2011
Journal article
Restricted
Discovering Europeana Users' Search Behavior
Ceccarelli D, Gordea S, Lucchese C, Nardini F M, Perego R Tolomei G
Europeana is a strategic project funded by the European Commission with the goal of making Europe's cultural and scientific heritage accessible to the public. ASSETS is a two-year Best Practice Network co-funded by the CIP PSP Programme to improve performance, accessibility and usability of the Europeana search engine. Here we present a characterization of the Europeana logs by showing statistics on common behavioral patterns of the Europeana users.Source: ERCIM NEWS, vol. 86, pp. 39-40
Ceccarelli D, Gordea S, Lucchese C, Nardini F M, Perego R Tolomei G
Europeana is a strategic project funded by the European Commission with the goal of making Europe's cultural and scientific heritage accessible to the public. ASSETS is a two-year Best Practice Network co-funded by the CIP PSP Programme to improve performance, accessibility and usability of the Europeana search engine. Here we present a characterization of the Europeana logs by showing statistics on common behavioral patterns of the Europeana users.Source: ERCIM NEWS, vol. 86, pp. 39-40
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ercim-news.ercim.eu 
| CNR IRIS | CNR IRIS
2011
Conference article
Restricted
Efficient diversification of search results using query logs
Capannini G, Nardini Fm, Perego R, Silvestri F
We study the problem of diversifying search results by exploiting the knowledge mined from query logs. Our proposal exploits the presence of different ``specializations'' of queries in query logs to detect the submission of ambiguous/faceted queries, and manage them by diversifying the search results returned in order to cover the different possible interpretations of the query. We present an original formulation of the results diversification problem in terms of an objective function to be maximized that admits the finding of an optimal solution in linear time.DOI: 10.1145/1963192.1963202
Metrics:
Capannini G, Nardini Fm, Perego R, Silvestri F
We study the problem of diversifying search results by exploiting the knowledge mined from query logs. Our proposal exploits the presence of different ``specializations'' of queries in query logs to detect the submission of ambiguous/faceted queries, and manage them by diversifying the search results returned in order to cover the different possible interpretations of the query. We present an original formulation of the results diversification problem in terms of an objective function to be maximized that admits the finding of an optimal solution in linear time.DOI: 10.1145/1963192.1963202
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | portal.acm.org
2011
Conference article
Open Access
A search architecture enabling efficient diversification of search results
Capannini G, Nardini Fm, Perego R, Silvestri F
In this paper, we deal with efficiency of the diversification of results returned by Web Search Engines (WSEs). We extend a search architecture based on additive Machine Learned Ranking (MLR) systems with a new module computing the diversity score of each retrieved document. Our proposed solution is designed to be used with other techniques, (e.g. early termination of rank computation, etc.). Furthermore, we use an efficient state-of-the-art diversification approach based on knowledge extracted from query logs, and prove that it can efficiently works in a additive machine learned ranking system, and we study its feasibility.
Capannini G, Nardini Fm, Perego R, Silvestri F
In this paper, we deal with efficiency of the diversification of results returned by Web Search Engines (WSEs). We extend a search architecture based on additive Machine Learned Ranking (MLR) systems with a new module computing the diversity score of each retrieved document. Our proposed solution is designed to be used with other techniques, (e.g. early termination of rank computation, etc.). Furthermore, we use an efficient state-of-the-art diversification approach based on knowledge extracted from query logs, and prove that it can efficiently works in a additive machine learned ranking system, and we study its feasibility.
