Grossniklaus, Michael
0000-0003-1609-2221 Berufsbeschreibung
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C-SPARQL : A Continuous Query Language for RDF Data Streams
This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time.
C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of examples, relative to urban computing applications, that systematically cover the SPARQL extensions. The expression of meaningful queries over streaming data is strictly connected to the availability of aggregation primitives, thus C-SPARQL also includes extensions in this respect.
Chapter 10 : Join Methods and Query Optimization
Joins between data sources are an essential ingredient of multi-domain queries, as they exploit connection patterns defined between service marts or between service interfaces. This chapter moves from the definition of a query language over service interfaces, sketching how queries can be directly expressed over service marts and how these can be translated over service interfaces. The fundamental operation discussed in this chapter is the binary join between two sources, which is influenced by the type (search vs. exact) of services and by the management (parallel vs. sequential) of service calls. Then, this chapter presents an optimization framework for queries over several service interfaces, which considers several cost metrics for mapping queries into query plans, consisting of specific operations over services, and includes a branch and bound approach to the exploration of the combinatorial search space of all possible query plans.
Incremental Reasoning on Streams and Rich Background Knowledge
This article presents a technique for Stream Reasoning, consisting in incremental maintenance of materializations of ontological entailments in the presence of streaming information. Previous work, delivered in the context of deductive databases, describes the use of logic programming for the incremental maintenance of such entailments. Our contribution is a new technique that exploits the nature of streaming data in order to efficiently maintain materialized views of RDF triples, which can be used by a reasoner.
By adding expiration time information to each RDF triple, we show that it is possible to compute a new complete and correct materialization whenever a new window of streaming data arrives, by dropping explicit statements and entailments that are no longer valid, and then computing when the RDF triples inserted within the window will expire. We provide experimental evidence that our approach significantly reduces the time required to compute a new materialization at each window change, and opens up for several further optimizations.
Search Computing Challenges and Directions
Search Computing (SeCo) is a project funded by the European Research Council (ERC). It focuses on building the answers to complex search queries like “Where can I attend an interesting conference in my field close to a sunny beach?” by interacting with a constellation of cooperating search services, using ranking and joining of results as the dominant factors for service composition. SeCo started on November 2008 and will last 5 years. This paper will give a general introduction to the Search Computing approach and then focus on its query optimization and execution engine, the aspect of the project which is most tightly related to “objects and databases” technologies.
An execution environment for C-SPARQL queries
Continuous SPARQL (C-SPARQL) is proposed as new language for continuous queries over streams of RDF data. It covers a gap in the Semantic Web abstractions which is needed for many emerging applications, including our focus on Urban Computing. In this domain, sensor-based information on roads must be processed to deduce localized traffic conditions and then produce traffic management strategies. Executing C-SPARQL queries requires the effective integration of SPARQL and streaming technologies, which capitalize over a decade of research and development; such integration poses several nontrivial challenges.
In this paper we (a) show the syntax and semantics of the C-SPARQL language together with some examples; (b) introduce a query graph model which is an intermediate representation of queries devoted to optimization; (c) discuss the features of an execution environment that leverages existing technologies; (d) introduce optimizations in terms of rewriting rules applied to the query graph model, so as to efficiently exploit the execution environment; and (e) show evidence of the effectiveness of our optimizations on a prototype of execution environment.
Search Computing Systems (Extended Abstract)
Search Computing defines a new class of applications, which enable end users to perform exploratory search processes over multi-domain data sources available on the Web. These applications exploit suitable software frameworks and models that make it possible for expert users to configure the data sources to be searched and the interfaces for query submission and result visualization. We describe some usage scenarios and the reference architecture for Search Computing systems.
Stream Reasoning : Where We Got So Far
Data Streams - unbounded sequences of time-varying data elements - are pervasive. They occur in a variety of modern applications including the Web where blogs, feeds, and microblogs are increasingly adopted to distribute and present information in real-time streams. We foresee the need for languages, tools and methodologies for representing, managing and reasoning on data streams for the Semantic Web. We collectively name those research chapters Stream Reasoning. In this extended abstract, we motivate the need for investigating Steam Reasoning; we characterize the notion of Stream Reasoning; we report the results obtained by Politecnico di Milano in studying Stream Reasoning from 2008 to 2010; and we close the paper with a short review of the related works and some outlooks.
Chapter 12 : Panta Rhei ; Flexible Execution Engine for Search Computing Queries
The efficient execution of data-intensive computations over services is a challenging task: data are retrieved from remote sources and therefore are not available in the query engine until after the execution of these calls, but the system must be inherently efficient thereafter, by guaranteeing that data is immediately cached and processed efficiently, according to the best query plan. In this chapter, we present a flexible execution model for search computing queries, named Panta Rhei. The proposed execution engine paradigm adopts the producer/consumer model and supports both data-driven and event-driven synchronization, and their interplay. Query plans are modeled as directed graphs, whose nodes are processing units and whose edges are either control or data flows. While control flows synchronize service calls and unit execution, data flows transfer data between units that process data flows to produce query results. We present the specification of Panta Rhei by formally defining the units for data production, consumption, manipulation, and caching, as well as the control and data flows. Finally, we discuss how a query plan is expressed in terms of a query execution plan.