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Yashwant Keswani

Publications and source records attributed to Yashwant Keswani.

2 recordsLinked to original sources

A Stitch in Time Saves Nine -- SPARQL querying of Property Graphs using Gremlin Traversals

Knowledge graphs have become popular over the past years and frequently rely on the Resource Description Framework (RDF) or Property Graphs (PG) as underlying data models. However, the query languages for these two data models -- SPARQL for RDF and Gremlin for property graph traversal -- are lacking interoperability. We present Gremlinator, a novel SPARQL to Gremlin translator. Gremlinator translates SPARQL queries to Gremlin traversals for executing graph pattern matching queries over graph databases. This allows to access and query a wide variety of Graph Data Management Systems (DMS) using the W3C standardized SPARQL query language and avoid the learning curve of a new Graph Query Language. Gremlin is a system-agnostic traversal language covering both OLTP graph database or OLAP graph processors, thus making it a desirable choice for supporting interoperability wrt. querying Graph DMSs. We present a comprehensive empirical evaluation of Gremlinator and demonstrate its validity and applicability by executing SPARQL queries on top of the leading graph stores Neo4J, Sparksee, and Apache TinkerGraph and compare the performance with the RDF stores Virtuoso, 4Store and JenaTDB. Our evaluation demonstrates the substantial performance gain obtained by the Gremlin counterparts of the SPARQL queries, especially for star-shaped and complex queries.

cs.DB↗

Let's HPC: A web-based interactive platform to aid High Performance Computing education

Let's HPC (www.letshpc.org) is an open-access online platform to supplement conventional classroom oriented High Performance Computing (HPC) and Parallel & Distributed Computing (PDC) education. The web based platform provides online plotting and analysis tools which allow users to learn, evaluate, teach and see the performance of parallel algorithms from a system's viewpoint. The user can quantitatively compare and understand the importance of numerous deterministic as well as non-deterministic factors of both the software and the hardware that impact the performance of parallel programs. At the heart of this platform is a database archiving the performance and execution environment related data of standard parallel algorithms executed on different computing architectures using different programming environments, this data is contributed by various stakeholders in the HPC community. The plotting and analysis tools of our platform can be combined seamlessly with the database to aid self-learning, teaching, evaluation and discussion of different HPC related topics. Instructors of HPC/PDC related courses can use the platform's tools to illustrate the importance of proper analysis in understanding factors impacting performance, to encourage peer learning among students, as well as to allow students to prepare a standard lab/project report aiding the instructor in uniform evaluation. The platform's modular design enables easy inclusion of performance related data from contributors as well as addition of new features in the future.

cs.CY↗