See at:
CNR IRIS 
| ISTI Repository | www.dcs.gla.ac.uk | CNR IRIS
2011
Journal article
Open Access
Efficient diversification of Web search results
Capannini G, Nardini Fm, Perego R, Silvestri F
In this paper we analyze the efficiency of various search results diversification methods. While ecacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unied framework for studying performance and feasibility of result diversification solutions is thus proposed. First we dene a new methodology for detecting when, and how, query results need to be diversied. To this purpose, we rely on the concept of "query renement" to estimate the probability of a query to be ambiguous. Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three dierent diversification methods: IASelect, xQuAD, and OptSelect. While the rst two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evaluate both the efficiency and the effctiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effctiveness.Source: PROCEEDINGS OF THE VLDB ENDOWMENT, vol. 4 (issue 7), pp. 451-459
DOI: 10.14778/1988776.1988781
DOI: 10.48550/arxiv.1105.4255
Project(s): S-CUBE
Metrics:
Capannini G, Nardini Fm, Perego R, Silvestri F
In this paper we analyze the efficiency of various search results diversification methods. While ecacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unied framework for studying performance and feasibility of result diversification solutions is thus proposed. First we dene a new methodology for detecting when, and how, query results need to be diversied. To this purpose, we rely on the concept of "query renement" to estimate the probability of a query to be ambiguous. Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three dierent diversification methods: IASelect, xQuAD, and OptSelect. While the rst two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evaluate both the efficiency and the effctiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effctiveness.Source: PROCEEDINGS OF THE VLDB ENDOWMENT, vol. 4 (issue 7), pp. 451-459
DOI: 10.14778/1988776.1988781
DOI: 10.48550/arxiv.1105.4255
Project(s): S-CUBE
Metrics:
See at:
arXiv.org e-Print Archive | Proceedings of the VLDB Endowment | Proceedings of the VLDB Endowment | doi.org | CNR IRIS | CNR IRIS | www.scopus.com | www.vldb.org
2011
Conference article
Restricted
Improving Europeana search experience using query logs
Ceccarelli D, Gordea S, Lucchese C, Nardini F M, Tolomei G
Europeana is a long-term project funded by the European Commission with the goal of making Europe's cultural and scientific heritage accessible to the public. Since 2008, about 1500 institutions have contributed to Europeana, enabling people to explore the digital re- sources of Europe's museums, libraries and archives. The huge amount of collected multi-lingual multi-media data is made available today through the Europeana portal, a search engine allowing users to explore such con- tent through textual queries. One of the most important techniques for enhancing users search experience in large information spaces, is the exploitation of the knowledge contained in query logs. In this paper we present a characterization of the Europeana query log, showing statistics on common behavioral patterns of the Europeana users. Our analysis highlights some significative differences between the Europeana query log and the historical data collected by general purpose Web Search Engine logs. In particular, we find out that both query and search session distributions show different behaviors. Finally, we use this information for designing a query recommendation technique having the goal of enhancing the functionality of the Europeana portal.DOI: 10.1007/978-3-642-24469-8_39
Project(s): Advanced Search Services and Enhanced Technological Solutions for the European Digital Library
Metrics:
Ceccarelli D, Gordea S, Lucchese C, Nardini F M, Tolomei G
Europeana is a long-term project funded by the European Commission with the goal of making Europe's cultural and scientific heritage accessible to the public. Since 2008, about 1500 institutions have contributed to Europeana, enabling people to explore the digital re- sources of Europe's museums, libraries and archives. The huge amount of collected multi-lingual multi-media data is made available today through the Europeana portal, a search engine allowing users to explore such con- tent through textual queries. One of the most important techniques for enhancing users search experience in large information spaces, is the exploitation of the knowledge contained in query logs. In this paper we present a characterization of the Europeana query log, showing statistics on common behavioral patterns of the Europeana users. Our analysis highlights some significative differences between the Europeana query log and the historical data collected by general purpose Web Search Engine logs. In particular, we find out that both query and search session distributions show different behaviors. Finally, we use this information for designing a query recommendation technique having the goal of enhancing the functionality of the Europeana portal.DOI: 10.1007/978-3-642-24469-8_39
