Human Factors in the Development of Trend Detection and Tracking Techniques

Human Factors in the Development of Trend Detection and Tracking Techniques

Chaomei Chen (Drexel University, USA), Kaushal Toprani (Drexel University, USA) and Natasha Lobo (Drexel University, USA)
Copyright: © 2006 |Pages: 7
DOI: 10.4018/978-1-59140-562-7.ch043
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Abstract

Trend detection has been studied by researchers in many fields, such as statistics, economy, finance, information science, and computer science (Basseville & Nikiforov, 1993; Chen, 2004; Del Negro, 2001). Trend detection studies can be divided into two broad categories. At technical levels, the focus is on detecting and tracking emerging trends based on dedicated algorithms; at decision making and management levels, the focus is on the process in which algorithmically identified temporal patterns can be translated into elements of a decision making process. Much of the work is concentrated in the first category, primarily focusing on the efficiency and effectiveness from an algorithmic perspective. In contrast, relatively fewer studies in the literature have addressed the role of human perceptual and cognitive system in interpreting and utilizing algorithmically detected trends and changes in their own working environments. In particular, human factors have not been adequately taken into account; trend detection and tracking, especially in text document processing and more recent emerging application areas, has not been studied as integral part of decision-making and related activities. However, rapidly growing technology, and research in the field of human-computer interaction has opened vast and, certainly, thought-provoking possibilities for incorporating usability and heuristic design into the areas of trend detection and tracking.

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