B.E. (Honors), Electronics Engineering, Birla Institute of Technology and Science, India, 1980
M.Tech., Computer Technology, Indian Institute of Technology Delhi, India, 1982
Ph.D., Computer Science, Iowa State University, 1988
Deductive databases and logic programming, data modeling, knowledge engineering, Semantic Web, bioinformatics, geoinformatics
Professor of Computer Science. His research expertise is in the areas of Databases, Data Mining, Big Data, Knowledge Representation, Semantic Web, and Reasoning with Incomplete and Inconsistent Information. He has published more than 150 research articles in these areas in leading computer science journals and conference proceedings and is the author of a popular textbook “Oracle 10g Programming: A Primer”, published by Pearson in 2007. His current research includes scalable graph storage system for big graph data, graph pattern mining, reasoning with incomplete and inconsistent information in Description Logics and Logic Programs, and machine learning/deep learning in image classification. Sunderraman received the B.E. (Honors) Electronics Engineering degree from Birla Institute of Technology and Science, Pilani, India in 1980, the M.Tech. Computer Engineering degree from Indian Institute of Technology, Delhi, India in 1982, and the Ph.D. degree in Computer Science from Iowa State University, Ames, Iowa in 1988.
Incomplete and inconsistent databases: This research deals with methods to effectively represent and query various kinds of incompleteness and inconsistencies in relational databases. In early research, pioneering work was done to represent and query relational database with disjunctive information. Later, for the first time, a data model and relational algebra was introduced to represent and query relational databases under the open world assumption with explicit negative data. More recently, data models for degrees of exclusive disjunctions as well as generalized disjunctions in paraconsistent databases have been developed.
Negation in deductive databases and logic programming: The well-founded semantics is widely accepted as the meaning assigned to logic programs and deductive databases with arbitrary negation in the body of rules. In this research, we have used the paraconsistent data model and algebra in a bottom-up approach to compute the well-founded model. Motivated by the bottom-up computation of the well-founded model, we are currently proposing program transformation methods that transform deductive databases with arbitrary negation into well-founded model equivalent deductive databases that do not have negation. For each predicate in the input database, the transformation introduces two predicates, one to keep track of all positive consequences and the other the negative consequences of the well-founded model.
Modeling and querying graph data: In this research, we have proposed a data model and query language to represent and query graph data at the conceptual level. Graph data are increasingly found in many domains including life sciences, social networks, and digital libraries. The data model extends a traditional object-oriented data model by introducing graph-specific objects and the query language provides querying capabilities with respect to the nodes, edges, paths, and sub-graph patterns. An prototype implementation is in progress and performance evaluation is in the planning stage.
Bioinformatics/neuroinformatics: Research in bioinformatics involves the invention as well as application of database and knowledge-base technologies in the life science domain. One of the projects involves the design and implementation of a knowledge-base to catalog neuronal circuitry. NeuronBank is a web-based tool that we have developed for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. In another project, we have developed a programming environment to store, query, and manipulate protein structure data. The structure data from the Protein Data Bank (PDB) is imported into an object-oriented database; a middleware system allows life scientists to work with protein structure data without having to learn much of the computer representation of the data.
Incomplete and inconsistent databases:
N. Viswanath and R. Sunderraman, Degrees of exclusivity in disjunctive databases, Proceedings of the 17th International Symposium on Methodologies for Intelligent Systems (ISMIS 2008), published as Foundations of Intelligent Systems (A. An, S. Matwin, Z. W. Ras and D. Slezak, eds.), Lecture Notes in Artificial Intelligence, vol. 4994, Springer-Verlag, pp. 375–380.
N. Viswanath and R. Sunderraman, Handling disjunctions in open world relational databases, Annual Meeting of the North American Fuzzy Information Processing Society 2008 (NAFIPS 2008), New York, May 2008, pp. 1–6.
R. Bagai and R. Sunderraman, A paraconsistent relational data model, International Journal of Computer Mathematics, vol. 55, no. 1–2, 1995, pp. 39–55.
K.-C. Liu and R. Sunderraman, A generalized relational model for indefinite and maybe information, IEEE Transactions on Knowledge and Data Engineering, vol. 3, no. 1, 1991, pp. 65–77.
K.-C. Liu and R. Sunderraman, Indefinite and maybe information in relational databases, ACM Transactions on Database Systems, vol. 15, no. 1, 1990, pp. 1–39.
Negation in deductive databases and logic programming:
W. Li, M. Fang, and R. Sunderraman, Computing the fitting model for general deductive databases: a program transformation method, submitted to the Annual Meeting of the North American Fuzzy Information Processing Society 2010 (NAFIPS 2010), April 2010.
R. Bagai and R. Sunderraman, Computing the well-founded model of deductive databases, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 4, no. 2, 1996, pp. 157–176.
R. Bagai and R. Sunderraman, A bottom-up approach to compute the fitting model of general deductive databases, Journal of Intelligent Information Systems, vol. 6, no. 1, 1996, pp. 59–75.
Modeling and querying graph data:
H. Yang, R. Sunderraman, and H. Tian, bcnQL: a query language for biochemical networks, International Journal of Data Mining in Bioinformatics, 2010 (to appear).
H. Yang, H. Tian, and R. Sunderraman, A novel query language for querying graph data, Proceedings of the Second International Conference on Information Systems, Technology, and Management (ICISTM), Dubai, March 2008.
P. S. Katz, R. Calin-Jageman, A. Dhawan, C. Frederick, S. Guo, R. Dissanayaka, N. Hiremath, W. Ma, X. Shen, H. C. Wang, H. Yang, S. Prasad, R. Sunderraman, and Y. Zhu, NeuronBank: a tool for cataloging neuronal circuitry, Frontiers in Systems Neuroscience, vol. 4, article 9, 2010.
Y. Wang, R. Sunderraman, and P. Phoungphol, High level programming environment system for protein structure data, Proceedings of the 3rd International Symposium on Bioinformatics Research and Applications (ISBRA 2007), Atlanta, May 2007, Lecture Notes in Bioinformatics, vol. 4463, Springer-Verlag, pp. 215–226.
H. Tian, R. Sunderraman, R. Calin-Jageman, H. Yang, Y. Zhu and P. Katz, NeuroQL: a domain-specific query language for neuroscience data, Proceedings of the 11th International Workshop on Foundations of Models and Languages for Data and Objects: Query Languages and Query Processing (QLQP 2006), Munich, March 2006, Lecture Notes in Computer Science, vol. 4254, Springer-Verlag, pp. 613–624.