Abstract
In the big data era, handling the volume, velocity and variety of data is the prime requirement for analyzing an event. This paper presents our work for interactive visual analysis software with comprehensive format data input to solve such issues. There are three subsystems to process different types and formats of public and personal data at the same time. A detailed case study shows that the tool efficiently finds the target people and location from various data sources without any offline training or manual search.
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References
Laney, D.: 3-D data management: controlling data volume, velocity and variety. META Group Research note, 6 February 2001
Thomas, J., Cook, K.: A visual analytics agenda. IEEE Comput. Graphics Appl. 26, 10–13 (2006)
Keim, D., et al.: Visual analytics: how much visualization and how much analytics? ACM SIGKDD Explor. Newsl. 11(2), 5–8 (2009)
Dou, W., et al.: LeadLine: interactive visual analysis of text data through event, identification and exploration. In: IEEE Conference on Visual Analytics Science and Technology 2012, Seattle, WA, 14–19 October 2012
Wanner, F., et al.: State-of-the-art report of visual analysis for event detection in text data streams. http://bib.dbvis.de/uploadedFiles/3_submission.pdf
Slingsby, A., et al.: Visual analysis of social networks in space and time. Mobile Data Challenge Workshop 2012, Newcastle (2012)
Criminal analysis: new prospects for investigation using i2 software. https://visualanalysis.com/Downloads/CaseStudies/ANB-IBASEOCRVP_UK_Q2%202011_ICP_Low.pdf
Top law enforcement software tools. http://www.capterra.com/law-enforcement-software/
Hogenboom, F., et al.: An overview of event extraction from text. In: van Erp, M., et al. (eds.) Proceedings of Detection, Representation, and Exploitation of Events in the Semantic Web, pp. 48–57, Bonn (2011)
Kandel, S., Paepcke, A., Hellerstein, J.M., Heer, J.: Enterprise data analysis and visualization: an interview study. IEEE Trans. Visual Comput. Graphics 18(12), 2917–2926 (2012)
Kietz, J.U., Maedche, A., Volz, R.: A method for semi-automatic ontology acquisition from a corporate intranet. In: EKAW-2000 Workshop “Ontologies and Text”, Juan-Les-Pins (2000)
Arazy, O., Woo, C.: Enhancing information retrieval through statistical natural language processing: a study of collocation indexing. MIS Q. 31(3), 525–546 (2007)
Data Driven Documents. http://d3js.org/
VAST Challenge (2014). http://www.vacommunity.org/VAST+Challenge+2014
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Park, J. et al. (2015). Interactive Visual Analysis for Comprehensive Dataset. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_46
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DOI: https://doi.org/10.1007/978-3-319-23989-7_46
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