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Bridging the Gap Between the Business and Social Worlds: A Data Artifact-Driven Approach

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 10680))

Abstract

The widespread adoption of Web 2.0 applications has forced enterprises to rethink their ways of doing business. To support enterprises in their endeavors, this paper puts forward business-data artifact and social-data artifact to capture, respectively, the intrinsic characteristics of the business world (associated with business process management systems) and social world (associated with Web 2.0 applications), and, also, to make these two worlds work together. While the research community has extensively looked into business-data artifacts, there is a limited knowledge about/interest in social-data artifacts. This paper defines social-data artifact, analyzes the interactions between business- and social-data artifacts, and develops an architecture to support these interactions. For demonstration purposes, an implementation of a socially-flavored faculty-hiring scenario is discussed in the paper. The implementation calls for specialized components known as social machines that support artifact interaction.

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Notes

  1. 1.

    Some authors use the term of Web 2.0 artifact [3].

  2. 2.

    By the end of 2013, Facebook was being used by 1.23 billion users worldwide, adding 170 million in just one year” [20].

  3. 3.

    LinkedIn’s list of attributes that support automatic analysis of members’ profiles is available at developer.linkedin.com/docs/fields. Examples of attributes include industry, location, specialties, and positions.

  4. 4.

    Since processes are usually known for being well-formed, we avoid the term of “social processes” and use “social activities” instead. These latter are generally unstructured and unordered.

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Maamar, Z. et al. (2017). Bridging the Gap Between the Business and Social Worlds: A Data Artifact-Driven Approach. In: Hameurlain, A., Küng, J., Wagner, R., Sakr, S., Razzak, I., Riyad, A. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXV. Lecture Notes in Computer Science(), vol 10680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56121-8_2

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  • DOI: https://doi.org/10.1007/978-3-662-56121-8_2

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