How ACM classification can be used for profiling a University CS department

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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)

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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

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ACMC Classification 1998 level 1

• G. Mathematics of Computing
• H. Information Systems
• I. Computing Methodologies
• J. Computer Applications
• K. Computing Milieux

• A. General Literature
• B. Hardware
• C. Comp. Sys. Organization
• D. Software
• E. Data
• F. Theory of Computation

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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

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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

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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

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ACM Classification 1998 level 3

• I.5 PATTERN RECOGNITION
• 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

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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

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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

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Mapping subject cluster to ACMC structural
elements

• A topic in subject cluster
• Head subject
• Gap
• Offshoot

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Parsimony what is better

• F2 and F4, two head subjects, or
• F, one head subject (with two more gaps, F1 and
  F3)

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C. Computer Systems Organization
D. Software and H. Information Systems
F. Theory of Computation D. Software
H. Information Systems
I. Computing Methodologies
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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

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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)

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