1. 1 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)

2. 2 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

3. 3 ACMC: Classification 1998: level 1 A. General Literature B. Hardware C. Comp. Sys. Organization D. Software E. Data F. Theory of Computation J D I G H C B EF K A CS G. Mathematics of Computing H. Information Systems I. Computing Methodologies J. Computer Applications K. Computing Milieux

4. 4 ACM Classification 1998: level 2 D. Software –D.0 GENERAL –D.1 PROGRAMMING TECHNIQUES (E)D.1 PROGRAMMING TECHNIQUESE –D.2 SOFTWARE ENGINEERING (K.6.3)D.2 SOFTWARE ENGINEERINGK.6.3 –D.3 PROGRAMMING LANGUAGESD.3 PROGRAMMING LANGUAGES –D.4 OPERATING SYSTEMS (C)D.4 OPERATING SYSTEMSC –D.m MISCELLANEOUS

5. 5 ACM Classification 1998: level 2 H. Information Systems –H.0 GENERAL –H.1 MODELS AND PRINCIPLESH.1 MODELS AND PRINCIPLES –H.2 DATABASE MANAGEMENT (E.5)H.2 DATABASE MANAGEMENTE.5 –H.3 INFORMATION STORAGE AND RETRIEVALH.3 INFORMATION STORAGE AND RETRIEVAL –H.4 INFORMATION SYSTEMS APPLICATIONSH.4 INFORMATION SYSTEMS APPLICATIONS –H.5 INFORMATION INTERFACES AND PRESENTATION (e.g., HCI) (I.7)H.5 INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)I.7 –H.m MISCELLANEOUS

6. 6 ACM Classification 1998: level 2 I. Computing Methodologies –I.0 GENERAL –I.1 SYMBOLIC AND ALGEBRAIC MANIPULATIONI.1 SYMBOLIC AND ALGEBRAIC MANIPULATION –I.2 ARTIFICIAL INTELLIGENCEI.2 ARTIFICIAL INTELLIGENCE –I.3 COMPUTER GRAPHICSI.3 COMPUTER GRAPHICS –I.4 IMAGE PROCESSING AND COMPUTER VISIONI.4 IMAGE PROCESSING AND COMPUTER VISION –I.5 PATTERN RECOGNITIONI.5 PATTERN RECOGNITION –I.6 SIMULATION AND MODELING (G.3)I.6 SIMULATION AND MODELINGG.3 –I.7 DOCUMENT AND TEXT PROCESSING (H.4, H.5)I.7 DOCUMENT AND TEXT PROCESSINGH.4H.5 –I.m MISCELLANEOUS

7. 7 ACM Classification 1998: level 3 I.5 PATTERN RECOGNITION oI.5.0 General oI.5.1 Models oI.5.2 Design Methodology oI.5.3 Clustering oI.5.4 Applications oI.5.5 Implementation (C.3)C.3 oI.5.m Miscellaneous

8. 8 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

9. 9 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

10. 10 Mapping subject cluster to ACMC: structural elements A topic in subject cluster Head subject Gap Offshoot

11. 11 Parsimony: what is better F2 and F4, two head subjects, or F, one head subject (with two more gaps, F1 and F3) F F1 F2 F3 F4

12. 12 GEBKJA E1 E2 E£ E4 E5 G1 G2 G3 G4 K1 K2 K3 K4 K5 K6 K7 K8 HFCD CS I Head subject Subject’s offshoot Gap I1 I2 I3 I4 I5 I6 I7 C. Computer Systems Organization D. Software and H. Information Systems F. Theory of Computation D. Software H. Information Systems I. Computing Methodologies

13. 13 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

14. 14 Three options for getting input data: In-house survey: “Please indicate up to six ACM classification 3d 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)

15. 15 Should be all three - for both developing and mutually testing!