View PDF Access through your institution Purchase PDF Journal of Systems and Software Volume 109, November 2015, Pages 78-87 Towards a hybrid relational and XML benchmark for loosely-coupled distributed data sources Mahesh B. Chaudhari, Suzanne W. Dietrich , Jennifer Ortiz, Spencer Pearson Show more Outline Share Cite https://doi.org/10.1016/j.jss.2015.07.029 Get rights and content Highlights • Designed heterogeneous data sources for a hybrid version of the TPC-H enterprise. • Developed hybrid LINQ queries over the relational and XML data sources. • Evaluated the hybrid benchmark for loosely-coupled distributed data sources. • Assessed query performance for two database products with various options. Abstract There are known benchmarks for the performance evaluation of relational and XML databases. However, there is an increasing demand for database applications that require access to heterogeneous loosely-coupled distributed data sources. This paper presents a hybrid benchmark based on TPC-H where the data sources are heterogeneous. Specifically, the paper describes the design of the relational and XML data sources as well as the query redesign in the LINQ query language, which supports queries over heterogeneous data sources. The results of a performance evaluation of the hybrid benchmark over various database products is included for untyped and typed XML with and without clearing the database cache. Previous Next Keywords Benchmark; Heterogeneous; LINQ Recommended articles Citing articles (1) Mahesh B. Chaudhari is currently a Sr. Software Engineer at Zephyr Health Inc. His primary responsibilities involve data modeling and enterprise integration, ontology development, algorithm design and incorporation into Zephyr Platform. Before Zephyr, he was a postdoctoral fellow at Arizona State University. He also has a Ph.D. in Computer Science from Arizona State University. His academic research interests focus on incremental view maintenance, condition monitoring over distributed heterogeneous data sources, and multiple query optimization. He has 2 years of teaching experience and is a recipient of PFF Emeriti Fellowship for excellence in research, teaching and mentorship. Suzanne W. Dietrich is a Professor in the Applied Computing program within the School of Mathematical and Natural Sciences at Arizona State University. She has an established research foundation covering materialized view maintenance in various environments including active rules and event processing, with an emphasis in performance evaluation. Her research has also included event-based application integration using rules. Jennifer Ortiz is currently a Computer Science Ph.D. student at the University of Washington. She is researching the development of tools to facilitate analysis of scientific data in the cloud. Jennifer has an M.S. in Computer Science from the University of Washington. Jennifer contributed to this paper as an undergraduate student in Applied Computing at Arizona State University. Spencer Pearson contributed to this paper as an undergraduate student at Arizona State University. His research interests include similarity joins and other similarity-aware operators. He graduated from Arizona State University with a B.S. in Applied Computing as part of Barrett, the Honors College. View full text Copyright © 2015 Elsevier Inc. All rights reserved. About ScienceDirect Remote access Shopping cart Advertise Contact and support Terms and conditions Privacy policy We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies. Copyright © 2021 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V. py g © ScienceDirect ® is a registered trademark of Elsevier B.V. g