NDLI: Using CiteSeer to analyze trends in the ACM's computing classification system

Content Provider	IEEE Xplore Digital Library
Author	Speretta, M. Gauch, S. Lakkaraju, P.
Copyright Year	2010
Description	Author affiliation: University of Kansas, Lawrence, KS, USA (Lakkaraju, P.) \|\| University of Arkansas, Fayetteville, AR, USA (Speretta, M.; Gauch, S.)
Abstract	Classification systems for research publications are often based on taxonomies. The ACM society for computing and professionals provides a digital library whose cataloguing system is based on a taxonomy that has been continuously updated over the years. The CiteSeer collection contains a large collection of computer science research papers, many of which are tagged with categories from the ACM's taxonomy. By analyzing the small portion of CiteSeer's manually tagged documents and by considering different time frames, we extracted statistics that shows how the ACM's taxonomy covers the publications in computer and information science research sub-fields. We also studied size and growth of categories over the last four available years. These results allow us to reveal areas with higher or lower publication rate. We believe that these techniques could be exploited to quickly identify trends within taxonomies. This would greatly help to construct more efficient browsing and searching systems.
Starting Page	571
Ending Page	577
File Size	388521
Page Count	7
File Format	PDF
ISBN	9781424475605
e-ISBN	9781424475629
DOI	10.1109/HSI.2010.5514510
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-13
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real time systems Sensor phenomena and characterization Classification systems Sensor systems Application software Middleware Wireless communication Wireless sensor networks ACM taxonomy Operating systems Packaging Computer networks CiteSeer digital library
Content Type	Text
Resource Type	Article

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