It is important task to discover communities or hidden groups by analyzing the messages collected in social networks. For the case when some members of a community are known, a proper method is still necessary to infer the remaining community members. To address such an issue, we develop a closeness centrality examination algorithm to obtain the remaining community members with some known community members. In the proposed model, the message connections among all social network members is captured by a weighted graph model where the edges are assigned with weights derived from the sensitivity of topics contained in the messages by text analysis. In addition, the nodes of known community members form a central sub tree in the weighted graph model. The suspicious priority list of possible community members is obtained by calculating a closeness centrality score to the central sub tree. With the priority list, the remaining community members can be determined using cluster analysis and outlier analysis. The proposed method is validated with experiments.
{"title":"Inferring Community Members in Social Networks by Closeness Centrality Examination","authors":"Jie Zhang, Xuerui Ma, Weihao Liu, Yong Bai","doi":"10.1109/WISA.2012.52","DOIUrl":"https://doi.org/10.1109/WISA.2012.52","url":null,"abstract":"It is important task to discover communities or hidden groups by analyzing the messages collected in social networks. For the case when some members of a community are known, a proper method is still necessary to infer the remaining community members. To address such an issue, we develop a closeness centrality examination algorithm to obtain the remaining community members with some known community members. In the proposed model, the message connections among all social network members is captured by a weighted graph model where the edges are assigned with weights derived from the sensitivity of topics contained in the messages by text analysis. In addition, the nodes of known community members form a central sub tree in the weighted graph model. The suspicious priority list of possible community members is obtained by calculating a closeness centrality score to the central sub tree. With the priority list, the remaining community members can be determined using cluster analysis and outlier analysis. The proposed method is validated with experiments.","PeriodicalId":313228,"journal":{"name":"2012 Ninth Web Information Systems and Applications Conference","volume":"33 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132846473","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}
Software aging is a severe test on the reliability of the software. In this paper, we present a method of nonlinear autoregressive models with exogenous inputs to detect the aging phenomenon of the software system. This method considered the relationship between multivariable and the influence of the delay of historical data. The experimental analysis shows that, using the NARX model to detect fault can be effectively applied in the software aging test.
{"title":"Software Aging Detection Based on NARX Model","authors":"Su Li, Q. Yong","doi":"10.1109/WISA.2012.22","DOIUrl":"https://doi.org/10.1109/WISA.2012.22","url":null,"abstract":"Software aging is a severe test on the reliability of the software. In this paper, we present a method of nonlinear autoregressive models with exogenous inputs to detect the aging phenomenon of the software system. This method considered the relationship between multivariable and the influence of the delay of historical data. The experimental analysis shows that, using the NARX model to detect fault can be effectively applied in the software aging test.","PeriodicalId":313228,"journal":{"name":"2012 Ninth Web Information Systems and Applications Conference","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121508664","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}
With the exponential increment of data, compression technology becomes an important tool in the field of data management, especially in text management. An increasing pressing challenge is how to efficiently query these massive amounts of sequence data in their compressed format. In this paper we study the problem of answering subsequence-search queries on LZ78 format of texts. We propose the concept of conditional common sub strings of queries to improve query performance. We present a techniques to find minimal conditional common sub strings in compressed text and a local uncompressing technique to verify and locate positions of answers in text. Finally, the experimental results over real data demonstrate the efficiency of our algorithm.
{"title":"Answering Multiple Queries in Compressed Texts","authors":"Bin Wang, Minghe Yu, Xiaochun Yang, Guoren Wang","doi":"10.1109/WISA.2012.55","DOIUrl":"https://doi.org/10.1109/WISA.2012.55","url":null,"abstract":"With the exponential increment of data, compression technology becomes an important tool in the field of data management, especially in text management. An increasing pressing challenge is how to efficiently query these massive amounts of sequence data in their compressed format. In this paper we study the problem of answering subsequence-search queries on LZ78 format of texts. We propose the concept of conditional common sub strings of queries to improve query performance. We present a techniques to find minimal conditional common sub strings in compressed text and a local uncompressing technique to verify and locate positions of answers in text. Finally, the experimental results over real data demonstrate the efficiency of our algorithm.","PeriodicalId":313228,"journal":{"name":"2012 Ninth Web Information Systems and Applications Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121761425","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}
With the development of cloud computing technologies, big data processing is becoming more and more important. How to mine and analyze massive data is facing a very big challenge. In this paper, we proposed an efficient massive data mining and analysis framework Data Cloud on large clusters. The most important part of Data Cloud is the Rabbit. It is a kind of massive data mining and analysis processing plan framework on the large clusters like the Pig and Hive. We make a detail analysis about the Rabbit plan.
{"title":"DataCloud: An Efficient Massive Data Mining and Analysis Framework on Large Clusters","authors":"Guigang Zhang, C. Li, Yong Zhang, Chunxiao Xing","doi":"10.1109/WISA.2012.26","DOIUrl":"https://doi.org/10.1109/WISA.2012.26","url":null,"abstract":"With the development of cloud computing technologies, big data processing is becoming more and more important. How to mine and analyze massive data is facing a very big challenge. In this paper, we proposed an efficient massive data mining and analysis framework Data Cloud on large clusters. The most important part of Data Cloud is the Rabbit. It is a kind of massive data mining and analysis processing plan framework on the large clusters like the Pig and Hive. We make a detail analysis about the Rabbit plan.","PeriodicalId":313228,"journal":{"name":"2012 Ninth Web Information Systems and Applications Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121400993","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}
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