Representing a Computer Science Research Organization on the ACM Computing Classification System

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Representing a Computer Science Research
Organization on the ACM Computing Classification
System
Boris Mirkin School of Computer Science and
Information Systems , Birkbeck College,
University of London, United Kingdom Susana
Nascimento and Luís Moniz Pereira Computer
Science Department and Centre for Artificial
Intelligence (CENTRIA) Faculdade de Ciências e
Tecnologia Universidade Nova de Lisboa Portugal
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Motivation an Objective Portrayal of Research
Organisation as a Whole

• Overview the structure of scientific subjects
  being developed in the organisation.
• Position the organisation over the ACM-CCS
  ontology.
• Assessing scientific subjects not fitting well to
  ACM-CCS
• these are potentially the growth points or other
  breaking through developments.
• Planning research restructuring and investment.
• Overview of scientific field being developed in a
  country, with a quantitative assessment of
  controversial areas
• e.g. the level of activity is not sufficient or
  the level of activities excesses the level of
  results.

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ACM-CCS 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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Cluster-Lift Method

• Express Research Activities of CS Organization
  (RAO) as a set of CLUSTERS of ACM-CCS Subjects
• Captures RAO in a straightforward way
• No information away about individual members or
  teams
• Can be implemented on different levels of the
  taxonomy
• Needs good clustering tecniques
• MAP individual clusters to ACM-CCS and GENERALISE
  them
• A new approach
• Extendable to other ontologies and activities

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Electronic Survey Tool for Data Collection
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Generic Survey Output fuzzy memberships over
all subjects in 3rd Layer of ACM-CCS
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Fuzzy Similarity between ACM-CCS Subjects

• Contribution by a respondent
• f(i) membership vector over all subjects i in
  3rd layer of ACM-CCS from the survey.
• A(i,j)f(i)?f(j), the product, for all ACM-CCS
  3rd layer subjects i and j.
• Matrices A(i,j) summed up over all individuals
  weighted according to their span ranges.
• Fuzzy similarity measure between two ACM-CCS
  subjects
• measure is proportional to the number and
  importance of research activitives in both
  subjects (details can be presented).

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Bulding Overlapping Subject Clusters

• Additive Clustering with Iterative Extraction
  (ADDI-S)
• Given the similarity matrix, the additive
  clustering problem is of finding one-by-one of K
  clusters and their intensity weights that
  minimize the sum of squared errors.
• Interpretable parameters of cluster intensity and
  its contribution to the explanation of the data
  scatter.
• Leads to tight clusters
• A subject i belongs to a cluster S in case its
  similarity is higher than half of the average
  similarity within the cluster S
• Subject i is also well separated from the rest,
  because for each entity j ? S, its average
  similarity with S is less than that.
• Computationally feasible.

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Generalising Subject Clusters mapped onto
ACM-CCS good and bad cases

• Blue cluster is tight, all topics are in one
  ACM-CCS subject.
• Red cluster is dispersed over many ACM-CCS
  subjects.

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Lifting a Subject Cluster onto the Ontology

• Elementary Structures
• The set of subject clusters, their head
  subjects, gaps and offshoots constitutes
  what can be called the profile of the
  organization under study.
• The total count of head subjects, gaps, and
  offshoots, each type weighted accordingly, can
  be used for scoring the extent of the fit between
  a research grouping and the ontology.

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Parsimonious Lifting of Subject Cluster onto
ACM-CCS

• Plural Solutions which one is better?
• Mapping (B) is better than (A) if gaps are much
  cheaper than additional head subjects.

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Real Case Study 2006 Survey of CS of
FCT-Universidade Nova de Lisboa

• Survey conducted in our Department in 2006
• Participation 30 individuals
• Each one supplied three ACM-CCS 2nd level topics
• 26 of 59 topics at ACM-CCS 2nd level are covered
• Additive clustering algorithm ADDI-S
• Six subject clusters found
• cl1 F1, F3, F4, D3 (contribution 27.08)
• cl2 C2, D1, D2, D3, D4, F3, F4, H2, H3, H5, I2,
  I6 (contribution 17.34)
• cl3 C2, C3, C4 (contribution 5.13)
• cl4 F4, G1, H2, I2, I3, I4, I5, I6, I7
  (contribution 4.42)
• cl5 E1, F2, H2, H3, H4 (contribution 4.03)
• cl6 C4, D1, D2, D4, K6 (contribution 4.00)

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Profile of DI-FCT-UNL (2006 Survey)
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Analysis

• The most contributing cluster with head subject (
  ) Theory of Computation comprises a very
  tight group
• The next contributing cluster has two head
  subjects ( ) D. Software and H. Information
  Systems, and several offshoots among the other
  head subjects, indicating that this cluster
  should be the structure underlying a certain
  unity of the department
• There are only 3 offshoots outside the
  departments head subjects.
• E1. Data Structures from H. Information
  Systems
• G1. Numerical Analysis from I. Computing
  Methodologies
• K6. Management of Computing and Information
  Systems from D. Software
• as all them seem natural, they potentially could
  be updated in the list of collateral links of the
  ACM ontology.