Introduction to the Fifth Workshop on Non-Functional Properties and Service Level Agreements Management in Service-Oriented Computing (NFPSLAM-SOC 2011)

  • Flavio De Paoli
  • Ioan Toma
  • Carlos Pedrinaci
  • Marcel Tilly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7221)


Nowadays businesses as well as the Web require information to be available in real-time in order to reply to requests, make effective decisions and generally remain competitive. This in turn requires data to be processed in realtime. In general in service-oriented architecture (SOA) one is less concerned with latency in data processing. Clearly, there are investigations of service-level agreements (SLA) and quality of service (QoS) to guarantee service delivery. Research around non-functional properties and service-level agreements for serviceoriented computing has reached a level of maturity. There are approaches for describing properties, managing SLAs and even for selecting and composing services based on NFPs. Beyond these classical topics SOA inspired extensions are enabling new and creative domains like the Internet of Things, real-time business or real-time Web. These new domains impose new requirements on SOA, such as a huge data volume, mediation between various data structures and a large number of sources that need to be procured, processed and provided. Questions like how to pick the right service out of tens of thousands of services if we talk about sensor networks or how to provide results with almost near zero-latency describe actual questions and challenges we are currently facing. Therefore, we have to look into new ways for processing data, converting and composing data coming from various sources and for enabling an easy and lightweight way to impose it on various sets of devices.


Service Composition Service Selection Enterprise Application Integration Adaptive Composition Link Data Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Flavio De Paoli
    • 1
  • Ioan Toma
    • 2
  • Carlos Pedrinaci
    • 3
  • Marcel Tilly
    • 4
  1. 1.University of Milano-BicoccaItaly
  2. 2.STI InnsbruckUniversity of InnsbruckAustria
  3. 3.Knowledge Media Institute - The Open UniversityUnited Kingdom
  4. 4.European Microsoft Innovation CenterGermany

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