Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM)

Hage, Willem Robert van; Malaisé, Veronique; Vries, Gerben Klaas Dirk de; Schreiber, Guus; Someren, Maarten W. van

doi:10.1007/s11042-010-0680-2

Cited by 22 publications

(16 citation statements)

References 22 publications

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“…6 http://www.getty.edu/research/conducting/research/vocabularies/ tgn/ 7 http://www.w3.org/TR/swbp-specified-values/ sem:Type classes provides a placeholder to define these patterns. An example of Rector's pattens applied to SEM can be found in Section 2 of [9]. Also, having an explicit sem:Type class makes it easier to define applications like facet browsers on top of SEM.…”

Section: Classesmentioning

confidence: 99%

Design and use of the Simple Event Model (SEM)

Hage

Malaisé

Segers

et al. 2011

Journal of Web Semantics

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206

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a b s t r a c tEvents have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for Web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.

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Section: Classesmentioning

confidence: 99%

Design and use of the Simple Event Model (SEM)

Hage

Malaisé

Segers

et al. 2011

Journal of Web Semantics

Self Cite

206

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“…As an example of a direct translation of a database row, one could directly address the fish detection Second, to transform the event data to a higher abstraction we will map the typhoon event data and the fish behavior events to a number of common RDF event models such as F [50], SEM [67] and LODE [54]. These event-centric representations should also simplify aligning our data with other Linked Open Data, such as typhoon events that are part of DBPedia.…”

Section: Discussionmentioning

confidence: 99%

Understanding fish behavior during typhoon events in real-life underwater environments

Spampinato

Palazzo

Boom

et al. 2012

Multimed Tools Appl

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The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan's coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting This research was funded by European Commission FP7 grant 257024, for the Fish4Knowledge project (www.fish4knowledge.eu). Appl (2014) 70:199-236 aspects from the biologists' point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of "typhoon" events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.

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“…Another field in which the use of complex reasoning over time-annotated data has been investigated is Maritime Safety and Security [21], where sensor data from ships is combined with external data to derive new knowledge. This scenario uses temporal relations and patterns of changes to extract higher level knowledge from low level data collected from sensors.…”

Section: Motivating Scenariosmentioning

confidence: 99%

Streaming the Web: Reasoning Over Dynamic Data

Margara¹,

Urbani²,

Harmelen³

et al. 2014

SSRN Journal

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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.

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Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM)

Cited by 22 publications

References 22 publications

Design and use of the Simple Event Model (SEM)

Design and use of the Simple Event Model (SEM)

Understanding fish behavior during typhoon events in real-life underwater environments

Streaming the Web: Reasoning Over Dynamic Data

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