{"title":"Mining Frequent Differences in File Collections","authors":"S. Chawathe","doi":"10.1109/IRI49571.2020.00058","DOIUrl":null,"url":null,"abstract":"Collections of textual files, or documents, with substantial inter-document similarities are common in diverse domains. A practically significant class of such similarities, and the dual differences, are well characterized by edit scripts, or colloquially diffs, that use a simple sequence model for documents. The study of such diffs provides valuable insights into the inter-document relationships within a collection and can guide data integration within and across collections. This paper describes a framework for such study that is based on frequently occurring inter-document differences. It motivates and defines a general problem of mining frequent differences and outlines some specific instances. It presents the design and implementation of a prototype system for interactively discovering and visualizing frequent differences. A notable feature of this method is its use of difference-components, or deltas, to bootstrap the discovery of interesting structure in file collections. The paper describes a preliminary experimental evaluation of the method and implementation on a widely used corpus of file-collections.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"8 1","pages":"357-364"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Collections of textual files, or documents, with substantial inter-document similarities are common in diverse domains. A practically significant class of such similarities, and the dual differences, are well characterized by edit scripts, or colloquially diffs, that use a simple sequence model for documents. The study of such diffs provides valuable insights into the inter-document relationships within a collection and can guide data integration within and across collections. This paper describes a framework for such study that is based on frequently occurring inter-document differences. It motivates and defines a general problem of mining frequent differences and outlines some specific instances. It presents the design and implementation of a prototype system for interactively discovering and visualizing frequent differences. A notable feature of this method is its use of difference-components, or deltas, to bootstrap the discovery of interesting structure in file collections. The paper describes a preliminary experimental evaluation of the method and implementation on a widely used corpus of file-collections.