Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Generating Effective Recommendations Using Viewing-Time Weighted Preferences for Attributes

Recommender systems are an increasingly important technology and researchers have recently argued for incorporating different kinds of data to improve recommendation quality. This paper presents a novel approach to generating recommendations and evaluates its effectiveness. First, we review evidence that item viewing time can reveal user preferences for items. Second, we model item preference as a weighted function of preferences for item attributes. We then propose a method for generating recommendations based on these two propositions. The results of a laboratory evaluation show that the proposed approach generated estimated item ratings consistent with explicit item ratings and assigned high ratings to products that reflect revealed preferences of users. We conclude by discussing implications and identifying areas for future research

CPA\u27s guide to the Internet

https://egrove.olemiss.edu/aicpa_guides/1967/thumbnail.jp

Integration of the Internet in career exploration in education

The basis of this thesis is a discussion of the pros and cons of incorporating the Internet as an educational tool for exploring career pathways

CPA\u27s guide to the Internet

https://egrove.olemiss.edu/aicpa_guides/1966/thumbnail.jp

Volume 17, Number 3, September 1997 OLAC Newsletter

Digitized September 1997 issue of the OLAC Newsletter

Software knowledge management using wikis : a needs and features analysis

Estágio realizado na StrongstepDocumento confidencial. Não pode ser disponibilizado para consultaTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Writing for the Workplace: Business Communication for Professionals

Employers consider communication one of the most critical skills for workers today. Writing for the Workplace: Business Communication for Professionals is an easy- to-follow guide that provides strategies for effective professional communication. Written to address the needs of both students entering the workforce and business professionals looking to improve their written communication, the book offers guides to compose typical workplace documents, from effective e-mails and convincing reports to winning presentations and engaging resumes. This concise book offers busy readers concrete strategies to improve their workplace writing

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss