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

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

doi:10.1007/s10707-016-0247-0

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…To solve the problem of MPP for a cloud, a middleware design has been built as an open-source model named GPCloud. According to [55], big data is a challenge, which is a difficult problem to be addressed in real world; a solution is anticipated based on the association analysis by storylines. The first method is storytelling and matching the dots by distance-based Bayesian inference, which includes spatial data for finding similar events; the second method is inference and forecasting of spatial association index and last method is a link analysis using spatio-logical inference for calculating the storylines in diverse positions, limited data volumes to forced districts, the concluding powerful event with filtering unrelated events and the means of events to aggregate big data.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Aspects of Big Data

Karim

Soomro

Burney

2018

Applied Computer Systems

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Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.

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Section: Literature Reviewmentioning

confidence: 99%