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Enabling Workflow Composition Within a Social Network Environment

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Business Process Management Workshops (BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

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Abstract

Social networks have emerged as a new paradigm for everyday communication, as well as for promoting collaboration within enterprises and organizations. Using current technology, social network participants not only exchange information but also invoke external applications in the form of gadgets. In order to provide enhanced, complex functionality to participants, enabling them to complete a specific business task, workflow composition based on gadget combination could be supported. In such case, gadgets executed in the context of a user profile may add or update information stored in it, while the profile owner is responsible to combine them. In this paper, we explore how to support such features, based on a recommendation mechanism, which automatically produces gadget composition plans, based on intelligent techniques. This context-driven process provides efficiency in cases when a large number of gadgets is available in a social network. The main contribution of the proposed framework lies in the fact that users are only expected to state the content to be added in their profile; the recommendation mechanism can then automatically discover and combine the appropriate gadgets, proposing a solution; no predefined workflows or any other knowledge of the available gadgets is required. The proposed mechanism supporting gadget composition promotes the adoption of social networks as a novel paradigm not only for content sharing and collaboration but also for business process management within the enterprise.

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Correspondence to Ourania Hatzi .

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Hatzi, O., Topali, D., Nikolaidou, M., Anagnostopoulos, D. (2014). Enabling Workflow Composition Within a Social Network Environment. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-06257-0_8

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  • Print ISBN: 978-3-319-06256-3

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