Temporal representation for scientific data provenance

Author

Peng Chen ; Plale, Beth ; Aktas, Mehmet S.

Author_Institution

Sch. of Inf. & Comput., Indiana Univ., Bloomington, IN, USA

fYear

2012

fDate

8-12 Oct. 2012

Firstpage

1

Lastpage

8

Abstract

Provenance of digital scientific data is an important piece of the metadata of a data object. It can however grow voluminous quickly because the granularity level of capture can be high. It can also be quite feature rich. We propose a representation of the provenance data based on logical time that reduces the feature space. Creating time and frequency domain representations of the provenance, we apply clustering, classification and association rule mining to the abstract representations to determine the usefulness of the temporal representation. We evaluate the temporal representation using an existing 10 GB database of provenance captured from a range of scientific workflows.

Keywords

abstract data types; data mining; frequency-domain analysis; meta data; pattern classification; pattern clustering; scientific information systems; temporal databases; time-domain analysis; workflow management software; abstract representation; association rule mining; classification; clustering; digital scientific data provenance; feature space reduction; frequency domain representation; metadata; provenance data representation; provenance database; scientific workflows; temporal representation; time domain representation; Clocks; Clustering algorithms; Data mining; Databases; Frequency domain analysis; Partitioning algorithms; Time domain analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

E-Science (e-Science), 2012 IEEE 8th International Conference on

Conference_Location

Chicago, IL

Print_ISBN

978-1-4673-4467-8

Type

conf

DOI

10.1109/eScience.2012.6404477

Filename

6404477