Modeling and Querying Metadata in the Semantic Sensor Web: The Model stRDF and the Query Language stSPARQL

Koubarakis, Manolis; Kyzirakos, Kostis

doi:10.1007/978-3-642-13486-9_29

Cited by 126 publications

(122 citation statements)

References 16 publications

Supporting

Mentioning

120

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: A4 Further Extensionsmentioning

confidence: 99%

Foundations of Modern Query Languages for Graph Databases

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Similar approaches for including time into RDF, as well as proposals for time and context-aware query languages, have been explored in [101][102][103][104][105][106][107]. All these solutions lack the support for continuous queries.…”

Section: Time-aware Reasoningmentioning

confidence: 99%

Streaming the Web: Reasoning over dynamic data

Margara

Urbani

Harmelen

et al. 2014

Journal of Web Semantics

111

View full text Add to dashboard Cite

a b s t r a c tIn the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning.

show abstract

“…Within the database community, extensive research has been conducted on two notions of time: valid time (when a change occurred in the real world) and transaction time (when a change was entered to the database) [22]. Various proposals adapting the notion of valid time have been made by the Linked Data community, such as temporal RDF graphs (temporal reification vocabulary) [16,15], multidimensional RDF (extended triple notion) [10], applied temporal RDF (named graphs) [43], stRDF (temporal quad) [25], RDF SpatialTemporalThematic (based on temporal graphs) [34], and temporal quintuples [26].…”

Section: Extensions Of Rdf(s)mentioning

confidence: 99%

Linked Data and Time – Modeling Researcher Life Lines by Events

Trame

Keβler

Kuhn

2013

Spatial Information Theory

View full text Add to dashboard Cite

Abstract. Most datasets on the Linked Data Web impose a static view on the represented entities and relations between them, neglecting temporal aspects of the reality they represent. In this paper, we address the representation of resources in their spatial, temporal and thematic context. We review the controversial proposals for the representation of time-dependent relations on the Linked Data Web. We argue that representing and using such relations is made hard through the direct encoding of inadequate conceptualizations, rather than through inherent limitations of the representation language RDF. Using the example of researcher life lines extracted from curricula vitae, we show how to model sequences of activities in terms of events. We build upon the event participation pattern from the DOLCE Ultralite+DnS Ontology and show how places and social roles that people play during their careers relate to events. Furthermore, we demonstrate how scientific achievements can be related to events in a career trajectory by means of temporal reasoning.

show abstract

Modeling and Querying Metadata in the Semantic Sensor Web: The Model stRDF and the Query Language stSPARQL

Cited by 126 publications

References 16 publications

Foundations of Modern Query Languages for Graph Databases

Foundations of Modern Query Languages for Graph Databases

Streaming the Web: Reasoning over dynamic data

Linked Data and Time – Modeling Researcher Life Lines by Events

Contact Info

Product

Resources

About