On accelerating ultra-large-scale mining

Thumbnail Image
Date
2017-01-01
Authors
Upadhyaya, Ganesha
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Person
Rajan, Hridesh
Professor and Department Chair of Computer Science
Research Projects
Organizational Units
Organizational Unit
Computer Science

Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.

History
The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.

Dates of Existence
1969-present

Related Units

Journal Issue
Is Version Of
Versions
Series
Department
Abstract

Ultra-large-scale mining has been shown to be useful for a number of software engineering tasks e.g. mining specifications, defect prediction. We propose a new research direction for accelerating ultra-large-scale mining that goes beyond parallelization. Our key idea is to analyze the interaction pattern between the mining task and the artifact to cluster artifacts such that running the mining task on one candidate artifact from each cluster is sufficient to produce results for other artifacts in the same cluster. Our artifact clustering criteria go beyond syntactic, semantic, and functional similarities to mining-task-specific similarity, where the interaction pattern between the mining task and the artifact is used for clustering. Our preliminary evaluation demonstrates that our technique significantly reduces the overall mining time.

Comments

This is a manuscript of a proceeding published as Upadhyaya, Ganesha, and Hridesh Rajan. "On accelerating ultra-large-scale mining." In Proceedings of the 39th International Conference on Software Engineering: New Ideas and Emerging Results Track, pp. 39-42. IEEE Press, 2017. doi: 10.1109/ICSE-NIER.2017.11. Posted with permission.

Description
Keywords
Citation
DOI
Copyright
Sun Jan 01 00:00:00 UTC 2017