TY - GEN
T1 - Understanding the semantics of data provenance to support active conceptual modeling
AU - Ram, Sudha
AU - Liu, Jun
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=49949112653&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-77503-4_3
DO - 10.1007/978-3-540-77503-4_3
M3 - Conference contribution
AN - SCOPUS:49949112653
SN - 3540775021
SN - 9783540775027
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 29
BT - Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development
T2 - 1st International Active Conceptual Modeling of Learning Workshop
Y2 - 8 November 2006 through 8 November 2006
ER -