A query language for semantic complex event processing: Syntax, semantics and implementation

Gillani, Syed; Zimmermann, Antoine; Picard, Gauthier; Laforest, Frédérique

doi:10.3233/sw-180313

Cited by 14 publications

(14 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data stream processing systems have greatly influenced current RSP models, and the focus has been on extending existing processing models to query discrete portions of potentially infinite data streams. However, RSP has also been proposed as a candidate for bridging the gap between the Complex Event Processing (CEP) paradigm and the Semantic Web standards [10,33,36,46,108]. CEP focuses on detecting patterns between events based on, for example, temporal characteristics such as event co-occurrence and event sequences.…”

Section: Motivationmentioning

confidence: 99%

“…For example, EP-SPARQL includes a subset of the operators from Allen's temporal algebra (see Figure 2.1), and an extension of the CQELS query language has been created to provide support for the sequence operator [33]. A few language models even separate general graph pattern matching from event-pattern matching, which abstracts away the process of matching individual events from the detection of event patterns [36,46,87].…”

Section: Complex Event Processing Using Rspmentioning

confidence: 99%

See 1 more Smart Citation

Complex Event Processing under Uncertainty in RDF Stream Processing

Keskisärkkä

2021

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

The Semantic Web provides a framework for representing, sharing, and integrating data on the Web using a set of specifications promoted by the World Wide Web Consortium (W3C). These specifications include RDF as the model for data interchange on the Web and languages (e.g., RDFS and OWL) for defining schemas and ontologies. While the Semantic Web has traditionally focused on static or slowly changing data, information on the Web is becoming increasingly dynamic, with sources such as Internet-of-Things devices, sensor networks, smart cities, social media, and more. RDF Stream Processing (RSP) extends Semantic Web technologies to support streaming data and continuous queries and has been suggested as a candidate for bridging the gap between Complex Event Processing (CEP), which focuses on identifying meaningful events and event patterns from streaming data, and the Semantic Web standards.Systems that operate on real-world data must often deal with uncertainty, which can arise from, for example, missing information, incomplete domain knowledge, sensor noise, or linguistic vagueness. Uncertainty has received attention in both Semantic Web and CEP research, but little is known about how it can be managed in RSP and how it might impact performance.The contributions of this thesis are threefold. First, the issue of supporting a general model of CEP in RSP is addressed. A set of requirements for CEP is identified and used to define an event ontology for use in RSP. An approach is then proposed for creating a CEP framework that can scale processing beyond the limitations of a single RSP instance. Second, an extension of the RSP-QL data model is defined for representation of statement-level annotations. The data model is then used as a basis for capturing different types of uncertainty in a use case inspired by a research project in electronic healthcare. Finally, the performance impact of explicitly managing different types of uncertainty is evaluated in a prototype implementation and a set of optimization strategies is introduced with a goal of reducing the impact of uncertainty on query execution performance. The results show that the proposed approach to representing statement-level metadata reduces required data transfer bandwidth and that it can improve query execution performance compared with using RDF reification. The optimization strategies produce improved query execution performance overall, but the impact of the heuristic depends on multiple factors, including the selectivity of filters, join cardinalities, and the cost of evaluating uncertainty functions.

show abstract

Section: Motivationmentioning

confidence: 99%

Section: Complex Event Processing Using Rspmentioning

confidence: 99%

Complex Event Processing under Uncertainty in RDF Stream Processing

Keskisärkkä

2021

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

show abstract

“…These events can be formalized using a computer logic language, like Prolog, or an ontology language, like OWL and processed in order to reveal more complex events. To be more compliant with this specific field, a group of semantically rich languages can be used such as SPAseq [14] and RTEC calculus [15].…”

Section: Related Workmentioning

confidence: 99%

Detecting Search and Rescue Missions from AIS Data

Varlamis

Tserpes

Sardianos

2018

2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)

View full text Add to dashboard Cite

The crossing of the Mediterranean by refugees has turned to be an extremely perilous activity. Human operators that handle Search and Rescue (SAR) missions need all the help they can muster in order to timely discover and assist in the coordination of the operations. In this work we present a tool that automatically detects SAR missions in the sea, by employing Automatic Identification System (AIS) data streams. The approach defines three steps to be taken: a) trajectory compression for affordable real time analysis in the presence of big data; b) detection of sub-operations to which a SAR mission is actually decomposed, and; c) synthesis of multiple vessels' inferred behavior to determine an ongoing SAR mission and its details. The evaluation results are promising showing that AIS data carry highly valuable information even in the absence of any other type of data that could make the problem easier (e.g. coast guard signals).Keywords-search and rescue missions, AIS data. density based clustering, navigation pattern detection, trajectory mining, complex event processing.

show abstract

“…Gillani et al [ 24 ] extended the SPARQL query language to include CEP operators. However, reasoning is not taken into account.…”

Section: Related Workmentioning

confidence: 99%

Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams

Bonte

Tommasini

Valle

et al. 2018

Sensors

View full text Add to dashboard Cite

In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate.

show abstract

A query language for semantic complex event processing: Syntax, semantics and implementation

Cited by 14 publications

References 51 publications

Complex Event Processing under Uncertainty in RDF Stream Processing

Complex Event Processing under Uncertainty in RDF Stream Processing

Detecting Search and Rescue Missions from AIS Data

Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams

Contact Info

Product

Resources

About