Learning in Multi-Agent Information Systems - A Survey from IS Perspective

Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area

Teaching Web Information Retrieval and Network Communications Undergraduate Courses in IT Curriculum

This paper provided the results of an investigation on the teaching of computer networks courses and web information retrieval courses in undergraduate Information Technology (IT) curriculum. The paper consists of two parts. First part of the paper surveys the undergraduate courses in two curriculum areas, computer networks and web information retrieval. The content of the survey includes the topics covered, the textbooks used, and major projects in these courses. The second part of the paper describes the authors’ experiences in teaching such courses to undergraduate students in various discipline areas including computer science majors, IT majors, and non-technical majors

Undergraduate Courses in Network Communications and Web Information Retrieval

In this paper we provide the result of an investigation on the teaching of computer networks courses and web information retrieval courses in undergraduate IT curriculum. The paper consists of two parts. First part of the paper surveys the undergraduate courses in two curriculum areas, computer networks and web information retrieval. The content of the survey includes the topics covered, the textbooks used, and major projects in these courses. The second part of the paper describes the authors’ experiences in teaching such courses to undergraduate students in various discipline areas including computer science majors, IT majors, and non-technical majors

Enhanced Integrated Scoring for Cleaning Dirty Texts

An increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evaluations performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98% as compared to 96.5% and 71% based on the use of only basic ISSAC and of Aspell, respectively.Comment: More information is available at http://explorer.csse.uwa.edu.au/reference

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Data Mining and Mining Disasters: Terrorism Information Awareness

This article identifies problems with data mining approaches to antiterrorism and counterterrorism

Data Mining: How Popular Is It?

Data Mining is a process used in the industry, to facilitate decision making. As the name implies, large volumes of data is mined or sifted, to find useful information for decision making. With the advent of E-business, Data Mining has become more important to practitioners. The purpose of this paper is to find out the importance of Data Mining by looking at the different application areas that have used data mining for decision making