BibSonomy publications for /user/praveenTue Nov 29 09:08:02 CET 2011Berlin / HeidelbergHigh-Performance Computing and Networking241-250Lecture Notes in Computer SciencePerformance Evaluation of Parallel GroupBy-Before-Join Query Processing in High Performance Database Systems21102001evaluation groupby join parallel performance Strategic decision making process uses a lot of GroupBy clauses and join operations queries. As the source of information in this type of application to these queries is commonly very huge, then parallelization of GroupBy-Join queries is unavoidable in order to speed up query processing time. In this paper, we investigate three parallelization techniques for GroupBy-Join queries, particularly the queries where the group-by clause can be performed before the join operation. We subsequently call this query “GroupBy-Before-Join” queries. Performance evaluation of the three parallel processing methods is also carried out and presented here.Tue Apr 13 12:40:40 CEST 2010sepCEUR-WS.orgECML PKDD Discovery Challenge 2009 (DC09)4972009conference dc09 discovery ecml myown pkdd Wed Jan 13 10:08:44 CET 2010New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulySocial Search and Discovery Using a Unified Approach2009IBM bookmarking ht09 intranet search social tagging web web2.0 This research explores new ways to augment the search and discovery of relations between Web 2.0 entities using multiple types and sources of social information. Our goal is to allow the search for all object types such as documents, persons and tags, while retrieving related objects of all types. We implemented a social-search engine using a unified approach, where the search space is expanded to represent heterogeneous information objects that are interrelated by several relation types. Our solution is based on multifaceted search, which provides an efficient update mechanism for relations between objects, as well as efficient search over the heterogeneous data. We describe a social search engine positioned within a large enterprise, applied over social data gathered from several Web 2.0 applications. We conducted a large user study with over 600 people to evaluate the contribution of social data for search. Our results demonstrate the high precision of social search results and confirm the strong relationship of users and tags to the topics retrieved.Wed Jan 13 10:08:06 CET 2010IBM Journal of Research and Development2159--165The Automatic Creation of Literature Abstracts219581958 IBM abstracts automatic creation literature mining text wismasys0809 Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the "auto-abstract."Sat Jan 09 13:41:13 CET 2010New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008information logdata logsonomy retrieval social Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.Sat Nov 14 11:54:45 CET 2009Arxiv preprint arXiv:0904.1113{k-Means has Polynomial Smoothed Complexity}2009complexity k-means Sun Nov 01 13:19:32 CET 2009JanuaryOWL Web Ontology Language Overview2004www2010-m3o Mon Oct 12 10:07:18 CEST 2009ICSEconf/icse/200912-22How tagging helps bridge the gap between social and technical aspects in software development.2009development social software tagging uni Mon Oct 12 09:54:11 CEST 2009CEUR-WS.org/Vol-405Semantify del.icio.us: Automatically Turn your Tags into Senses2008analysis disambiguation social tagging At present tagging is experimenting a great diffusion as the most adopted way to collaboratively classify resources over the Web. In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data. It allows to easily semantify the tags of the users of a tagging service: it automatically finds out for each tag the related concept of Wikipedia in order to describe Web resources through senses. On the basis of a set of evaluation tests, we analyze all the advantages of our sense-based way of tagging, proposing new methods to keep the set of users tags more consistent or to classify the tagged resources on the basis of Wikipedia categories, YAGO classes or Wordnet synsets. We discuss also how our semanitified social tagging data are strongly linked to DBPedia and the datasets of the Linked Data community. Sat Oct 10 13:33:15 CEST 2009Bled, SloveniaProceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)SeptemberCharacterizing Semantic Relatedness of Search Query Terms20092009 WebMining analysis ecml_pkdd ein logsonomies myown query relatedness search semantic similarity_measures workshop Thu Oct 01 14:41:54 CEST 2009Semantic Computing, 2008 IEEE International Conference onAug.50-57Exploiting Semantic Query Context to Improve Search Ranking2008data log query search semantic One challenge for relevance ranking in Web search is underspecified queries. For such queries, top-ranked documents may contain information irrelevant to the search goal of the user; some newly-created relevant documents are ranked lower due to their freshness and to the large number of existing documents that match the queries. To improve the relevance ranking for underspecified queries requires better understanding of users' search goals. By analyzing the semantic query context extracted from the query logs, we propose Q-Rank to effectively improve the ranking of search results for a given query. Experiments show that Q-Rank outperforms the current ranking system of a large-scale commercial Web search engine, improving the relevance ranking for 82% of the queries with an average increase of 8.99% in terms of discounted cumulative gains. Because Q-Rank is independent of the underlying ranking algorithm, it can be integrated with existing search engines.Mon Jun 29 17:41:53 CEST 2009Los Alamitos, CA, USAInformation Technology: New Generations, Sixth International Conference on149-153A New Algorithm for Frequent Itemset Generation in Non-Binary Search Space020092009 Praveen algorithm frequent generation itemset non_binary search_space Wed Jun 24 15:51:36 CEST 2009SPIREconf/spire/2008188-199Lecture Notes in Computer ScienceClique Analysis of Query Log Graphs.52802008analysis clique graphs log mining query semantics Mon Jun 22 10:14:48 CEST 2009New York, NY, USASIGMOD Rec.154--59Semantic integration of semistructured and structured data sources281999data integration query semantic semistructured structured Providing an integrated access to multiple heterogeneous sources is a challenging issue in global information systems for cooperation and interoperability. In this context, two fundamental problems arise. First, how to determine if the sources contain semantically related information, that is, information related to the same or similar real-world