Project(s): Advanced Search Services and Enhanced Technological Solutions for the European Digital Library
Metrics:
See at:
doi.org | gateway.webofknowledge.com | CNR IRIS | CNR IRIS | www.springerlink.com
2011
Other
Open Access
Mining lifecycle event logs for enhancing service-based applications
Nardini Fm, Tolomei G, Silvestri F, Leitner P, Dustdar S
Service-Oriented Architectures (SOAs), and traditional enterprise systems in general, record a variety of events (e.g., messages being sent and received between service components) to proper log files, i.e., event logs. These files constitute a huge and valuable source of knowledge that may be extracted through data mining techniques. To this end, process mining is increasingly gaining interest across the SOA community. The goal of process mining is to build models without emph{a priori} knowledge, i.e., to discover structured process models derived from specific emph{patterns} that are present in actual traces of service executions recorded in event logs. However, in this work we focus on detecting frequent sequential patterns, thus considering process mining as a specific instance of the more general sequential pattern mining problem. Furthermore, we apply two sequential pattern mining algorithms to a real event log provided by the Vienna Runtime Environment for Service-oriented Computing, i.e., vresco. The obtained results show that we are able to find services that are frequently invoked together within the same sequence. Such knowledge could be useful at design-time, when service-based application developers could be provided with service recommendation tools that are able to predict and thus to suggest next services that should be included in the current service composition.Project(s): S-CUBE
Nardini Fm, Tolomei G, Silvestri F, Leitner P, Dustdar S
Service-Oriented Architectures (SOAs), and traditional enterprise systems in general, record a variety of events (e.g., messages being sent and received between service components) to proper log files, i.e., event logs. These files constitute a huge and valuable source of knowledge that may be extracted through data mining techniques. To this end, process mining is increasingly gaining interest across the SOA community. The goal of process mining is to build models without emph{a priori} knowledge, i.e., to discover structured process models derived from specific emph{patterns} that are present in actual traces of service executions recorded in event logs. However, in this work we focus on detecting frequent sequential patterns, thus considering process mining as a specific instance of the more general sequential pattern mining problem. Furthermore, we apply two sequential pattern mining algorithms to a real event log provided by the Vienna Runtime Environment for Service-oriented Computing, i.e., vresco. The obtained results show that we are able to find services that are frequently invoked together within the same sequence. Such knowledge could be useful at design-time, when service-based application developers could be provided with service recommendation tools that are able to predict and thus to suggest next services that should be included in the current service composition.Project(s): S-CUBE
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2011
Other
Open Access
Query Log Mining to Enhance User Experience in Search Engines
Nardini Fm
The Web is the biggest repository of documents humans have ever built. Even more, it is increasingly growing in size every day. Users rely on Web search engines (WSEs) for finding information on the Web. By submitting a textual query expressing their information need, WSE users obtain a list of documents that are highly relevant to the query. Moreover, WSEs tend to store such huge amount of users activities in query logs. Query log mining is the set of techniques aiming at extracting valuable knowledge from query logs. This knowledge represents one of the most used ways of enhancing the users search experience. According to this vision, in this thesis we firstly prove that the knowledge extracted from query logs suffer aging effects and we thus propose a solution to this phenomenon. Secondly, we propose new algorithms for query recommendation that overcome the aging problem. Moreover, we study new query recommendation techniques for efficiently producing recommendations for rare queries. Finally, we study the problem of diversifying Web search engine results. We define a methodology based on the knowledge derived from query logs for detecting when and how query results need to be diversified and we develop an efficient algorithm for diversifying search results.
