The big data of violent events: algorithms for association analysis using spatio-temporal storytelling View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-10

AUTHORS

Raimundo F. Dos Santos, Arnold Boedihardjo, Sumit Shah, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan

ABSTRACT

This paper proposes three methods of association analysis that address two challenges of Big Data: capturing relatedness among real-world events in high data volumes, and modeling similar events that are described disparately under high data variability. The proposed methods take as input a set of geotemporally-encoded text streams about violent events called “storylines”. These storylines are associated for two purposes: to investigate if an event could occur again, and to measure influence, i.e., how one event could help explain the occurrence of another. The first proposed method, Distance-based Bayesian Inference, uses spatial distance to relate similar events that are described differently, addressing the challenge of high variability. The second and third methods, Spatial Association Index and Spatio-logical Inference, measure the influence of storylines in different locations, dealing with the high-volume challenge. Extensive experiments on social unrest in Mexico and wars in the Middle East showed that these methods can achieve precision and recall as high as 80 % in retrieval tasks that use both keywords and geospatial information as search criteria. In addition, the experiments demonstrated high effectiveness in uncovering real-world storylines for exploratory analysis. More... »

PAGES

879-921

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10707-016-0247-0

DOI

http://dx.doi.org/10.1007/s10707-016-0247-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017785155


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