Sven Groppe

Data Management and Query Processing in Semantic Web Databases

The Semantic Web, which is intended to establish a machine-understandable Web, is currently changing from being an emerging trend to a technology used in complex real-world applications. A number of standards and techniques have been developed by the World Wide Web Consortium (W3C), e.g., the Resource Description Framework (RDF), which provides a general method for conceptual descriptions for Web resources, and SPARQL, an RDF querying language. Recent examples of large RDF data with billions of facts include the UniProt comprehensive catalog of protein sequence, function and annotation data, the RDF data extracted from Wikipedia, and Princeton Universitys WordNet. Clearly, querying performance has become a key issue for Semantic Web applications.In his book, Groppe details various aspects of high-performance Semantic Web data management and query processing. His presentation fills the gap between Semantic Web and database books, which either fail to take into account the performance issues of large-scale data management or fail to exploit the special properties of Semantic Web data models and queries. After a general introduction to the relevant Semantic Web standards, he presents specialized indexing and sorting algorithms, adapted approaches for logical and physical query optimization, optimization possibilities when using the parallel database technologies of todays multicore processors, and visual and embedded query languages.Groppe primarily targets researchers, students, and developers of large-scale Semantic Web applications. On the complementary book webpage readers will find additional material, such as an online demonstration of a query engine, and exercises, and their solutions, that challenge their comprehension of the topics presented.

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.

Semantic Models for Scalable Search in the Internet of Things

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.

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.

Reformulating XPath queries and XSLT queries on XSLT views

Applications using XML for data representation very often use different XML formats and thus require the transformation of XML data. The common approach transforms entire XML documents from one format into another, e.g. by using an XSLT stylesheet. Different from this approach, we use an XSLT stylesheet in order to transform a given XPath query or a given XSLT query so that we retrieve and transform only that part of the XML document, which is sufficient to answer the given query. Among other things, our approach avoids problems of replication, saves processing time, and in distributed scenarios, transportation costs.

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.

XPath query transformation based on XSLT stylesheets

Whenever XML data must be shared by heterogeneous applications, transformations between different application-specific XML formats are necessary. The state-of-the-art method transforms entire XML documents from one application format into another e.g. by using an XSLT stylesheet, so that each application can work locally on its preferred format. In our approach, we use an XSLT stylesheet in order to transform a given XPath query such that we retrieve and transform only that part of the XML document which is sufficient to answer the given query. Among other things, our approach avoids problems of replication, saves processing time and in distributed scenarios, transportation costs.