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How ACM classification can be used for profiling a University CS department Boris Mirkin, 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 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. 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. 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 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 – – – – – 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. 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 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, 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 • 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 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 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 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 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 Should be all three - for both developing and mutually testing! 15