Web-KR '12最新文献

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OmpiJava: a tool for development of high-performance reasoning applications for the semantic web OmpiJava:为语义web开发高性能推理应用程序的工具

Web-KR '12 Pub Date : 2012-10-29 DOI: 10.1145/2389656.2389659

A. Cheptsov

{"title":"OmpiJava: a tool for development of high-performance reasoning applications for the semantic web","authors":"A. Cheptsov","doi":"10.1145/2389656.2389659","DOIUrl":"https://doi.org/10.1145/2389656.2389659","url":null,"abstract":"The World Wide Web has naturally been evolving towards processing extra-large data volumes, such as collected by Linked Life Data or Open PHACTS repositories, capable of hosting billions of information entities (e.g., RDF triples used in Semantic Web) and beyond. In view of the explosive data growth along with excessive QoS requirements on scalability and processing time constraints, the Web is expected to dominate the data-centric computing already in the next decade. On the other hand, most of the current HPC infrastructures, both academic and industrial, do not support parallel Web applications, e.g., developed in the Hadoop framework, due to their service-oriented implementation in the Java programming language, which is (and will surely remain) prevalent for the Web programming. As a reaction to novel challenges of promoting data-centric supercomputing to the Web, we present a solution that introduces the Message Passing Interface (MPI) bindings to Java, seamlessly integrated in one of the most popular current MPI implementations - Open MPI. Our implementation enables Java-based Semantic Web applications to be successfully ported to the most of modern HPC systems. We also discuss the design features of Open MPI that enable the proliferation of MPI into Java applications. Finally, we present a pilot Semantic Statistics scenario implemented with MPI, Random Indexing, and discuss future work in terms of promising Semantic Web applications, such as Reasoning.","PeriodicalId":200862,"journal":{"name":"Web-KR '12","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117112875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Efficient mining of correlated sequential patterns based on null hypothesis 基于零假设的关联序列模式高效挖掘

Web-KR '12 Pub Date : 2012-10-29 DOI: 10.1145/2389656.2389660

C. Lin, Ming Ji, Marina Danilevsky, Jiawei Han

{"title":"Efficient mining of correlated sequential patterns based on null hypothesis","authors":"C. Lin, Ming Ji, Marina Danilevsky, Jiawei Han","doi":"10.1145/2389656.2389660","DOIUrl":"https://doi.org/10.1145/2389656.2389660","url":null,"abstract":"Frequent pattern mining has been a widely studied topic in the research area of data mining for more than a decade. However, pattern mining with real data sets is complicated - a huge number of co-occurrence patterns are usually generated, a majority of which are either redundant or uninformative. The true correlation relationships among data objects are buried deep among a large pile of useless information. To overcome this difficulty, mining correlations has been recognized as an important data mining task for its many advantages over mining frequent patterns.\u0000 In this paper, we formally propose and define the task of mining frequent correlated sequential patterns from a sequential database. With this aim in mind, we re-examine various interestingness measures to select the appropriate one(s), which can disclose succinct relationships of sequential patterns. We then propose PSBSpan, an efficient mining algorithm based on the framework of the pattern-growth methodology which mines frequent correlated sequential patterns. Our experimental study on real datasets shows that our algorithm has outstanding performance in terms of both efficiency and effectiveness.","PeriodicalId":200862,"journal":{"name":"Web-KR '12","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122882808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A distributed, semiotic-inductive, and human-oriented approach to web-scale knowledge retrieval 网络规模知识检索的分布式、符号归纳和面向人的方法

Web-KR '12 Pub Date : 2012-10-29 DOI: 10.1145/2389656.2389658

Edy Portmann, M. Kaufmann, C. Graf

引用次数: 17