Nardini Fm
The Web is the biggest repository of documents humans have ever built. Even more, it is increasingly growing in size every day. Users rely on Web search engines (WSEs) for finding information on the Web. By submitting a textual query expressing their information need, WSE users obtain a list of documents that are highly relevant to the query. Moreover, WSEs tend to store such huge amount of users activities in query logs. Query log mining is the set of techniques aiming at extracting valuable knowledge from query logs. This knowledge represents one of the most used ways of enhancing the users search experience. According to this vision, in this thesis we firstly prove that the knowledge extracted from query logs suffer aging effects and we thus propose a solution to this phenomenon. Secondly, we propose new algorithms for query recommendation that overcome the aging problem. Moreover, we study new query recommendation techniques for efficiently producing recommendations for rare queries. Finally, we study the problem of diversifying Web search engine results. We define a methodology based on the knowledge derived from query logs for detecting when and how query results need to be diversified and we develop an efficient algorithm for diversifying search results.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2011
Other
Open Access
S-CUBE - Knowledge extraction from service usage
Silvestri F, Nardini F M, Tolomei G
Data is everywhere. Computer systems keep track of activities of users in the form of log files. Ranging from system logs on Web servers to logs collected by large-scale service based applications, this type of data represents a goldmine of knowledge that, once extracted, can help the stakeholders of the whole system to understand better if, and how, the application can be improved. To this aim, data mining consist of a set of techniques aiming at extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. With recent tremendous technical advances in processing power, storage capacity, and inter-connectivity of computer technology, data mining is seen as an increasingly important tool by modern business to transform unprecedented quantities of digital data into business intelligence giving an informational advantage. Service-centric systems are said to be flexible and dynamic. To support this flexibility, event processing mechanisms can be used to record which events occur within the system. This includes both basic "service events" (e.g., service is created) and complex events regarding QoS (e.g., average response time of service X has changed) and invocations (e.g., service X has been invoked), supporting complex event processing. Users can subscribe to various events of interest, and get notified either via email or Web service notifications (e.g., WS-Eventing). Such notifications may trigger adaptive behavior (e.g., rebinding to other services). Service Oriented Architectures (SOAs) are thus complex infrastructures consisting of thousands or millions of service interacting together in order to achieve complex operations (tasks). Service invocation logs are file tracing the interactions between services. As in other contexts, data mining techniques can be thus applied in order to derive useful knowledge. Such knowledge can be spent in order to enhance both effectiveness and efficiency of the overall infrastructure. The same approach within other fields like, for example, the Web domain is proven to be effective. The knowledge extracted by means of data mining techniques from query logs (files containing the interactions of the users with the search engine) is the first way a search engine improve its performances in terms of effectiveness and efficiency. In this deliverable we thus investigate how useful knowledge can be extracted from service logs and possible ways of applications within the SOA context. In order to do that as one of the case studies we will use logs from search engines activities to extract knowledge regarding users activity.Project(s): S-CUBE
Silvestri F, Nardini F M, Tolomei G
Data is everywhere. Computer systems keep track of activities of users in the form of log files. Ranging from system logs on Web servers to logs collected by large-scale service based applications, this type of data represents a goldmine of knowledge that, once extracted, can help the stakeholders of the whole system to understand better if, and how, the application can be improved. To this aim, data mining consist of a set of techniques aiming at extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. With recent tremendous technical advances in processing power, storage capacity, and inter-connectivity of computer technology, data mining is seen as an increasingly important tool by modern business to transform unprecedented quantities of digital data into business intelligence giving an informational advantage. Service-centric systems are said to be flexible and dynamic. To support this flexibility, event processing mechanisms can be used to record which events occur within the system. This includes both basic "service events" (e.g., service is created) and complex events regarding QoS (e.g., average response time of service X has changed) and invocations (e.g., service X has been invoked), supporting complex event processing. Users can subscribe to various events of interest, and get notified either via email or Web service notifications (e.g., WS-Eventing). Such notifications may trigger adaptive behavior (e.g., rebinding to other services). Service Oriented Architectures (SOAs) are thus complex infrastructures consisting of thousands or millions of service interacting together in order to achieve complex operations (tasks). Service invocation logs are file tracing the interactions between services. As in other contexts, data mining techniques can be thus applied in order to derive useful knowledge. Such knowledge can be spent in order to enhance both effectiveness and efficiency of the overall infrastructure. The same approach within other fields like, for example, the Web domain is proven to be effective. The knowledge extracted by means of data mining techniques from query logs (files containing the interactions of the users with the search engine) is the first way a search engine improve its performances in terms of effectiveness and efficiency. In this deliverable we thus investigate how useful knowledge can be extracted from service logs and possible ways of applications within the SOA context. In order to do that as one of the case studies we will use logs from search engines activities to extract knowledge regarding users activity.Project(s): S-CUBE
See at:
CNR IRIS | ISTI Repository | CNR IRIS