Matches in ScholarlyData for { ?s ?p ?o. }
- item-2 hasContent yongjian-yu.
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- 338 creator florian-rosenberg.
- 338 creator oliver-moser.
- 338 creator schahram-dustdar.
- 338 type InProceedings.
- 338 label "Non-Intrusive Monitoring and Service Adaption for WS-BPEL".
- 338 sameAs 338.
- 338 abstract "Web service processes currently lack monitoring and dynamic (runtime) adaptation mechanisms. In highly dynamic processes, services frequently need to be exchanged due to a variety of reasons. In this paper we present VieDAME, which allows monitoring BPEL processes according to Quality of Service (QoS) attributes and to replace existing partner services based on various (pluggable) replacement strategies. The chosen replacement services can be syntactically or semantically equivalent to the BPEL interface. Services can be automatically replaced during runtime without any downtime of the overall system. We implemented our solution with an aspect-oriented approach by intercepting SOAP messages and allow services to be exchanged during runtime with little performance penalty costs, as shown in our experiments, thereby making our approach suitable for high-availability BPEL environments.".
- 338 hasAuthorList authorList.
- 338 hasTopic World_Wide_Web.
- 338 isPartOf proceedings.
- 338 keyword "Aspect-oriented Programming".
- 338 keyword "BPEL".
- 338 keyword "QoS Monitoring".
- 338 keyword "Service Selection and Mediation".
- 338 title "Non-Intrusive Monitoring and Service Adaption for WS-BPEL".
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- item-1 hasContent oliver-moser.
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- 365 creator liping-wang.
- 365 creator na-li.
- 365 creator qing-li.
- 365 type InProceedings.
- 365 label "Substructure similarity measurement in chinese recipes".
- 365 sameAs 365.
- 365 abstract "Improving the precision of information retrieval has been a challenging issue on Chinese Web. This is because, on one hand, Chinese expressions are complicated thus rendering much burden on the searching systems, and on the other hand, the way local users interact with a Chinese website is quite different from that on an English website. As exemplified by Chinese recipes on the Web, it is not easy/natural for people to use keywords (eg, recipe names) to search recipes, since the names can be literally so abstract that they do not bear much, if any, information on the underlying ingredients or cooking methods. In this paper, we investigate the underlying features of Chinese recipes, and based on workflow-like cooking processes, we model recipes as graphs. Benefiting from the characteristics of graphs, we mine frequent common patterns in a cooking graph database. We also propose a novel similarity measure based on the frequent patterns, and devise a novel filtering algorithm to prune unrelated data so as to support efficient and effective on-line searching. Based on our prototype system called RecipeView, we evaluate different graph matching algorithms to examine their capabilities in a complex graph database. These algorithms include Maximum Common Subgraph (MCS) and FSG, the former is a common and popular subgraph isomorphism algorithm widely used in chem/bioinformatics, and the latter is originally proposed as a frequent graph pattern algorithm. In RecipeView, we combine FSG with our proposed similarity measure to detect common subgraphs from a cooking graph database. Our initial experimental studies show that the combined algorithm is highly competitive when compared with its counterparts, therefore is more suitable for on-line similarity search.".
- 365 hasAuthorList authorList.
- 365 hasTopic World_Wide_Web.
- 365 isPartOf proceedings.
- 365 keyword "FSG".
- 365 keyword "cooking graph".
- 365 keyword "filtering".
- 365 keyword "recipes".
- 365 keyword "similarity measure".
- 365 title "Substructure similarity measurement in chinese recipes".
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- item-1 hasContent liping-wang.
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- 367 creator de-sheng-wang.
- 367 creator hang-li.
- 367 creator tao-qin.
- 367 creator tie-yan-liu.
- 367 creator wen-ying-xiong.
- 367 creator xu-dong-zhang.
- 367 type InProceedings.
- 367 label "Learning to Rank Relational Objects and Its Application to Web Search".
- 367 sameAs 367.
- 367 abstract "Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not consider the cases in which there exists relationship between the objects to be ranked, despite of the fact that such situations are very common in practice. For example, in web search, given a query certain relationships usually exist among the the retrieved documents, e.g., URL hierarchy, similarity, etc, and sometimes it is necessary to utilize the information in ranking of the documents. Existing methods, unfortunately, cannot deal with the problem. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the ranking model is defined as a function of not only the contents (features) of objects but also the relations between objects. The paper further focuses on one setting of the learning problem in which the way of using relation information is predetermined. It formalizes the learning task as an optimization problem in the setting, and then proposes employing SVM techniques to conduct the optimization. Experimental results show that the proposed method outperforms the baseline methods for two ranking tasks in web search, indicating that the proposed method can indeed make effective use of relation information and content information in ranking.".
- 367 hasAuthorList authorList.
- 367 hasTopic World_Wide_Web.
- 367 isPartOf proceedings.
- 367 keyword "Learning to Rank Relational Objects".
- 367 keyword "Pseudo Relevance".
- 367 keyword "Relational Ranking SVM".
- 367 keyword "Topic Distillation".
- 367 title "Learning to Rank Relational Objects and Its Application to Web Search".
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