2004 · 212 citations · 15 references
ComputingComputer ScienceInformation SystemsData AnalyticsData ManagementDatabasesData ArchivingData CleaningData PreprocessingDatabase ManagementDigital ArchivingTransaction Processing SystemsCommitConfiguration Management ProcessElectronic Data ArchivingRevisionsTransaction ProcessingTransaction RecoveryVersioning
Preprocessing directly affects the quality of analysis results. The paper discusses four essential preprocessing tasks—data extraction, transaction recovery, mapping changes to fine‑grained entities, and data cleaning—for fine‑grained CVS archive analysis. The authors formalize sliding‑time windows that link commit mails to revisions and present two approaches that map changes to affected file building blocks such as junctions or sections. DOI 10.1049/ic:20040466, ISBN 0 86341 432 X, Edinburgh 25 May 2004, PDF format, and preprocessing is the common phase of all version‑archive analyses.
DOI: 10.1049/ic:20040466 ISBN: 0 86341 432 X Location: Edinburgh, UK Conference date: 25 May 2004 Format: PDF All analyses of version archives have one phase in common: the preprocessing of data. Preprocessing has a direct impact on the quality of the results returned by an analysis. In this paper we discuss four essential preprocessing tasks necessary for a fine-grained analysis of CVS archives: (a) data extraction, (b) transaction recovery, (c) mapping of changes to fine-grained entities, and (d) data cleaning. We formalize the concept of sliding time windows and show how commit mails can relate revisions to transactions. We also present two approaches that map changes to the affected building blocks of a file, e.g. junctions or sections. Inspec keywords: transaction processing; configuration management; data warehouses; data mining Subjects: Knowledge engineering techniques; Software engineering techniques; Other DBMS
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