Abstract. In this paper we evaluate the performance of the SQL queries generated by ontop, a system that uses a formal approach to translate and optimize SPARQL queries and R2RML mappings. We show that the performance of ontop’s SQL queries is superior to that of the performance of well known

is (E)xtracted from the source, (T)ransformed into RDF or OWL datasets in the target vocabulary and (L)oaded into a SPARQL endpoint or an OWL reasoner. This process carries all the issues that come with e.g., the need for synchronisation mechanisms to keep data up-to-date, the extra cost in time

in the presence of arbitrary complex optional graph patterns. In this paper, we propose: (i) a basic graph pattern translation algorithm, BGPtoSQL, that translates a basic graph pattern to its SQL equivalent; and based on BGPtoSQL, (ii) a semantics preserving SPARQL-to-SQL query translation algorithm, SPARQLtoSQL

, that translate information into a common object model, that combine information from several sources, that allow browsing of information, and that manage constraints across heterogeneous sites. Tsimmis is a joint project between Stanford and the IBM Almaden Research Center.

to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations (e.g., join and Top N).

it supports concurrent execution of diverse mobile applications. We identify agility as a key attribute of adaptive systems, and describe how to quantify and measure it. We present the results of our evaluation of Odyssey, indicating performance improvements up to a factor of 5 on a benchmark of three

the design, implementation and evaluation of highperformance OS migration built on top of the Xen VMM. 1

and implemented a prototype system that converts XML documents to relational tuples, translates semi-structured queries over XML documents to SQL queries over tables, and converts the results to XML. We have qualitatively evaluated this approach using several real DTDs drawn from diverse domains. It turns out

RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data.

predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering by recording their reactions to documents they read. The reactions are called annotations; they can be accessed by other people’s filters. Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser. Tapestry’s client/server architecture, its various components, and the Tapestry query language are described.