Jinghua Groppe

Embedding SPARQL into XQuery/XSLT

The tree-based languages XQuery and XSLT for XML are widely supported. Many tools do not yet support the new RDF graph query language SPARQL. We propose to embed SPARQL subqueries into XQuery/XSLT, such that XQuery and XSLT benefit from the graph query language constructs of SPARQL, and SPARQL benefits from features of XQuery/XSLT, which SPARQL does not support. The embedding enables XQuery/XSLT tools to handle at the same time XML queries and SPARQL subqueries, and XML and RDF data.

A SPARQL Engine for Streaming RDF Data

The basic data format of the semantic Web is RDF. SPARQL, which has been developed by the W3C, is the upcoming standard for RDF query languages. Typical engines for processing SPARQL queries on RDF data first read all RDF data, may build indices of the complete read data and afterwards evaluate SPARQL queries. Such engines cannot operate on streaming RDF data. Streaming query engines operating on streams of data can (a) discard irrelevant input as early as possible, and thus save processing costs and space costs, (b) build indices only on those parts of the data, which are needed for the evaluation of the query, and (c) determine partial results of a query as early as possible, and thus evaluate queries more efficiently. We propose such a streaming SPARQL engine, which is the first streaming SPARQL engine to the best of our knowledge.

Filtering unsatisfiable XPath queries

The satisfiability test checks, whether or not the evaluation of a query returns the empty set for any input document, and can be used in query optimization for avoiding the submission and the computation of unsatisfiable queries. Thus, applying the satisfiability test before executing a query can save processing time and query costs. We focus on the satisfiability problem for queries formulated in the XML query language XPath, and propose a schema-based approach to the satisfiability test of XPath queries, which checks whether or not an XPath query conforms to the constraints in a given schema. If an XPath query does not conform to the constraints given in the schema, the evaluation of the query will return an empty result for any valid XML document. Thus, the XPath query is unsatisfiable. We present a complexity analysis of our approach, which proves that our approach is efficient for typical cases. We present an experimental analysis of our developed prototype, which shows the optimization potential of avoiding the evaluation of unsatisfiable queries.

Efficient processing of SPARQL joins in memory by dynamically restricting triple patterns

Since there are a lot of similar or common properties between RDF and relational databases and between SPARQL and SQL, many efforts focus on leveraging the research results of optimizing relational query languages for optimizing SPARQL queries. However, SPARQL has its own characteristics different from SQL, which are not fully exploited by existing work. Therefore, there is still much space for research on optimizing SPARQL queries. Based on the triple nature of RDF data, we create 7 indices to retrieve RDF data quickly; based on the SPARQL-specific properties and the 7 indices, we develop a new, efficient approach to computing join by dynamically restricting triple patterns. Our experimental results show the efficiency of our approach.

Translating XPath queries into SPARQL queries

The W3C has developed XPath [3] as a query language for XML data. XPath is embedded in many other languages like XQuery and XSLT. The name of XPath derives from its basic concept, the path expression, with which the user can hierarchically address the nodes of the XML data. The user of XPath may not only use simple relationships like parent-child, but also more complex relationships like the descendant relationship, which is the transitive closure of the parent-child relationship. Furthermore, complex filter expressions are allowed in XPath queries. RDF is a language for representing information about resources in the World Wide Web. SPARQL [2] supports querying RDF by triple and optional patterns, con- and disjunctions and extensible value testing.

XPath Query Simplification with regard to the Elimination of Intersect and Except Operators

XPath is widely used as an XML query language and is embedded in XQuery expressions and in XSLT stylesheets. In this paper, we propose a rule set which logically simplifies XPath queries by using a heuristic method in order to improve the processing time. Furthermore, we show how to substitute the XPath 2.0 intersect and except operators in a given XPath query with computed filter expressions. A performance evaluation comparing the execution times of the original XPath queries, which contain the intersect and except operators, and of the queries that are the result of our simplification approach shows that, depending on the used query evaluator and on the original query, performance improvements of a factor of up to 350 are possible.

Visual query system for analyzing social semantic web

The social web is becoming increasingly popular and important, because it creates the collective intelligence, which can produce more value than the sum of individuals. The social web uses the Semantic Web technology RDF to describe the social data in a machine-readable way. RDF query languages play certainly an important role in the social data analysis for extracting the collective intelligence. However, constructing such queries is not trivial since the social data is often quite large and assembled from a large number of different sources. In order to solve these challenges, we develop a Visual Query System (VQS) for helping the analysts of social data and other semantic data to formulate such queries easily and exactly. In this VQS, we suggest a condensed data view, a browser-like query creation system for absolute beginners and a Visual Query Language (VQL) for beginners and experienced users. Using the browser-like query creation or the VQL, the analysts of social data and other semantic data can construct queries with no or little syntax knowledge; using the condensed view, they can determine easily what queries should to be used. Furthermore, our system also supports precise suggestions to extend and refine existing queries.

LuposDate: a semantic web database system

Managing and querying Semantic Web are important issues for Semantic Web applications. Therefore, we have developed a Semantic Web database system with logically and physically optimized SPARQL engines to manage and query RDF data, named LuposDate. In order to present the functionalities of the LUPOSDATE system and engines, we have developed an online demonstration, which is available at http://www.ifis.uni-luebeck.de/index.php?id=luposdate-demo.

Parallelizing join computations of SPARQL queries for large semantic web databases

While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Todays multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.

External sorting for index construction of large semantic web databases

Todays Semantic Web datasets become increasingly larger containing up to several hundred million triples. The performance of index construction is a crucial factor for the success of large Semantic Web databases. In this paper, we propose two new approaches for RDF index construction: External chunks merge sort and Distribution Sort for RDF. The former stores and retrieves chunks from a special chunks heap to speed up replacement selection. The latter leverages the RDF-specific properties to construct RDF indices and significantly improves the performance. Our experimental results show that our approaches significantly speed up RDF index construction, and are important techniques for large Semantic Web databases.