How ACM classification can be used for profiling a University CS department Boris Mirkin, SCSIS Birkbeck, London Joint work with Susana Nascimento and Luis Moniz Pereira (Universidad Nova, Lisbon, Portugal) 1
Motivation: an Objective Portrayal of Organisation as a Whole • Overview the structure of scientific subjects being developed in organisation Position the organisation over ACMC Asses scientific subjects not fitting well to ACMC • • • these can be potentially points of growth Plan research restructuring and investment Overview scientific field being developed in a country/territory – With quantitative assessment of controversial areas: • • the level of activity is not sufficient the level of activities by far excesses the level of results 2
ACMC: Classification 1998: level 1 • • • A. General Literature B. Hardware C. Comp. Sys. Organization D. Software E. Data F. Theory of Computation A B C • G. Mathematics of Computing • H. Information Systems • I. Computing Methodologies • J. Computer Applications • K. Computing Milieux E D F CS G H I J K 3
ACM Classification 1998: level 2 • D. Software – D. 0 GENERAL – D. 1 PROGRAMMING TECHNIQUES (E) – D. 2 SOFTWARE ENGINEERING (K. 6. 3) – D. 3 PROGRAMMING LANGUAGES – D. 4 OPERATING SYSTEMS (C) – D. m MISCELLANEOUS 4
ACM Classification 1998: level 2 • H. Information Systems – – – H. 0 GENERAL H. 1 MODELS AND PRINCIPLES H. 2 DATABASE MANAGEMENT (E. 5) H. 3 INFORMATION STORAGE AND RETRIEVAL H. 4 INFORMATION SYSTEMS APPLICATIONS H. 5 INFORMATION INTERFACES AND PRESENTATION (e. g. , HCI) (I. 7) – H. m MISCELLANEOUS 5
ACM Classification 1998: level 2 • I. Computing Methodologies – – – – – I. 0 GENERAL I. 1 SYMBOLIC AND ALGEBRAIC MANIPULATION I. 2 ARTIFICIAL INTELLIGENCE I. 3 COMPUTER GRAPHICS I. 4 IMAGE PROCESSING AND COMPUTER VISION I. 5 PATTERN RECOGNITION I. 6 SIMULATION AND MODELING (G. 3) I. 7 DOCUMENT AND TEXT PROCESSING (H. 4, H. 5) I. m MISCELLANEOUS 6
ACM Classification 1998: level 3 I. 5 PATTERN RECOGNITION o o o o I. 5. 0 General I. 5. 1 Models I. 5. 2 Design Methodology I. 5. 3 Clustering I. 5. 4 Applications I. 5. 5 Implementation (C. 3) I. 5. m Miscellaneous 7
Representing research organisation as a set of subject clusters • Input: Set of ACMC research topics assigned with researchers working on them – Similarity between ACMC topics depending on the numbers working on both – Clustering ACMC topics according to the similarity • Clusters may overlap • A robust clustering method (Mirkin 1987) • Output: Set of subject clusters 8
Mapping subject clusters to ACMC: good and bad cases • Navy cluster is tight, all topics are in one ACMC category • Red cluster is dispersed over many ACMC categories CS 9
Mapping subject cluster to ACMC: structural elements • A topic in subject cluster • Head subject • Gap • Offshoot 10
Parsimony: what is better • F 2 and F 4, two head subjects, or • F, one head subject (with two more gaps, F 1 and F 3) F F 1 F 2 F 3 F 4 11
C. Computer Systems Organization D. Software and H. Information Systems F. Theory of Computation D. Software H. Information Systems I. Computing Methodologies E 1 E 2 E£ E 4 E 5 A G 1 G 2 G 3 G 4 E B G K 1 K 2 K 3 K 4 K 5 K 6 K 7 K 8 J K Head subject I Subject’s offshoot CS Gap C I 1 I 2 I 3 I 4 D F I 5 I 6 I 7 H 12
Steps: • Getting members’ ACMC subjects, possibly along with the degree of success achieved • Evaluating similarity between ACM subjects and clustering them; • Parsimoniously mapping clusters to ACMC • aggregating profiles from different clusters and, potentially, different organisations on ACMC; • interpretation of the results 13
Three options for getting input data: • In-house survey: “Please indicate up to six ACM classification 3 d level topics you work on” (supplemented with the order, period and success attribute) • RAE research CVs (needs text analyser + ACMC matching device) • Advanced Knowledge Technologies (AKT, N. Shadbolt 2003) or AKT-like system for collecting and analysing web resources (needs an ACMC matching device) 14
Should be all three - for both developing and mutually testing! 15
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