Scientific SPARQL: Semantic Web Queries over Scientific Data

Andrejev, Andrej; Risch, Tore

doi:10.1109/icdew.2012.67

Cited by 17 publications

(19 citation statements)

References 15 publications

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“…Other proposed extensions of SPARQL target specific domains or types of applications, including tSPARQL [Hartig 2009], which allows for specifying and processing trust annotations in terms of which results can be trusted and why; SciS-PARQL [Andrejev and Risch 2012], which provides primitives to deal with numeric arrays of (scientific) information; SPARQL-MM [Kurz et al 2015], which proposes user-defined functions to help when querying meta-data about multimedia artefacts; GeoSPARQL [Perry and Herring 2012;Battle and Kolas 2012], stSPARQL [Koubarakis and Kyzirakos 2010] and SPARQL-ST [Perry et al 2011], which propose extensions to support spatial and temporal queries; EP-SPARQL [Anicic et al 2011], C-SPARQL [Barbieri et al 2010] and Streaming SPARQL [Bolles et al 2008], which deal with processing dynamic information and support, offering event processing, reasoning and querying over windows of streaming data, and so forth.…”

Section: A4 Further Extensionsmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

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We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges; and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.

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Section: A4 Further Extensionsmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

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“…A number of derived arrays, e.g. the ones representing the columns of an original matrix, share the same storage object in memory as explained in [8]. In section IV we also define array proxy objects that operate exactly as array descriptors but point to arrays stored in the back-end database instead.…”

Section: B Scientific Sparql Database Manager (Ssdm)mentioning

confidence: 99%

“…. For this we have defined our extension, SciSPARQL, and implemented it in our prototype system, Scientific SPARQL Database Manager, SSDM [8]. In this work we show how our approach scales to handle large datasets efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Scientific Analysis by Queries in Extended SPARQL over a Scalable e-Science Data Store

Andrejev

Toor

Hellander

et al. 2013

2013 IEEE 9th International Conference on E-Science

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Abstract-Data-intensive applications in e-Science require scalable solutions for storage as well as interactive tools for analysis of scientific data. It is important to be able to query the data in a storage-independent way, and to be able to obtain the results of the data-analysis incrementally (in contrast to traditional batch solutions). We use the RDF data model extended with multidimensional numeric arrays to represent the results, parameters, and other metadata describing scientific experiments, and SciSPARQL, an extension of the SPARQL language, to combine massive numeric array data and metadata in queries. To address the scalability problem we present an architecture that enables the same SciSPARQL queries to be executed on the RDF dataset whether it is stored in a relational DBMS or mapped over a specialized geographically distributed e-Science data store. In order to minimize access and communication costs, we represent the arrays with proxy objects, and retrieve their content lazily. We formulate typical analysis tasks from a computational biology application in terms of SciSPARQL queries, and compare the query processing performance with manually written scripts in MATLAB.

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“…SPARQL is a promising domain calculus based query language for scientific applications [1] [2]. FLOQ supports both SQL and SPARQL as alternative query languages for queries to the FLOQ ontology.…”

Section: Data Models and Query Languagesmentioning

confidence: 99%

Scalable Queries Over Log Database Collections

2015

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In industrial settings, machines such as trucks, hydraulic pumps, etc. are widely distributed at different geographic locations where sensors on machines produce large volumes of data. The data produced is stored locally in autonomous databases called log databases. The collection of log databases is dynamically changing when new sites are dynamically added or removed from the federation.In this application context, an efficient way to search and analyze passed behavior of products in use is desired. To enable scalable queries over collections of distributed and autonomous log databases we developed the FLOQ (Fused LOg database Query processor) system, which provides a global view of the working status of all machines on the sites through a meta-database integrating the dynamic log database collection. A particular challenge in this scenario is a scalable way to process numerical queries that identify anomalies by joining data from the metadatabase with data selected from the collection of distributed and autonomous log databases. The Thesis describes the architecture of FLOQ. In particular different strategies to execute numerical queries over log database collections are investigated. FLOQ allows both the meta-database and the log databases to be stored in multiple formats using different kinds of data managers. FLOQ provides general and extensible mechanisms for efficient processing of queries over different kinds of distributed data sources.

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Scientific SPARQL: Semantic Web Queries over Scientific Data

Cited by 17 publications

References 15 publications

Foundations of Modern Query Languages for Graph Databases

Foundations of Modern Query Languages for Graph Databases

Scientific Analysis by Queries in Extended SPARQL over a Scalable e-Science Data Store

Scalable Queries Over Log Database Collections

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