This paper argues that our service-oriented conception of the enterprise is poised for a disruptive change. This change will leverage innovations in data science to enable us to continually learn and improve models the models that drive services. It will also enable us to manage these service using model-based dashboards, with models serving as both sensors (enabling us to view operational data using model-based abstractions) and effectors (enabling us to specify desired states of affairs in terms of changes to these models, with the associated machinery computing the necessary actions/interventions required to make the operational machinery to conform to these specifications). These changes need to be viewed in the context of a broader shift towards post-theoretic enterprises, where the traditional, largely static bodies of knowledge that have driven the enterprise will be supplanted by a much more fluid (and datadriven) collection of insights.
{"title":"The Post-Theoretic Enterprise: A Service-Oriented View","authors":"A. Ghose, K. Dam","doi":"10.1109/SOCA.2014.59","DOIUrl":"https://doi.org/10.1109/SOCA.2014.59","url":null,"abstract":"This paper argues that our service-oriented conception of the enterprise is poised for a disruptive change. This change will leverage innovations in data science to enable us to continually learn and improve models the models that drive services. It will also enable us to manage these service using model-based dashboards, with models serving as both sensors (enabling us to view operational data using model-based abstractions) and effectors (enabling us to specify desired states of affairs in terms of changes to these models, with the associated machinery computing the necessary actions/interventions required to make the operational machinery to conform to these specifications). These changes need to be viewed in the context of a broader shift towards post-theoretic enterprises, where the traditional, largely static bodies of knowledge that have driven the enterprise will be supplanted by a much more fluid (and datadriven) collection of insights.","PeriodicalId":138805,"journal":{"name":"2014 IEEE 7th International Conference on Service-Oriented Computing and Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129631184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Often, there is a lack of efficient procedures in order to identify design errors when transforming customer requirements into servic-oriented solution. A well-established approach supporting this kind of transformation in classical requirements engineering is the traceability matrix which allows tracing the coverage of customer requirements by system components in general, or services in particular. The matrix shows which services realize which customer requirements. However, the matrix does not show the relevance of a service in order to meet a certain quality aspect (e.g. Security, usability, maintainability, etc.). Since this information is not explicitly covered, it is the system designer who has to keep this knowledge in mind when designing an appropriate set of services that build the overall system. Unfortunately, stakeholders not involved in the design decisions, e.g. Customers, Project managers and developers, have a hard time to understand the significance of the coverage of customer requirements by services in order to meet desired quality aspects. This may cause misinterpretations especially in the early stages of the service system development life-cycle. In this paper, we present a heuristic-based approach to continually monitor and control design decisions and their effects on certain quality aspects. This approach extends the traceability matrix by a weighted decision-matrix.
{"title":"The Weighted Decision Matrix: Tracking Design Decisions in Service Compositions","authors":"Alexandra Mazak, Bernhard Kratzwald","doi":"10.1109/SOCA.2014.55","DOIUrl":"https://doi.org/10.1109/SOCA.2014.55","url":null,"abstract":"Often, there is a lack of efficient procedures in order to identify design errors when transforming customer requirements into servic-oriented solution. A well-established approach supporting this kind of transformation in classical requirements engineering is the traceability matrix which allows tracing the coverage of customer requirements by system components in general, or services in particular. The matrix shows which services realize which customer requirements. However, the matrix does not show the relevance of a service in order to meet a certain quality aspect (e.g. Security, usability, maintainability, etc.). Since this information is not explicitly covered, it is the system designer who has to keep this knowledge in mind when designing an appropriate set of services that build the overall system. Unfortunately, stakeholders not involved in the design decisions, e.g. Customers, Project managers and developers, have a hard time to understand the significance of the coverage of customer requirements by services in order to meet desired quality aspects. This may cause misinterpretations especially in the early stages of the service system development life-cycle. In this paper, we present a heuristic-based approach to continually monitor and control design decisions and their effects on certain quality aspects. This approach extends the traceability matrix by a weighted decision-matrix.","PeriodicalId":138805,"journal":{"name":"2014 IEEE 7th International Conference on Service-Oriented Computing and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129751644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Service-based applications are often developed as compositions of partner services. A service integrator needs precise methods to specify the quality attributes expected by each partner service, as well as effective techniques to verify these attributes. In previous work, we identified the most common specification patterns related to provisioning service-based applications and developed an expressive specification language (SOLOIST) that supports them. SOLOIST is an extension of metric temporal logic with aggregate temporal modalities that can be used to write quantitative temporal properties. In this paper we address the problem of performing offline checking of service execution traces against quantitative requirements specifications written in SOLOIST. We present a translation of SOLOIST into CLTLB (D), a variant of linear temporal logic, and reduce the trace checking of SOLOIST to bounded satisfiability checking of CLTLB (D), which is supported by ZOT, an SMT-based verification toolkit. We detail the results of applying the proposed offline trace checking procedure to different types of traces, and compare its performance with previous work.
{"title":"Offline Trace Checking of Quantitative Properties of Service-Based Applications","authors":"D. Bianculli, C. Ghezzi, S. Krstic, P. S. Pietro","doi":"10.1109/SOCA.2014.14","DOIUrl":"https://doi.org/10.1109/SOCA.2014.14","url":null,"abstract":"Service-based applications are often developed as compositions of partner services. A service integrator needs precise methods to specify the quality attributes expected by each partner service, as well as effective techniques to verify these attributes. In previous work, we identified the most common specification patterns related to provisioning service-based applications and developed an expressive specification language (SOLOIST) that supports them. SOLOIST is an extension of metric temporal logic with aggregate temporal modalities that can be used to write quantitative temporal properties. In this paper we address the problem of performing offline checking of service execution traces against quantitative requirements specifications written in SOLOIST. We present a translation of SOLOIST into CLTLB (D), a variant of linear temporal logic, and reduce the trace checking of SOLOIST to bounded satisfiability checking of CLTLB (D), which is supported by ZOT, an SMT-based verification toolkit. We detail the results of applying the proposed offline trace checking procedure to different types of traces, and compare its performance with previous work.","PeriodicalId":138805,"journal":{"name":"2014 IEEE 7th International Conference on Service-Oriented Computing and Applications","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127027823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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