concept(s). Second, how to handle semantic heterogeneity to support integration and uniform query interfaces. Complicating factors with respect to conventional view integration techniques are related to the fact that the sources to be integrated already exist and that semantic heterogeneity occurs on the large-scale, involving terminology, structure, and context of the involved sources, with respect to geographical, organizational, and functional aspects related to information use. Moreover, to meet the requirements of global, Internet-based information systems, it is important that tools developed for supporting these activities are semi-automatic and scalable as much as possible. The goal of this paper is to describe the MOMIS [4, 5] (Mediator envirOnment for Multiple Information Sources) approach to the integration and query of multiple, heterogeneous information sources, containing structured and semistructured data. MOMIS has been conceived as a joint collaboration between University of Milano and Modena in the framework of the INTERDATA national research project, aiming at providing methods and tools for data management in Internet-based information systems. Like other integration projects [1, 10, 14], MOMIS follows a “semantic approach” to information integration based on the conceptual schema, or metadata, of the information sources, and on the following architectural elements: i) a common object-oriented data model, defined according to the ODLI3 language, to describe source schemas for integration purposes. The data model and ODLI3 have been defined in MOMIS as subset of the ODMG-93 ones, following the proposal for a standard mediator language developed by the I3/POB working group [7]. In addition, ODLI3 introduces new constructors to support the semantic integration process [4, 5]; ii) one or more wrappers, to translate schema descriptions into the common ODLI3 representation; iii) a mediator and a query-processing component, based on two pre-existing tools, namely ARTEMIS [8] and ODB-Tools [3] (available on Internet at http://sparc20.dsi.unimo.it/), to provide an I3 architecture for integration and query optimization. In this paper, we focus on capturing and reasoning about semantic aspects of schema descriptions of heterogeneous information sources for supporting integration and query optimization. Both semistructured and structured data sources are taken into account [5]. A Common Thesaurus is constructed, which has the role of a shared ontology for the information sources. The Common Thesaurus is built by analyzing ODLI3 descriptions of the sources, by exploiting the Description Logics OLCD (Object Language with Complements allowing Descriptive cycles) [2, 6], derived from KL-ONE family [17]. The knowledge in the Common Thesaurus is then exploited for the identification of semantically related information in ODLI3 descriptions of different sources and for their integration at the global level. Mapping rules and integrity constraints are defined at the global level to express the relationships holding between the integrated description and the sources descriptions. ODB-Tools, supporting OLCD and description logic inference techniques, allows the analysis of sources descriptions for generating a consistent Common Thesaurus and provides support for semantic optimization of queries at the global level, based on defined mapping rules and integrity constraints.Mon Jun 22 10:00:22 CEST 2009AI*IA 2003: Advances in Artificial Intelligence237--249Preprocessing and Mining Web Log Data for Web Personalization2003data log mining preprocessing web We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site. ER -Fri Jun 19 17:34:06 CEST 2009New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data miningdescription = {Extracting semantic relations from query logs}, location = {San Jose, California, USA}, isbn = {978-1-59593-609-7}, doi = {http://doi.acm.org/10.1145/1281192.1281204}76--85Extracting semantic relations from query logs20072007 logd query relation search semantic In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We rst propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.Fri Jun 19 17:23:34 CEST 20096th Annual European Semantic Web Conference (ESWC2009)June669-683Reducing Ambiguity in Tagging Systems with Folksonomy Search Expansion2009Multimedia Search_Engine Semantic_Web Visualization ontology search semantic_web web_2.0 Search facilities are vital both within folksonomy (or social tagging mechanism) based systems and across folksonomy based systems. Although these systems allow great malleability and adaptability, they also surfer from problems, such as ambiguity in the meaning of tags, flat organisation of tags and some degree of unstabilising factor on consensus about which tags best describe some certain Web resources. It has been argued that folksonomy structure can be enhanced by ontologies; however, as suggested by Hotho et al., a key question remains open: how to exploit the benefits of ontologies without bothering untrained users with its rigidity. In this paper, we propose an approach to address the problem of ambiguity in tagging systems by expanding folksonomy search with ontologies, which is completely transparent to users. Preliminary implementations and evaluations on the efficiency and the usefulness of such expansions are very promising.Fri Jun 19 13:25:28 CEST 2009New York, NY, USAWSCD '09: Proceedings of the 2009 workshop on Web Search Click Data8--14Analysis of long queries in a large scale search log2009analysis imported large long queries scale Fri Jun 19 12:02:49 CEST 2009Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search49562008bookmarking comparison search social traditional Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.Fri May 15 13:50:22 CEST 2009Washington, DC, USASCAM '07: Proceedings of the Seventh IEEE International Working Conference on Source Code Analysis and Manipulation193--202The Programmer's Lexicon, Volume I: The Verbs2007dictionary identifiers vocabulary Method names make or break abstractions: good ones communicate the intention of the method, whereas bad ones cause confusion and frustration. The task of naming is subject to the whims and idiosyncracies of the individual since programmers have little to guide them except their personal experience. By analysing method implementations taken from a corpus of Java applications, we establish the meaning of verbs in method names based on actual use. The result is an automatically generated, domain-neutral lexicon of verbs, similar to a natural language dictionary, that represents the common usages of many programmers.