Navigational patterns have applications in several areas including: web personalization, recommendation, userprofiling and clustering, etc. Most existing works on navigational pattern-discovery give little consideration to the effects of time (or temporal trends) on navigational patterns. Some recent works have proposed frameworks for partial temporal representation of navigational patterns. This paper proposes a framework that models navigational patterns as full temporal objects that may be represented as time series. Such a representation allows a rich array of analysis techniques to be applied to the data. The proposed framework also enhances the understanding and interpretation of discovered patterns, and provides a rich environment for integrating the analysis of navigational patterns with data from the underlying organizational environments and other external factors. Such integrated analysis is very helpful in understanding navigational patterns (e.g., E-commerce sites may integrate the trend analysis of navigational patterns with other market data and economic indicators). To achieve full temporal representation, this paper proposes a navigational pattern-discovery technique that is not based on pre-defined thresholds. This is a shift from existing techniques that are driven by pre-defined thresholds that can only support partial temporal representation of navigational patterns.
Communities of practice (CoPs) may be described as groups whose members regularly engage in sharing and learning, based on common interests (Lesser & Storck, 2001). Traditional communities of practice exist within organizations and are centered on work functions. These CoPs may be self-organizing or corporately sponsored. They exist to encourage learning and interaction, create new knowledge, and identify and share best practices for the organization’s processes (Wenger, 1998). The members of a community of practice may be collocated (within an office) or spatially dispersed (e.g., a group may interact via electronic chat). There may also be communities of practice that are not centered on work functions. For example, several online groups exist for enthusiasts of new technology, politics, environment, and so forth. These groups qualify as bona fide CoPs. We classify the CoPs discussed so far as active communities of practice because the members actively seek to learn and share from each other. In this work, however, we examine passive communities of practice in which the members do not actively interact with each other. This class of CoPs shares the core characteristics of traditional communities of practice—the members can learn from each other, and the organization can gain useful knowledge capital and best practices. Our discussions will be based on user communities using cable-TV viewers as a case in point. In contrast to work-centered CoPs whose members share knowledge and learn how to perform their work tasks better, members of user-centered CoPs learn how to maximize the utility from the product/service of interest. In both cases, a learning organization can extract useful knowledge capital and best practices to improve its processes and products/services. In this work, we use the case of cable-TV viewers to show how useful knowledge can be learned and shared in passive user-centered communities of practice. Our techniques will be based on data mining and knowledge discovery, which are introduced in the subsection that follows.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.