Understanding the semantics of data provenance to support active conceptual modeling

Sudha Ram, Jun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Scopus citations


Data Provenance refers to the lineage of data including its origin, key events that occur over the course of its lifecycle, and other details associated with data creation, processing, and archiving. We believe that tracking provenance enables users to share, discover, and reuse the data, thus streamlining collaborative activities, reducing the possibility of repeating dead ends, and facilitating learning. It also provides a mechanism to transition from static to active conceptual modeling. The primary goal of our research is to investigate the semantics or meaning of data provenance. We describe the W7 model that represents different components of provenance and their relationships to each other. We conceptualize provenance as a combination of seven interconnected elements including "what", "when", "where", "how", "who", "which" and "why". Each of these components may be used to track events that affect data during its lifetime. A homeland security example illustrates how current conceptual models can be extended to embed provenance.

Original languageEnglish (US)
Title of host publicationActive Conceptual Modeling of Learning - Next Generation Learning-Base System Development
Number of pages13
StatePublished - 2008
Event1st International Active Conceptual Modeling of Learning Workshop - Tucson, AZ, United States
Duration: Nov 8 2006Nov 8 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4512 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Active Conceptual Modeling of Learning Workshop
Country/TerritoryUnited States
CityTucson, AZ

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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