SAT: Spatial Awareness from Textual Input

Kalashnikov, Dmitri V.; Ma, Yiming; Mehrotra, Sharad; Hariharan, Ramaswamy; Venkatasubramanian, Nalini; Ashish, Naveen

doi:10.1007/11687238_82

Cited by 7 publications

(8 citation statements)

References 3 publications

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“…From these reports, 2359 uncertain locations are extracted and mapped into the probabilistic representation, using the framework from [8]. The number of the locations might not be sufficient for testing the scalability.…”

Section: Methodsmentioning

confidence: 99%

“…We will use the probabilistic model for spatial uncertainty developed in [3,[5][6][7][8], because it builds on the formal probability theory and it has been shown to be very effective in practice. Similar models are also employed in [2,4].…”

Section: Related Workmentioning

confidence: 99%

“…Representation. In many applications that deal with uncertain event locations [8,11], we need to be able to represent pdfs of complex shapes in the database. There are several known methods for such a representation, such as histograms and modeling pdf as a mixture of Gaussians or of other distributions.…”

Section: Related Workmentioning

confidence: 99%

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Index for fast retrieval of uncertain spatial point data

Kalashnikov

Mehrotra

et al. 2006

Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems

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Location information gathered from a variety of sources in the form of sensor data, video streams, human observations, and so on, is often imprecise and uncertain and needs to be represented approximately. To represent such uncertain location information, the use of a probabilistic model that captures the imprecise location as a probability density function (pdf) has been recently proposed. The pdfs can be arbitrarily complex depending on the type of application and the source of imprecision. Hence, efficiently representing, storing and querying pdfs is a very challenging task. While the current state of the art indexing approaches treat the representation and storage of pdfs as a black box, in this paper, we take the challenge of representing and storing any complex pdf in an efficient way. We further develop techniques to index such pdfs to support the efficient processing of location queries. Our extensive experiments demonstrate that our indexing techniques significantly outperform the best existing solutions.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Index for fast retrieval of uncertain spatial point data

Kalashnikov

Mehrotra

et al. 2006

Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems

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“…Also, spatial expressions in natural language are rarely precise (e.g. the library is located in the centre of the town; he is moving towards the cinema) [28]; in other words, they usually do not provide enough information to identify the exact geographical location of an object or event [36]. Abstract, noncoordinate-based methods are necessary to deal with these uncertainties [20].…”

Section: Qualitative Versus Quantitative Questionsmentioning

confidence: 99%

Inferring additional knowledge from QTCN relations

Delafontaine

Bogaert

Cohn

et al. 2011

Information Sciences

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Abstract. It is widely held that people tend to use qualitative rather than quantitative phrases when raising or answering questions about moving objects. Queries about whether an object is moving towards or away from another object or whether objects are getting closer to each other or further away from each other, require qualitative responses. This characteristic should be reflected in a calculus to be used to describe and reason about continuously moving objects. In this paper, we present a qualitative trajectory calculus of relations between two disjoint moving objects, whose movement is constrained by a network. The proposed calculus (QTC N ) is formally introduced and illustrated. Particular attention is placed on how to infer additional knowledge from QTC N relations by means of composition tables and the transformation of QTC N relations into relations defined by the Relative Trajectory Calculus on Networks (RTC N ).

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“…Developing effective SA systems has the potential to radically improve decision support in crises by improving the accuracy and reliability of the information available to the decision-makers [2], [22], [25], [26], [27]. In this paper, we study the problem of representing and querying uncertain location information about real-world events that are described using free text.…”

Section: Introductionmentioning

confidence: 99%

Toward Managing Uncertain Spatial Information for Situational Awareness Applications

Kalashnikov

Mehrotra

2008

IEEE Trans. Knowl. Data Eng.

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Abstract-Situational awareness (SA) applications monitor the real world and the entities therein to support tasks such as rapid decision making, reasoning, and analysis. Raw input about unfolding events may arrive from variety of sources in the form of sensor data, video streams, human observations, and so on, from which events of interest are extracted. Location is one of the most important attributes of events, useful for a variety of SA tasks. In this paper, we consider the problem of reaching situation awareness from textual input. We propose an approach to probabilistically model and represent (potentially uncertain) event locations described by human reporters in the form of free text. We analyze several types of spatial queries of interest in SA applications. We design techniques to store and index the uncertain locations, to support the efficient processing of queries. Our extensive experimental evaluation over real and synthetic data sets demonstrates the effectiveness and efficiency of our approach.

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SAT: Spatial Awareness from Textual Input

Cited by 7 publications

References 3 publications

Index for fast retrieval of uncertain spatial point data

Index for fast retrieval of uncertain spatial point data

Inferring additional knowledge from QTCN relations

Toward Managing Uncertain Spatial Information for Situational Awareness Applications

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