Anca A. Ivan

Mashup Advisor: A Recommendation Tool for Mashup Development

Mashup editors, like Yahoo Pipes and IBM Lotus Mashup Maker, allow non-programmer end-users to ldquomash-uprdquo information sources and services to meet their information needs. However, with the increasing number of services, information sources and complex operations like filtering and joining, even an easy to use editor is not sufficient. MashupAdvisor aims to assist mashup creators to build higher quality mashups in less time. Based on the current state of a mashup, the MashupAdvisor quietly suggests outputs (goals) that the user might want to include in the final mashup. MashupAdvisor exploits a repository of mashups to estimate the popularity of specific outputs, and makes suggestions using the conditional probability that an output will be included, given the current state of the mashup. When a suggestion is accepted, MashupAdvisor uses a semantic matching algorithm and a metric planner to modify the mashup to produce the suggested output. Our prototype was implemented on top of IBM Lotus MashupMaker and our initial results show that it is effective.

Learning Ontologies to Improve the Quality of Automatic Web Service Matching

Automatically finding suitable Web services given a request is a difficult problem because the interface descriptions of Web services are often terse and cryptic. Dictionary and information retrieval based techniques have proven useful in disambiguating the semantics of service descriptions, but they are limited in their capability to consider the relationships between the words describing the Web services. Current ontology-based approaches typically require a user to explicitly create domain ontologies. This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service descriptions and (2) using these ontologies to guide the mapping between Web services. Our approach differs from earlier work on service matching by considering the relationship between words rather than treating them as a bag of unrelated words. The experimental results indicate that with our unsupervised approach we can eliminate up to 70% of incorrect matches that are made by dictionary-based approaches.

Learning ontologies to improve the quality of automatic web service matching

Automatically finding suitable Web services given a request is a difficult problem because the interface descriptions of Web services are often terse and cryptic. Dictionary and information retrieval based techniques have proven useful in disambiguating the semantics of service descriptions, but they are limited in their capability to consider the relationships between the words describing the Web services. Current ontology-based approaches typically require a user to explicitly create domain ontologies. This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service descriptions and (2) using these ontologies to guide the mapping between Web services. Our approach differs from earlier work on service matching by considering the relationship between words rather than treating them as a bag of unrelated words. The experimental results indicate that with our unsupervised approach we can eliminate up to 70% of incorrect matches that are made by dictionary-based approaches.

Discovering Business Process Similarities: An Empirical Study with SAP Best Practice Business Processes

Large organizations tend to have hundreds of business processes. Discovering and understanding the similarities among these business processes are useful to organizations for a number of reasons: (a) business processes can be managed and maintained more efficiently, (b) business processes can be reused in new or changed implementations, and (c) investment guidance on which aspects of business processes to improve can be obtained. In this empirical paper, we present the results of our study on over five thousand business processes obtained from SAP’s standardized business process repository divided up into two groups: Industry-specific and Cross-industry. The results are encouraging. We found that 39% of cross-industry processes and 43% of SAP-industry processes have commonalities. Additionally, we found that 20% of all processes studied have at least 50% similarity with other processes. We use the notion of semantic similarity on process and process activity labels to determine similarity. These results indicate that there is enough similarity among business processes in organizations to take advantage of. While this is anecdotally stated, to our knowledge, this is the first attempt to empirically validate this hypothesis using real-world business processes of this size. We present the implications and future research directions on this topic and call for further empirical studies in this area.

Semantic Matching to Achieve Web Service Discovery and Composition

In this paper, we present a novel algorithm to discover and compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. In addition, we integrate semantic and ontological matching with an indexing method, which we call attribute hashing, to enable fast lookup of semantic ally related concepts