On quantifying changes in temporally evolving dataset

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On quantifying changes in temporally evolving dataset

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Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents

Napa Valley, California, USA

POSTER SESSION: Poster session 2/knowledge management table of contents

Pages 1459-1460

Year of Publication: 2008

ISBN:978-1-59593-991-3

Authors

Rohan Choudhary	Indian Insitute of Technology, New Delhi, India
Sameep Mehta	IBM India Research Lab, New Delhi, India
Amitabha Bagchi	Indian Insitute of Technology, New Delhi, India

Sponsors

ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval

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ACM New York, NY, USA

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ABSTRACT

In this paper, we present a general framework to quantify changes in temporally evolving data. We focus on changes that materialize due to evolution and interactions of features extracted from the data. The changes are captured by the following key transformations: create, merge, split, continue, and cease. First, we identify various factors which influence the importance of each transformation. These factors are then combined using a weight vector. The weight vector encapsulates domain knowledge. We evaluate our algorithm using the following datasets: DBLP, IMDB, Text and Scientific Dataset.

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Rohan Choudhary, Sameep Mehta, and Amitabha Bagchi. On quantifying changes in temporally evolving dataset. In IBM Research Report-RI08011, 2008.

INDEX TERMS

Primary Classification:
H. Information Systems
H.2 DATABASE MANAGEMENT
H.2.8 Database applications
Subjects: Data mining