Abstract:
Despite the rapid growth of text-based computer-mediated communication (CMC), its limitations have rendered the media highly incoherent. This poses problems for content analysis of online discourse archives. Interactional coherence analysis (ICA) attempts to accurately identify and construct CMC interaction networks. In this study, we propose the Hybrid Interactional Coherence (HIC) algorithm for identification of web forum interaction. HIC utilizes a bevy of system and linguistic features, including message header information, quotations, direct address, and lexical relations. Furthermore, several similarity-based methods including a Lexical Match Algorithm (LMA) and a sliding window method are utilized to account for interactional idiosyncrasies. Experiments results on two web forums revealed that the proposed HIC algorithm significantly outperformed comparison techniques in terms of precision, recall, and F-measure at both the forum and thread levels. Additionally, an example was used to illustrate how the improved ICA results can facilitate enhanced social network and role analysis capabilities.
Abstract:
Gene array data have been widely used for cancer diagnosis in recent years. However, high dimensionality has been a major problem for gene array-based classification. Gene selection is critical for accurate classification and for identifying the marker genes to discriminate different tumor types. This paper created a framework of gene selection methods based on previous studies. We focused on optimal search-based gene subset selection methods that evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this study is the first to introduce tabu search to gene selection from high dimensional gene array data. Experimental studies on several gene array datasets demonstrated the effectiveness of optimal search-based gene subset selection to identify marker genes.
Abstract:
Nanotechnology research and applications have experienced rapid growth in recent years. We assessed the status of nanotechnology research worldwide by applying bibliographic, content map, and citation network analysis to a data set of about 200,000 nanotechnology papers published in the Thomson Science Citation Index Expanded database (SCI) from 1976 to 2004. This longitudinal study shows a quasi-exponential growth of nanotechnology articles with an average annual growth rate of 20.7% after 1991. The United States had the largest contribution of nanotechnology research and China and Korea had the fastest growth rates. The largest institutional contributions were from the Chinese Academy of Sciences and the Russian Academy of Sciences. The high-impact papers generally described tools, theories, technologies, perspectives, and overviews of nanotechnology. From the top 20 institutions, based on the average number of paper citations in 1976-2004, 17 were in the Unites States, 2 in France and 1 in Germany. Content map analysis identified the evolution of the major topics researched from 1976 to 2004, including investigative tools, physical phenomena, and experiment environments. Both the country citation network and the institution citation network had relatively high clustering, indicating the existence of citation communities in the two networks, and specific patterns in forming citation communities. The United States, Germany, Japan, and China were major citation centers in nanotechnology research with close inter-citation relationships. © 2008 Springer Science+Business Media B.V.
Abstract:
Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research. © 2013 ACM.
Abstract:
Knowledge management is essential to modern organizations. Due to the information overload problem, managers are facing critical challenges in utilizing the data in organizations. Although several automated tools have been applied, previous applications often deem knowledge items independent and use solely contents, which may limit their analysis abilities. This study focuses on the process of knowledge evolution and proposes to incorporate this perspective into knowledge management tasks. Using a patent classification task as an example, we represent knowledge evolution processes with patent citations and introduce a labeled citation graph kernel to classify patents under a kernel-based machine learning framework. In the experimental study, our proposed approach shows more than 30 percent improvement in classification accuracy compared to traditional content-based methods. The approach can potentially affect the existing patent management procedures. Moreover, this research lends strong support to considering knowledge evolution processes in other knowledge management tasks. © 2009 M.E. Sharpe, Inc.
