SUMMARY Modern software often uses ontologies as its key component to store data and their relationships. This is different from using an ontology as a stand‐alone tool for knowledge sharing and representation. The ontology component needs to work with other software components and needs to evolve as the software evolves. Ontology design has been a research topic for years; however, most of these studies focus on using ontologies as stand‐alone applications. This paper studies ontology patterns that can be applied to design ontologies as an integral part of a service‐oriented application. The paper first briefly reviews various ontology design issues including a brief survey of existing ontology design patterns. The paper then outlines general principles for using ontologies in software applications, including the needs to incorporate ontology design process as a part of software development processes, design ontologies as a component of an overall software architecture, and use ontologies to enhance software evolution and the role that ontologies can play in software validation. The paper then proposes some common ontology patterns that can be used to design ontologies in service‐oriented applications. This is followed by examining two international projects, SENSEI and FCINT, where ontologies are used in service‐oriented applications and several ontology design patterns are used. Copyright © 2011 John Wiley & Sons, Ltd.

Decision support and knowledge discovery systems often compute aggregate values of interesting attributes by processing a huge amount of data in very large databases and/or warehouses. In particular, iceberg query is a special type of aggregation query that computes aggregate values above a user-provided threshold. Usually, only a small number of results will satisfy the threshold constraint. Yet, the results often carry very important and valuable business insights. Because of the small result set, iceberg queries offer many opportunities for deep query optimization. However, most existing iceberg query processing algorithms do not take advantage of the small-result-set property and rely heavily on the tuple-scan-based approach. This incurs intensive disk accesses and computation, resulting in long processing time especially when data size is large. Bitmap index, which builds one bitmap vector for each attribute value, is gaining popularity in both column-oriented and row-oriented databases in recent years. It occupies less space than the raw data and gives opportunities for more efficient query processing. In this paper, we exploited the property of bitmap index and developed a very effective bitmap pruning strategy for processing iceberg queries. Our index-pruning-based approach eliminates the need of scanning and processing the entire data set (table) and thus speeds up the iceberg query processing significantly. Experiments show that our approach is much more efficient than existing algorithms commonly used in row-oriented and column-oriented databases.

Software-as-as-Service (SaaS) is a new approach for developing software, and it is characterized by its multi-tenancy architecture and its ability to provide flexible customization to individual tenant. However, the multi-tenancy architecture and customization requirements have brought up new issues in software, such as database design, database partition, scalability, recovery, and continuous testing. This paper proposes a hybrid test database design to support SaaS customization with two-layer database partitioning. The database is further extended with a new built-in redundancy with ontology so that the SaaS can recover from ontology, data or meta-data failures. Furthermore, constraints in metadata can be used either as test cases or policies to support SaaS continuous testing and policy enforcement.

Snippets are used by almost every text search engine to complement the ranking scheme in order to effectively handle user searches, which are inherently ambiguous and whose relevance semantics are difficult to assess. Despite the fact that XML is a standard representation format of Web data, research on generating result snippets for XML search remains limited. To tackle this important yet open problem, in this article, we present a system eXtract which generates snippets for XML search results. We identify that a good XML result snippet should be a meaningful information unit of a small size that effectively summarizes this query result and differentiates it from others, according to which users can quickly assess the relevance of the query result. We have designed and implemented a novel algorithm to satisfy these requirements. Furthermore, we propose to cluster the query results based on their snippets. Since XML result clustering can only be done at query time, snippet-based clustering significantly improves the efficiency while compromising little clustering accuracy. We verified the efficiency and effectiveness of our approach through experiments.

Snippets are used by almost every text search engine to complement ranking scheme in order to effectively handle user searches, which are inherently ambiguous and whose relevance semantics are difficult to assess. Despite the fact that XML is a standard representation format of web data, research on generating result snippets for XML search remains untouched.

Snippets are used by almost every text search engine to complement ranking schemes in order to effectively handle user keyword search. Despite the fact that XML is a standard representation format of web data, research on generating result snippets for XML search remains untouched. In this work, we present eXtract, a system that efficiently generates self-contained result snippets within a given size bound which effectively summarize the query results and differentiate them from one another, according to which users can quickly assess the relevance of the query results.