Implementing a Practical Spatio-Temporal Composite Event Language

Moody, Ken; Bacon, Jean; Evans, David; Schwiderski-Grosche, Scarlet

doi:10.1007/978-3-642-17226-7_7

Cited by 6 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates that these two landmarks were not visited one after another before. If that is the case, the function just returns the set of candidate edges generated so far just before processing such pair of landmarks (lines [34][35]. This way, the function is able to provide a set of candidate edges even though the whole sequence of landmarks in R is not fully included in G .…”

Section: Location Prediction Each Time the Route Composer Epr Enlargmentioning

confidence: 99%

“…Despite CEP's widespread usage, there exists a scarcity of CEP solutions which deal with spatiotemporality since only a few works actually propose practical CEP applications able to process spatiotemporal data [34]. In that sense, [35] devises a formal framework to timely detect spatiotemporal relationships between moving entities.…”

Section: Related Workmentioning

confidence: 99%

“…(CEP) is an evolution of the former publish/subscribe model in order to deal with more complex subscription patterns, so it can be regarded as a relatively recent technology [7]. Despite CEP's widespread usage, there exists a scarcity of CEP solutions which deal with spatiotemporality since only a few works actually propose practical CEP applications able to process spatiotemporal data [34]. In that sense, [35] devises a formal framework to timely detect spatiotemporal relationships between moving entities.…”

Section: Complex Event Processing Complex Event Processingmentioning

confidence: 99%

“…Since each route in a graph is reflected as a sequence of exclusive edges, this detection is done by searching the maximum set of edges that connect, in the same order, R landmarks in G . This task is carried out by the function select edges (lines [23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Location Prediction Each Time the Route Composer Eprmentioning

confidence: 99%

See 3 more Smart Citations

Human Mobility Prediction Based on Social Media with Complex Event Processing

Terroso-Sáenz

Cuenca-Jara

González-Vidal

et al. 2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The combination of mobile and social media sensors is foreseen to become a crucial course of action so as to comprehensively capture and understand the movement of people in large spatial regions. In that sense, the present work describes a novel personal location predictor that makes use of these two types of sensors. Firstly, it extracts the mobility models of an area capturing aspects related to particular users along with crowd-based features on the basis of geotagged tweets. Unlike previous approaches, the proposed solution mines such models in an online manner so that no previous off-line training is required. Then, on the basis of such models, a predictor able to forecast the next activity and position of a user is developed. Finally, the described approach is tested by using Twitter datasets from two different cities.

show abstract

Section: Location Prediction Each Time the Route Composer Epr Enlargmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Complex Event Processing Complex Event Processingmentioning

confidence: 99%

Section: Location Prediction Each Time the Route Composer Eprmentioning

confidence: 99%

See 2 more Smart Citations

Human Mobility Prediction Based on Social Media with Complex Event Processing

Terroso-Sáenz

Cuenca-Jara

González-Vidal

et al. 2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…And four complex event operators: same location and sequence, remote and sequence, same location and concurrency, remote and concurrency. They have used SpaTec language in the monitoring system of London bus and propose the system architecture [14]. Jin Beihong has proposed a complex event Query language: CPSL [15].…”

Section: Related Workmentioning

confidence: 99%

STECEQL: A Spatio-Temporal Constraint Event Query Language for Internet of Vehicles

2014

Int. j. comput. netw. commun. secur.

View full text Add to dashboard Cite

In recent years, the complex event technology has been widely used in the monitoring and real-time querying information of the internet of things. The internet of vehicles is a novel researching area of the intelligent transportation systems, which developed with the technology of internet of things. It is different from the traditional internet of things. There are a large number of moving objects and they will produce large amounts of temporal and spatial information in the internet of vehicles. It becomes the core issue of the complex event technology in the internet of vehicles, how to effectively represent and process these information of the moving objects. We propose a novel temporal and spatial constraint complex event query language STeCEQL for the internet of vehicles. In STeCEQL, we use time interval as temporal model and grid map as spatial models. We give the syntax and operational semantics of STeCEQL based on the temporal and spatial model. Finally, our experiments illustrate the effectiveness of the operational semantics.

show abstract

Deployment strategies for distributed complex event processing

Cugola

Margara

2012

Computing

View full text Add to dashboard Cite

Implementing a Practical Spatio-Temporal Composite Event Language

Cited by 6 publications

References 19 publications

Human Mobility Prediction Based on Social Media with Complex Event Processing

Human Mobility Prediction Based on Social Media with Complex Event Processing

STECEQL: A Spatio-Temporal Constraint Event Query Language for Internet of Vehicles

Deployment strategies for distributed complex event processing

Contact Info

Product

Resources

About