Internet Computing and Information Science Research Group

 

Coordinator: Dr. Kenneth Berman

 

Members:

 

Dr. Fred Annexstein

Dr. Kenneth Berman

Dr. Yizong Cheng

Dr. Jerry Paul

Dr. John Schlipf

 

Major Research Areas:

 

·         Internet and Web Technologies

·         Networks and Graph Theory

·         Parallel, Distributed and Cluster Computing

·         Bioinformatics and Computational Genomics

·         Logic and Semantics

·         Human/User Centric Computing

·         Knowledge Management

·         Algorithms

·         Computer and Information Technology Applications to Education

 

Research Directions and Projects:  

 

  1. Information search and retrieval methods. Determining and discovering the structure of information and the uses of information. Analysis of structural and scalability properties of WWW, Peer-to-peer networks, and other large-scale network structures. Network and web crawling technologies. Methods for utilizing hyperlink digraph structures and semantic information; Clustering and classification; ranking and relevancy of web objects. The Internet and distributed content delivery and retrieval applications; distributed hash tables, graph center problems, server and facility location problems, content caching.

 

  1. Advancing web-based information search, retrieval, and sharing methods for individuals and organizations.  Drs. Berman and Annexstein and their graduate students have developed a new, scalable network crawler capable of quickly determining the network state of large peer-to-peer network applications. The crawler has successfully monitored large networks, often involving thousands of globally distributed hosts. The crawler was designed and developed to harness the power of parallel computing clusters, which is required in dynamic network contexts. Using the network crawler, our research team was one of the first to analyze a large scale P2P application known as Gnutella. In this research we were able to determine the large scale topological structure of this network in a highly dynamic environment.  A beta-version of the network crawling software was released and made publicly available in 2001.

 

A current project is extending the functionality of the network crawler in the following ways to enhance the search and acquisition of knowledge: (1) Extending the functionality of our crawler to diverse network applications, beyond the original Gnutella topology application. (2) Developing crawling technology for diverse Internet content applications including, crawling to analyze the specific contents of peer-to-peer networks, the contents of public web servers, and other accessible databases. (3) Enhancing the crawling technology to achieve a focused network crawler, capable of searching and retrieving focused content sources. Finally, we propose to augment our crawler technology with an associated semantic engine. Such an engine will be programmed to act on semantic metadata, which is required for multimedia searching applications.

 

  1. Integrating new peer-to-peer concepts in the study of knowledge acquisition and management systems. Designing models for trusted peer computing for the goal of developing secure sharing information systems. Investigating reliability and reachability in networks and networks of information with the aim of designing algorithms and analytic models for fault-tolerant routing and searching schemes.  Applications to education and research.

 

  1. Methods for knowledge acquisition and management. Information contexts and application of context for knowledge based systems.  Data exploration and search and retrieval of patterns and exception in large data repositories. Decision support and search strategies for data objects of critical interest. Multi-dimensional data analysis, semantic web, metadata definition, and multimedia object searching. Applications of knowledge management to instruction and distance learning. User-centric knowledge systems.

 

  1. Developing computational theory and software for inferring, analyzing, and utilizing gene networks.  Applying the result to systematic pathway analysis and treatment discovery.

 

  1. Developing models and algorithms for message passing systems and cluster computing.   

 

  1. Designing models and analyzing reliability, routing and security of networks and information.  Designing and analyzing models for fault-tolerant routing schemes such as acorn and directional routing schemes.  Developing a theory of directional compasses and directional embeddings with applications to network fault tolerance and topic-sensitive searching and information retrieval.

 

  1. Graph visualization and drawing.  Mapping out and studying topology of peer-to-peer and small-world networks, such as Gnutella and the Internet.

 

 Graduate Courses:

 

Advanced Algorithms 1 and 2 (20-ECES-781,782)

Internet Algorithms (20-ECES-690)

Graph Theory and Networks (20-ECES-842)

Mathematical Logic I, II (20-ECES-621,624)
Logic in Artificial Intelligence (20-ECES-724)

Computational Genomics (20-ECES-786)