In Service-Oriented Architectures, everything is a service and everyone is a service provider. We... more In Service-Oriented Architectures, everything is a service and everyone is a service provider. Web services (or simply services) are loosely coupled software components that are published, discovered, and invoked across the Web. As the use of Web services grows, in order to correctly interact with the growing services, it is important to understand the business protocols that provide clients with the information on how to interact with services. In dynamic Web services environments, service providers need to constantly adapt their business protocols for reflecting the restrictions and requirements proposed by new applications, new business strategies, and new laws, or for fixing problems found in the protocol definition. However, the effective management of such a protocol evolution raises critical problems: one of the most critical issues is how to handle instances running under the old protocol when it has been changed. Simple solutions, such as aborting them or allowing them to continue to run according to the old protocol, can be considered, but they are inapplicable for many reasons (e.g., the loss of work already done and the critical nature of work). In this paper, we present a framework that supports service managers in managing the business protocol evolution by providing several features, such as a variety of protocol change impact analyses automatically determining which ongoing instances can be migrated to the new version of protocol, and data mining techniques inferring interaction patterns used for classifying ongoing instances migrateable to the new protocol. To support the protocol evolution process, we have also developed database-backed GUI tools on top of our existing system. The proposed approach and tools can help service managers in managing the evolution of ongoing instances when the business protocols of services with which they are interacting have changed.
Given a set of entities and a query, similar entity search aims to identify all entities similar ... more Given a set of entities and a query, similar entity search aims to identify all entities similar to the query (e.g., a person or a document). One issue in similar entity search is determining which similarity functions should be used for a given similarity analysis task, as no single similarity function can perform well for all different types of data and tasks. Although existing approaches for similarity analysis have made significant progress, they have not addressed the fundamental issue. In this paper, we propose an approach for suggesting which similarity functions (e.g., edit distance) are most appropriate for computing similarity between two entities. We identify data features (e.g., misspellings or word orders) that are considerable when choosing similarity functions. We also introduce the concept of similarity function background knowledge that associates data features with similarity functions, and apply the knowledge to recommend suitable similarity functions. Evaluation results on real-life datasets show the effectiveness and feasibility of our approach.
In Service-Oriented Architectures, everything is a service and everyone is a service provider. We... more In Service-Oriented Architectures, everything is a service and everyone is a service provider. Web services (or simply services) are loosely coupled software components that are published, discovered, and invoked across the Web. As the use of Web services grows, in order to correctly interact with the growing services, it is important to understand the business protocols that provide clients with the information on how to interact with services. In dynamic Web services environments, service providers need to constantly adapt their business protocols for reflecting the restrictions and requirements proposed by new applications, new business strategies, and new laws, or for fixing problems found in the protocol definition. However, the effective management of such a protocol evolution raises critical problems: one of the most critical issues is how to handle instances running under the old protocol when it has been changed. Simple solutions, such as aborting them or allowing them to continue to run according to the old protocol, can be considered, but they are inapplicable for many reasons (e.g., the loss of work already done and the critical nature of work). In this paper, we present a framework that supports service managers in managing the business protocol evolution by providing several features, such as a variety of protocol change impact analyses automatically determining which ongoing instances can be migrated to the new version of protocol, and data mining techniques inferring interaction patterns used for classifying ongoing instances migrateable to the new protocol. To support the protocol evolution process, we have also developed database-backed GUI tools on top of our existing system. The proposed approach and tools can help service managers in managing the evolution of ongoing instances when the business protocols of services with which they are interacting have changed.
Given a set of entities and a query, similar entity search aims to identify all entities similar ... more Given a set of entities and a query, similar entity search aims to identify all entities similar to the query (e.g., a person or a document). One issue in similar entity search is determining which similarity functions should be used for a given similarity analysis task, as no single similarity function can perform well for all different types of data and tasks. Although existing approaches for similarity analysis have made significant progress, they have not addressed the fundamental issue. In this paper, we propose an approach for suggesting which similarity functions (e.g., edit distance) are most appropriate for computing similarity between two entities. We identify data features (e.g., misspellings or word orders) that are considerable when choosing similarity functions. We also introduce the concept of similarity function background knowledge that associates data features with similarity functions, and apply the knowledge to recommend suitable similarity functions. Evaluation results on real-life datasets show the effectiveness and feasibility of our approach.
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Papers by Seung Ryu