Representing situational knowledge acquired from sensor data for atmospheric phenomena

Stocker, Markus; Baranizadeh, Elham; Portin, Harri; Komppula, Mika; Rönkkö, Mauno; Hamed, A.; Virtanen, Annele; Lehtinen, K. E. J.; Laaksonen, A.; Kolehmainen, Mikko

doi:10.1016/j.envsoft.2014.04.006

Cited by 15 publications

(14 citation statements)

References 17 publications

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“…Knowledge is then, specifically, situational knowledge. Applications in this context have been developed by Stocker et al (2014b) for vehicles detected and classified in road-pavement vibration data; Stocker et al (2014a) for atmospheric new particle formation detected and classified in data for particle size distribution of polydisperse aerosols; (Stocker et al, Plant disease pressure situation modelling in agriculture Computers and Electronics in Agriculture, in review) for plant disease pressure in agriculture computed from weather data using a physicallybased model; Clemente et al (2013) for collision avoidance of ships in harbour areas; Fenza et al (2010) for airport security; De Maio et al (2012) for intrusion detection in a video-surveilled area of a bank; Doulaverakis et al (2011) for security and surveillance. Relevant in this context are also theories of situation awareness (Endsley 1995;Stanton et al 2006).…”

Section: Discussionmentioning

confidence: 99%

“…Stocker et al (2014a) also discuss forms of situational knowledge processing. In contrast, the description here is limited to knowledge acquisition and curation.…”

Section: Subsystemsmentioning

confidence: 99%

“…The case is for the acquisition of situational knowledge for atmospheric new particle formation, from data for particle size distribution of polydisperse aerosols (Stocker et al 2013(Stocker et al , 2014a. We highlight the +K subsystems and functionality most relevant to the case, and discuss the case from the three +K viewpoints: science, information, and computational.…”

Section: Case Studymentioning

confidence: 99%

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Knowledge-based environmental research infrastructure: moving beyond data

2015

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Over the past decades, sensor networks have been deployed around the world to monitor over time and space a large number of properties appertaining to various environmental phenomena. A popular example is the monitoring of particulate matter and gases in ambient air undertaken, for instance, to assess air quality and inform decision makers and the public. Such infrastructure can generate large amounts of data, which must be processed to derive useful information. Infrastructure may be for environmental research, specifically. In order to reduce duplication and improve interoperability, efforts have been initiated more recently that aim at abstract architectural descriptions of infrastructure that supports the acquisition, curation, access, and processing of measurement and observation data. The ENVRI Reference Model (ENVRI-RM) is an example for an abstract architectural description of infrastructure tailored for environmental research. We briefly summarize ENVRI-RM and provide an overview of its subsystems, functionality, and viewpoints. We highlight that its primary focus is on the data life-cycle in environmental research infrastructure. As our contribution, we Communicated by: H. A. Babaie extend ENVRI-RM with functionality for the acquisition of knowledge from data, and the curation, access, and processing of knowledge. The extension, which we name +K, aims at addressing the knowledge life-cycle in environmental research infrastructure. We present the +K subsystems and functionality, and discuss the extension from ENVRI-RM viewpoints. We argue that the +K extension can be superimposed on ENVRI-RM to form the ENVRI-RM+K model for the 'archetypical' knowledge-based environmental research infrastructure that addresses both data and knowledge lifecycles. We demonstrate the application of the extension to a concrete use case in aerosol science.

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Section: Discussionmentioning

confidence: 99%

“…Stocker et al (2014a) also discuss forms of situational knowledge processing. In contrast, the description here is limited to knowledge acquisition and curation.…”

Section: Subsystemsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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Knowledge-based environmental research infrastructure: moving beyond data

2015

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“…4 FMI also provided a MATLAB script that implements an algorithm for the extraction of storm polygon data from radar data. Because the MMEA Platform uses Octave, 5 we implemented the MATLAB program logic as Octave script. Given a scheduled driver direction (consisting of departure time, origin, and destination) we use Google Directions 6 to obtain (alternative) routes, driving distance and estimated driving time.…”

Section: Methodsmentioning

confidence: 99%

“…First, together with our related work [5,6], we underscore the suitability of the notion of situation for the modelling, i.e. explicit representation, of situational knowledge obtained using models from processed data collected and managed by environmental monitoring systems, possibly building on environmental sensor networks.…”

Section: Introductionmentioning

confidence: 99%

A Software System for the Discovery of Situations Involving Drivers in Storms

Stocker

Kauhanen

Hiirsalmi

et al. 2015

IFIP Advances in Information and Communication Technology

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Part 3: Decision Support Tools and SystemsInternational audienceWe present an environmental software system that obtains, integrates, and reasons over situational knowledge about natural phenomena and human activity. We focus on storms and driver directions. Radar data for rainfall intensity and Google Directions are used to extract situational knowledge about storms and driver locations along directions, respectively. Situational knowledge about the environment and about human activity is integrated in order to infer situations in which drivers are potentially at higher risk. Awareness of such situations is of obvious interest. We present a prototype user interface that supports adding scheduled driver directions and the visualization of situations in space-time, in particular also those in which drivers are potentially at higher risk. We think that the system supports the claim that the concept of situation is useful for the modelling of information about the environment, including human activity, obtained in environmental monitoring systems. Furthermore, the presented work shows that situational knowledge, represented by heterogeneous systems that share the concept of situation, is relatively straightforward to integrate

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