question. The correct template is chosen by using an original set of regex-like patterns, based on both syntactical and semantic features of the tokens extracted from the question. Preliminary results obtained using a limited set of templates are encouraging and suggest a number of improvements. QA3can currently provide a correct answer to 27 of the 50 questions of the test set of the task 3 of QALD-6 challenge, remarkably improving the state of the art in natural language question answering over data cubes.

Published
2019
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23. Exploiting Cluster Analysis for Constructing Multi-dimensional Histograms on Both Static and Evolving Data.

Author

Ioannidis, Yannis, Scholl, Marc H., Schmidt, Joachim W., Matthes, Florian, Hatzopoulos, Mike, Boehm, Klemens, Kemper, Alfons, Grust, Torsten, Boehm, Christian, Furfaro, Filippo, Mazzeo, Giuseppe M., and Sirangelo, Cristina

Abstract

Density-based clusterization techniques are investigated as a basis for constructing histograms in multi-dimensional scenarios, where traditional techniques fail in providing effective data synopses. The main idea is that locating dense and sparse regions can be exploited to partition the data into homogeneous buckets, preventing dense and sparse regions from being summarized into the same aggregate data. The use of clustering techniques to support the histogram construction is investigated in the context of either static and dynamic data, where the use of incremental clustering strategies is mandatory due to the inefficiency of performing the clusterization task from scratch at each data update. [ABSTRACT FROM AUTHOR]

Published
2006
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24. Clustering-Based Histograms for Multi-dimensional Data.

Author

Tjoa, A. Min, Trujillo, Juan, Furfaro, Filippo, Mazzeo, Giuseppe M., and Sirangelo, Cristina

Abstract

A new technique for constructing multi-dimensional histograms is proposed. This technique first invokes a density-based clustering algorithm to locate dense and sparse regions of the input data. Then the data distribution inside each of these regions is summarized by partitioning it into non-overlapping blocks laid onto a grid. The granularity of this grid is chosen depending on the underlying data distribution: the more homogeneous the data, the coarser the grid. Our approach is compared with state-of-the-art histograms on both synthetic and real-life data and is shown to be more effective. [ABSTRACT FROM AUTHOR]

Published
2005
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25. Fast and Effective Big Data Exploration by Clustering

Author

Michele Ianni, Elio Masciari, Giuseppe M. Mazzeo, MarioMezzanzanica CarloZaniolo, Ianni, Michele, Masciari, Elio, Mazzeo, Giuseppe M., and Carlozaniolo, Mariomezzanzanica

Subjects
Big Data, Clustering, Experimental Evaluation
Abstract

The rise of Big Data era calls for more efficient and effective Data Exploration and analysis tools. In this respect, the need to support advanced analytics on Big Data is driving data scientist’ interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion. Experimental results confirm the accuracy and scalability of CLUBS+ on platforms tailored for Big Data management.

Published
2020

26. Efficiently representing and querying sensor network readings on data grids

Author

Cuzzocrea, A., Furfaro, F., Elio Masciari, Mazzeo, G. M., Saccà, D., Cuzzocrea, Alfredo, Furfaro, Filippo, Masciari, Elio, Mazzeo, Giuseppe M., Saccã&nbsp, Domenico, Cuzzocrea, Alfredo Massimiliano, F., Furfaro, E., Masciari, G. M., Mazzeo, and D., Saccà

Subjects
Software
Published
2006

27. Clustering big data

Author

Giuseppe M. Mazzeo, Carlo Zaniolo, Elio Masciari, Michele Ianni, Jorge Bernardino andChristoph Quix, Ianni, Michele, Masciari, Elio, Mazzeo, Giuseppe M., and Zaniolo, Carlo

Subjects
Big Data, Computer science, business.industry, Big data, Data mining, computer.software_genre, Cluster analysis, business, computer, Clustering

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