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On Identifying and Reducing Irrelevant Information in Service Composition and Execution
 

On Identifying and Reducing Irrelevant Information in Service Composition and Execution

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The increasing availability of massive information on the ...

The increasing availability of massive information on the
Web causes the need for information aggregation by filtering and ranking
according to user’s goals. In the last years both industrial and academic
researchers have investigated the way in which quality of services can be
described, matched, composed and monitored for service selection and
composition. However, very few of them have considered the problem of
evaluating and certifying the quality of the provided service information
to reduce irrelevant information for service consumers, which is crucial to
improve the efficiency and correctness of service composition and execu-
tion. This paper discusses several problems due to the lack of appropriate
way to manage quality and context in service composition and execution,
and proposes a research roadmap for reducing irrelevant service informa-
tion based on context and quality aspects. We present a novel solution
for dealing with irrelevant information about Web services by developing
information quality metrics and by discussing experimental evaluations

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    On Identifying and Reducing Irrelevant Information in Service Composition and Execution On Identifying and Reducing Irrelevant Information in Service Composition and Execution Presentation Transcript

    • On Identifying and Reducing Irrelevant Information in Service Composition and ExecutionHong-Linh Truong1, Marco Comerio2, Andrea Maurino2, Schahram Dustdar1, Flavio De Paoli2, Luca Panziera2 1 Distributed Systems Group, Vienna University of Technology 2 Departmen of Informatices, Systems and Communication University of Milano - Bicocca truong@infosys.tuwien.ac.at http://www.infosys.tuwien.ac.at/Staff/truong WISE 2010, 13 Dec 2010, Hong Kong 1
    • Overview Motivation Irrelevant information problems in service composition and execution Enhancing context and quality support for information about services and their data Reducing services and resources by qualifying non-functional information Conclusions and future work WISE 2010, 13 Dec 2010, Hong Kong 2
    • Motivating examples (1) In the Semantic WS Challenge 2009  http://sws-challenge.org/wiki/index.php/Scenario:_Logistics_Management  Logistic operators offer shipping services, each characterized by a set of NFPs (e.g., payment method, payment deadline, base price, etc.)  Assume 100 equivalent services, each offers 5 service contracts Without the quality of information, e.g., completeness and timeliness, for service contracts  Time-consuming task to detect irrelevant contracts  Missing automatic detection of irrelevant contracts potentially leads to wrong decision → irrelevant service information in service composition WISE 2010, 13 Dec 2010, Hong Kong 3
    • Motivating examples (2) In many cases service composition is conducted before composition execution Temporal distance between composition and execution time potentially makes  At execution time, information about services in the composition becomes irrelevant when the composition is executed  QoS-based service adaptation is only a particular example → irrelevant service information in service execution WISE 2010, 13 Dec 2010, Hong Kong 4
    • Motivating examples (3) Lets compose services (e.g., Flickr and Youtube) given context constraints (e.g., free for non-commercial purpose, country location, etc.) and quality of data and services  No or unstructured copyright policy and data licensing information  impossible to be used for automatic service selection No quality and context associated with provided data  Impossible to be used for selecting and filtering data → irrelevant information in service usage WISE 2010, 13 Dec 2010, Hong Kong 5
    • Our goals Examine possible irrelevant information problems in the context of service composition and execution  The typical lifecycle of service composition and execution  Information about services and their data: service description, quality of service, service context, quality of data, etc.  “context” in our work: data/service usage right/licensing, law enforcements + traditional context (e.g., location) Focus on data-intensive services (data-as-a-service)  Not just a service as a whole: the service provider may not be the data provider  Common topics with the Web information community WISE 2010, 13 Dec 2010, Hong Kong 6
    • Limitation of existing work Generic information overloading solutions  not targeted to information about services Tag cloud-based service filtering  not sure how tag clouds can be used to describe the quality of information about services and their data Context and QoS-based service selection approaches  Do not consider quality of data  Do not consider context, QoS and QoD together  Often assume high-quality service information Do not distinguish the service level and the data resource level WISE 2010, 13 Dec 2010, Hong Kong 7
    • Service composition & execution Type C: information about the composite service and data provided by the composite service.Type A:Requirements aboutservice and dataschemas, NFPs,documentation, service Type E:contracts, and information about dataprovenance information requested by the consumer Type D: data delivered by data- intensive services. Type B: service and data schemas, NFPs, documentation, service contracts, and provenance information WISE 2010, 13 Dec 2010, Hong Kong 8
