1 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 CENTRIA

2 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.

3 ACM-CCS: Classification 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 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

5 Electronic Survey Tool for Data Collection

6 Generic Survey Output: fuzzy memberships over all subjects in 3 rd Layer of ACM-CCS

7 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).

8 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.

9 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. CS

10 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. Lifting a Subject Cluster onto the Ontology

11 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’.

12 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%)

13 Profile of DI-FCT-UNL (2006 Survey) GEBKJA E1 E2 E3 E4 E5 G1 G2 G3 G4 K1 K2 K3 K4 K5 K6 K7 K8 H F C D CS I Head subject Offshoot Gap I1 I2 I3 I4 I5 I6 I7 C. Computer Systems Organization F. Theory of Computation D. Software I. Computing Methodologies D. Software and H. Information Systems H. Information Systems D. Software and H. Information Systems

14 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 department’s 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. GEBKJA H F C D CS I