A complex event processing approach to perceive the vehicular context

Terroso-Sáenz, Fernando; Valdés-Vela, Mercedes; Campuzano, Francisco; Botía, Juan A.; Skarmeta-Gómez, Antonio F.

doi:10.1016/j.inffus.2012.08.008

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…if E = 0 then (35) return E +1 (36) ← − 1 (37) return E (38) (39) function make prediction(E) (40) ← 0 (41) foreach ∈ (E) do (42) prob ← ∑ ∈E| . = .…”

Section: Experiments Setupmentioning

confidence: 99%

“…Due to the fact that such activities are organized in a tree structure (see Figure 4), it is possible to assess the similarity between two activities by means of a conceptual distance. For the current scope, we have used the Semantic-Hierarchical Similarity (SHS) [42] that is defined by the formula SHS ( , ) = CCA ( , ) .detail degree .detail degree + .detail degree ,…”

Section: Settingsmentioning

confidence: 99%

See 1 more Smart Citation

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

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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.

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“…if E = 0 then (35) return E +1 (36) ← − 1 (37) return E (38) (39) function make prediction(E) (40) ← 0 (41) foreach ∈ (E) do (42) prob ← ∑ ∈E| . = .…”

Section: Experiments Setupmentioning

confidence: 99%

Section: Settingsmentioning

confidence: 99%

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

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“…In order to provide a preliminary test of our proposal, we have made use of the CEP system proposed in [8] to discover the occupants of a vehicle by means of some of its built-in sensors. One of the EPRs of such system intends to perceive if there is a person in the driver seat.…”

Section: Target Scenariomentioning

confidence: 99%

Towards anticipate detection of complex event processing rules with probabilistic modelling

Terroso-Sáenz¹,

González-Vidal²,

Skarmeta³

2016

Int. J. DNE

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Nowadays, Big Data implies not only the need of processing high volume of data, but also do it in a timely manner. In this scope, the Complex Event Processing (CEP) paradigm has arisen as a prominent real-time rule-based solution. Due to its reactive nature, a CEP system might suffer from slight delays in the activation of its rules that could not be desirable in certain environments. As a result, the present work introduces a novel mechanism that intends to anticipate the activation of event-based rules and, thus, come up with even faster CEP systems. This is achieved by means of a probabilistic modelling of each rule's precondition. Finally, the proposal includes a preliminary evaluation so as to show its suitability.

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“…The field of information fusion for vehicular and driver safety has been explored in recent research [21,88,89]. Current active vehicle safety systems do not take context information into account, e.g., is the driver distracted or not?…”

Section: Context Fusionmentioning

confidence: 99%

Context Representation and Fusion: Advancements and Opportunities

Khattak

Akbar

Aazam

et al. 2014

Sensors

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The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

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A complex event processing approach to perceive the vehicular context

Cited by 28 publications

References 17 publications

Human Mobility Prediction Based on Social Media with Complex Event Processing

Human Mobility Prediction Based on Social Media with Complex Event Processing

Towards anticipate detection of complex event processing rules with probabilistic modelling

Context Representation and Fusion: Advancements and Opportunities

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