    • Irrelevant information problems Context and quality information models  Little support for data licensing/rights and quality of data (QoD) associated with services and data resources Context and quality information access APIs  No/limited description of data and service usage  No separate API for retrieving quality and context information of services and their data  No quality and context information associated with the requested data Context and quality evaluation techniques  Missing compatibility evaluation techniques for context and quality of composite services WISE 2010, 13 Dec 2010, Hong Kong 9
    • Current research focuses and practice uses  Context and quality information models  Often used only a fraction of context, little information about QoD, and unstructured context and quality description  Access APIs  Mainly static publishing, mainly QoS metrics at runtime but typically at the service as a whole level  Adaptive and context-aware algorithms  Mainly for adapting individual services in a composition based on QoS and “traditional” context  Either for the consumer-service flow or the composite service- service flowThe role of data concerns? Context and quality associated with dataresources? → a common topic of Web services and Web resources WISE 2010, 13 Dec 2010, Hong Kong 10
    • Our suggested roadmap Develop meta and domain-dependent semantic representations for quality and context information  To enrich traditional QoS and context parameters with data- specific parameters using the linked data model Develop context and quality information that can be accessed via open APIs for services and data resources  To support on-the-fly access to such information  Not just for the well-known “the broken SOA triangle” Develop techniques for context and quality compatibility evaluation  To focus more on data/service licensing and QoD for service composition based on their data and control dependencies WISE 2010, 13 Dec 2010, Hong Kong 11
    • Reducing services and resources by qualifying non-functional information A particular solution to deal with untrusted/low- quality information about services  Apply to information exchanged between services/service information systems, composition engines, composition tools and developers Our initial solutions  Use quality of data metrics to characterize service information  We just utilize some basic metrics  Filter service information based on consumers requests.  Could be integrated with other solutions WISE 2010, 13 Dec 2010, Hong Kong 12
    • Some QoD metrics (1) Completeness specifies the ratio of missing values of provided NFP information, NFPp to the expected minimum set of NFPs, NFPmin ∥NFP p∩NFP min∥ Completeness=1− NFP min Timeliness specifies how current a non-functional property is. Timeliness=1− Age  ExpectedLifetime ,1 Note: these metrics can be associated with data provided byservices, used for reducing irrelevant results WISE 2010, 13 Dec 2010, Hong Kong 13
    • Some QoD metrics (2) Interpretability specifies the availability of documentation and metadata for correct interpretation of service information Category Service information Examples schema conceptual service and data WSDL, SAWSDL, pre/post schemas conditions, data models documentation documents APIs explanation, best practices NFP non-functional properties categorization, location, QoS information contract service contracts and contract service level agreements based on templates NFPs provenance Provenance information versioning of schemas, NFPs, contracts Interpretability= ∑ score category i ×wi  ∑ wi Note: The evaluation of this metric requires different techniques for different categories WISE 2010, 13 Dec 2010, Hong Kong 14
    • Filtering mechanisms Two types of filtering  Interpretability and NFPs. NFP-based filtering:  Step 1: Extract and establish NFPmin and ExpectedLifetime from the developer’s requirement;  Step 2: Evaluate QoD metrics, e.g., Completeness and Timeliness;  Step 3: Establish filtering thresholds based on QoD metrics;  Step 4: Eliminate services whose information does not meet conditions setup in Step 3;  Step 5: Refine the filtering by repeating Step 3 WISE 2010, 13 Dec 2010, Hong Kong 15
    • Experiment: filtering service contracts http://bit.ly/ewsYZC Analyze the time required for ranking of 500 WSML (Web service modeling language) contracts  without filters;  applying a filtering phase on completeness;  applying a filtering phase on timeliness and  applying a filtering phase on completeness and timeliness. We performed two different experiments using an Intel(R) Core(TM)2 CPU T5500 1.66GHz with 2GB RAM and Linux kernel 2.6.33 64 bits. WISE 2010, 13 Dec 2010, Hong Kong 16
    • Experiments: filtering evaluation Performance evaluation with threshold: Completeness ≥ 0.6 (Filter 1) and Timeliness > 0.2 (Filter 2). WISE 2010, 13 Dec 2010, Hong Kong 17
    • Experiments: filtering evaluation With thresholds= {0, 0.2, 0.4, 0.6, 0.8, 1} which are equivalent to {not required, optional, preferred, strong preferred, required, strict required } WISE 2010, 13 Dec 2010, Hong Kong 18
    • Conclusions and future work (1) We have identified several irrelevant information problems in the lifecycle of service composition and execution We proposed 3 topics for enhancing context and quality support for information about services  a particular solution based on information quality metrics is illustrated Future work  Systematically extend and evaluate specific QoD metrics for service information  Integrate our QoD-based solution with existing service selection and composition techniques/tools WISE 2010, 13 Dec 2010, Hong Kong 19
    • Conclusion and future work (2) http://www.dbai.tuwien.ac.at/sodp/ In order to reduce irrelevant information for data services: data-as-a-service publishing needs to combine forces from (Web) data management, SOC and cloud/grid computing WISE 2010, 13 Dec 2010, Hong Kong 20