The big data of violent events: algorithms for association analysis using spatio-temporal storytelling
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- 作者：Raimundo F. Dos Santos Jr. ; Arnold Boedihardjo ; Sumit Shah ; Feng Chen…
- 关键词：Spatial ; temporal systems ; Entity relationship modeling ; Social media networks ; Spatial and physical reasoning ; Semantic networks ; Big Data
- 刊名：GeoInformatica
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：20
- 期：4
- 页码：879-921
- 全文大小：6,632 KB
- 刊物类别：Earth and Environmental Science
- 刊物主题：Geography
Geographical Information Systems and Cartography
Data Structures, Cryptology and Information Theory
Computer Science, general
Information Storage and Retrieval
Multimedia Information Systems - 出版者：Springer Netherlands
- ISSN：1573-7624
- 卷排序：20
文摘
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.