Matching of Incomplete Service Specifications Exemplified by Privacy Policy Matching

Platenius, Marie Christin; Arifulina, Svetlana; Petrlic, Ronald; Schäfer, Wilhelm

doi:10.1007/978-3-319-14886-1_2

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Similarly, the other parts of the specification (e.g., pre-/postconditions) are matched with respect to the question whether the provided service satisfies the requester's requirements. For more details, please refer to our earlier work, e.g., [9,10].…”

Section: Service Discovery and Matchingmentioning

confidence: 99%

SeSAME

Arifulina

Walther

Becker

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Today, software components are traded on markets in form of services. These services can be service compositions consisting of several services. If a software architect wants to provide such a service composition in the market for trade, she needs to perform several tasks: she needs to model the composition, to discover existing services to be part of that composition, and to analyze the composition's functional correctness as well as its quality, e.g., performance. Up to now, the architect needed to find and use different tools for these tasks. Typically, these tools are not interoperable with each other. We provide the tool SeSAME that supports a software architect in all of these tasks. SeSAME is an integrated Eclipse-based tool-suite providing a comprehensive service specification language to model service compositions and existing services. Furthermore, it includes modules for service matching, functional analysis, and non-functional analysis. SeSAME is the first tool that integrates all these tasks into one tool-suite and, thereby, provides holistic support for trading software services. Thus, it contributes to the acceptance and success of a service market.

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Section: Service Discovery and Matchingmentioning

confidence: 99%

SeSAME

Arifulina

Walther

Becker

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

Self Cite

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“…In (Platenius et al, 2015) incomplete characterization of services is also considered with the aim to determine the extent to which partially complete service specifications for QoS attributes match users' requirements. In this approach, a fuzziness service score is computed, however this is from the users' perspective on the importance of each service QoS attribute combining also incomplete assessments.…”

Section: Uncertainty Due To Partial Service Assessmentsmentioning

confidence: 99%

Handling Uncertainty During Services Ranking

George¹,

Vassilis²

2016

STSC

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Many researchers have suggested fuzzy-based methods to derive rankings of services based on the fuzzy degree that each service satisfies a set of weighted quality attributes. Most of these methods assume a closed set of candidate services completely assessed. However, the candidate service set may include services which have not been fully assessed yet with respect to all quality attributes. Unassessed candidates introduce hesitation regarding the ranking of already evaluated services. In addition, even after services have been completely assessed, users may challenge the alleged assessments by disagreeing with them. Furthermore, users may suggest their preferences for quality attributes of services without providing numbers or ranges of numbers, but rather by providing characterizations of the relative importance of each attribute for their specific purposes. This paper suggests Intuitionistic Fuzzy Sets (IFSs) to handle these sources of uncertainty during services' ranking based on their Quality of Service (QoS) attributes. IFS score functions are used to rank services with regard to each quality attribute. The final ranking can be derived by applying an objective method based on entropy weights for the quality attributes. In addition, users' subjective selection of services based on preferences for the relative importance of quality properties is also discussed.

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How to Complete Customer Requirements

Geierhos

Bäumer

2016

Natural Language Processing and Information Systems

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Matching of Incomplete Service Specifications Exemplified by Privacy Policy Matching

Cited by 6 publications

References 16 publications

SeSAME

SeSAME

Handling Uncertainty During Services Ranking

How to Complete Customer Requirements

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