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Displaying 1-9 out of 9 total
Mining Context-Aware Preferences on Relational and Sensor Data
Found in: Database and Expert Systems Applications, International Workshop on
By Davide Beretta,Elisa Quintarelli,Emanuele Rabosio
Issue Date:September 2011
pp. 116-120
The increasing amount of available digital data motivates the development of techniques for the management of the information overload which risks to actually reduce people's knowledge instead of increasing it. Research is concentrating on topics related t...
Data Mining for XML Query-Answering Support
Found in: IEEE Transactions on Knowledge and Data Engineering
By Mirjana Mazuran,Elisa Quintarelli,Letizia Tanca
Issue Date:August 2012
pp. 1393-1407
Extracting information from semistructured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the Internet grows. Indeed, documents are often so large that the data set returned as...
Anomaly Detection in XML databases by means of Association Rules
Found in: Database and Expert Systems Applications, International Workshop on
By Giulia Bruno, Paolo Garza, Elisa Quintarelli, Rosalba Rossato
Issue Date:September 2007
pp. 387-391
Anomaly detection has the double purpose of discovering interesting exceptions and identifying incorrect data in huge amounts of data. Since anomalies are rare events which violate the frequent relationships among data, we propose a method to detect freque...
Ontology-Based Information Tailoring
Found in: Data Engineering Workshops, 22nd International Conference on
By Carlo Curino, Elisa Quintarelli, Letizia Tanca
Issue Date:April 2006
pp. 5
Current applications are often forced to filter the richness of datasources in order to reduce the information noise the user is subject to. We consider this aspect as a critical issue of applications, to be factorized at the data management level. The Con...
Temporal Aspects of Semistructured Data
Found in: Temporal Representation and Reasoning, International Syposium on
By Barbara Oliboni, Elisa Quintarelli, Letizia Tanca
Issue Date:June 2001
pp. 0119
In many applications information about the history of data and their dynamic aspects are just as important as static information. During the last years the increasing amount of information accessible through the Web has presented new challenges to academic...
ADaPT: Automatic Data Personalization based on contextual preferences
Found in: 2014 IEEE 30th International Conference on Data Engineering (ICDE)
By Antonio Miele,Elisa Quintarelli,Emanuele Rabosio,Letizia Tanca
Issue Date:March 2014
pp. 1234-1237
This demo presents a framework for personalizing data access on the basis of the users' context and of the preferences they show while in that context. The system is composed of (i) a server application, which “tailors” a view over the available data on th...
Mining flexible association rules from XML
Found in: Proceedings of the 2009 EDBT/ICDT Workshops (EDBT/ICDT '09)
By Barbara Oliboni, Elisa Quintarelli, Elisabetta Caneva
Issue Date:March 2009
pp. 85-92
The role of the eXtensible Markup Language (XML) is becoming very important in the research fields focusing on the representation, the exchange, and the integration of information coming from different data sources and containing information related to var...
A methodology for preference-based personalization of contextual data
Found in: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (EDBT '09)
By Antonio Miele, Elisa Quintarelli, Letizia Tanca
Issue Date:March 2009
pp. 94-104
The widespread use of mobile appliances, with limitations in terms of storage, power, and connectivity capability, requires to minimize the amount of data to be loaded on user's devices, in order to quickly select only the information that is really releva...
Graph transformation to infer schemata from XML documents
Found in: Proceedings of the 2005 ACM symposium on Applied computing (SAC '05)
By Elisa Quintarelli, Luciano Baresi
Issue Date:March 2005
pp. 642-646
Semi-structured data are characterized by the lack of a predefined schema. This heterogeneity simplifies the management of such data, but analysis and queries become more difficult and demand for schemata that describe these data. Super-imposed structures ...