Grid Service Discovery with Rough Sets Maozhen Li, Member, IEEE, Bin Yu, Omer Rana, and Zidong Wang, Senior Member, IEEE IEEE TRANSACTION S ON KNOLEDGE AND DATA ENGINEERING, VOL. 20, NO. 6, JUNE 2008 Present by Chen, Ting-Wei

2 Outline Introduction The Design of ROSSE QoS Modeling ROSSE Case Study ROSSE Evaluation Conclusions

3 Introduction ROSSE Rough sets-based search engine Discovery Grid service Maximize user satisfaction in service discovery Evaluate the discovery of computing services Accuracy Efficiency

4 The Design of ROSSE (cont.) Service publication Service discovery

5 The Design of ROSSE (cont.)

6 Step 1 advertise the service to ROSSE through a Web user interface Step 2 Load into the ROSSE Service Repository Names Properties

7 The Design of ROSSE (cont.) Step 3 Publish service ontology that can be defined in OWL Step 4 Load into the ROSSE Ontology Repository Inference engine to infer the semantic relationships of properties

8 The Design of ROSSE (cont.) Step 5-6 Step 7-9 Step Step Step 14-16

9 The Design of ROSSE (cont.) Step 5 Post a service query to ROSSE Service category of interest Expected service properties Via its Web user interface Step 6 Pass to the Irrelevant Property Identification component To page 8

10 The Design of ROSSE (cont.) Step 7 Access the ROSSE Service Repository Step 8 Identify and mark the properties of advertised services Define in the ROSSE Ontology Repository Step 9 The query is passed to the DPR component To page 8

11 The Design of ROSSE (cont.) Step 10 Access the ROSSE Service Repository to identify and mark dependent properties Step 11 The DPR component invokes the Service Similarity Computing (SSC) To page 8

12 The Design of ROSSE (cont.) Step 12 Access ROSSE Service Repository Compute the match degrees of relevant properties of advertised service to the service query Step 13 Functionally matched services have distinct nonfunctional properties related to QoS SSC invoke the QoS Modeling To page 8

13 The Design of ROSSE (cont.) Step 14 In turn filters functionally matched services Step 15 Via the Web user interface of ROSSE Step 16 A list of discovered services To page 8

14 The Design of ROSSE (cont.) Rough Sets for Service Discovery Mathematical technique to deal with uncertainty in knowledge discovery Rough set theory definitely has property p possibly has property p absolutely does not have property p

15 The Design of ROSSE (cont.) Rough set theory for ROSSE 7 8

16 The Design of ROSSE (cont.) Irrelevant Property Identification Semantic relationships with the properties Define Exact match: pQ=pA, or pQ is a subclass of pA Plug-in match: pA subsumes pQ Subsume match: pQ subsumes pA Nomatch: No subsumption between pQ and pA Uncertain: No subsumption between pQ and pA, and pA=NULL

17 The Design of ROSSE (cont.) Dependent Property Reduction Indecisive property ROSSE deals with uncertainty of property Identify indecisive properties 3 4

18 The Design of ROSSE (cont.) Identify individual indecisive properties Check all possible combinations of these individual indecisive properties

19 The Design of ROSSE (cont.) Computing Similarity Degrees The preliminary Fuzzy From a semantic relationship to a fuzzy variable Does not consider the semantic distances of the properties To increase the accuracy in assigning matching degrees Between and

20 The Design of ROSSE (cont.) 1 if exact match, if plug in match, if subsume match, 0 if nomatch Similarity degree to a service query 5 6

21 QoS Modeling (cont.) System-Related QoS System-Related QoS Properties Reliability if 1 if Execution Efficiency if 1 if Availability 9 10

22 QoS Modeling (cont.) Non-System-Related QoS Properties Cost-Effectiveness if 1 if Reputation

23 QoS Modeling (cont.) Overall QoS Values of Functionally Matched Services Overall QoS value

24 ROSSE Case Study (cont.) ROSS Implementation Web system JAVA Web ROSS Service Repository UDDI registry for WSDL services Service repository for OWL-S OWL-S service repository Record service element (name and property)

25 ROSSE Case Study (cont.) Discovery of Computing Services in ROSSE Building a Decision Table 1 → The property is explicitly X → The property is not explicitly Properties Service d3b4e4f3d7f2c4 g3 e1b3 S1S1 1111xxx11 S2S2 X1x1xxx1X S3S3 X1x1x11XX S4S4 X1xx111XX S5S5 X11xxx1XX S6S xxxX S7S7 X1xxx1xXX S8S8 1111xx1XX S9S9 X1x1x1x1X S 10 X1xxxxx1X S 11 X1xxx1xxx S 12 X1111xx11 S x1xX

26 ROSSE Evaluation (cont.) Accuracy of ROSSE in Service Discovery Increased Similarity Degrees of ROSSE UDDI OWL-S ROSSE

27 ROSSE Evaluation (cont.) Measuring Precision and Recall Group 1-Constrains No service had an uncertain property At least one property of a service was assigned an exact match Group 2-No constrains

28 ROSSE Evaluation (cont.) The performance in the tests of group 1 The performance in the tests of group 2

29 ROSSE Evaluation (cont.) The performance of ROSSE in group 1 and group 2

30 ROSSE Evaluation (cont.) The overhead of ROSSE in matching services Efficiency of ROSSE in accessing service records Efficiency of ROSSE in Service Discovery

31 Conclusions ROSSE for discovery of grid services Dynamically reduce uncertain properties when matching services ROSSE increase the accuracy of service discovery To maximize user satisfaction in service discovery ROSSE improves the precision and recall

Thanks for your attention See you next time