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Work as a Service

  • Daniel V. Oppenheim
  • Lav R. Varshney
  • Yi-Min Chee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)

Abstract

Improving work within and among enterprises is of pressing importance. We take a services-oriented view of both doing and coordinating work by treating work as a service. We discuss how large work engagements can be decomposed into a set of smaller interconnected service requests and conversely how larger engagements can be built up from smaller ones. Encapsulating units of work into service requests enables assignment to any organization qualified to service the work, and naturally lends itself to ongoing optimization of the overall engagement.

A service request contains two distinct parts: coordination information for coordinating work and payload information for doing work. Coordination information deals with business concerns such as risk, cost, schedule, and value co-creation. On the other hand, payload information defines the deliverables and provides what is needed to do the work, such as designs or use-cases. This general two-part decomposition leads to a paradigm of work as a two-way information flow between service systems, rather than as a business process that needs to be implemented or integrated between two organizations.

Treating work as information flow allows us to leverage extant understanding of information systems and facilitates information technology support for work using mainstream service-oriented architectures (SOA). Significant benefits from this approach include agility in setting up large engagements to be carried out by distributed organizations, visibility into operations without violating providers’ privacy or requiring changes to internal processes, responsiveness to unpredictability and change, and ongoing optimizations over competing business objectives.

Keywords

Work encapsulation service decoupling information flow 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel V. Oppenheim
    • 1
  • Lav R. Varshney
    • 1
  • Yi-Min Chee
    • 1
  1. 1.IBM Thomas J. Watson Research CenterHawthorneUSA

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