Abstract
Big data analysis and application is an efficient approach for analyzing and identifying different patterns, relations and trends in daily life. In this paper we proposed an intelligent big data platform for information sharing in connection to administrative law and criminal justice. We first explored the structure of the data and utilized Apache Pig and Hadoop to handle structured, semi-structured and unstructured data. We extracted and transformed useful features from data and delivered and stored them in database using Cassandra and Zookeeper. After obtaining required features we applied machine learning and neural network algorithms in the data sets, to classify or mine potential knowledge. Finally we stored the results in MongoDB in which all staff from law enforce departments can have access to them through Web APP. We have applied several state-of-the-art data mining techniques and big data analytic tools that are specifically used for data processing and feature extraction. The experimental results show that our system is efficient in improving the filing rate, and also time-saving in processing large number of data. The promising outcomes will be beneficial for administrative enforcement and law enforcement to speed up the process of solving law cases and provide insights that enable them track case activities, predict the likelihood of warnings, effectively deploy resources and make faster decisions.
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References
Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47 (2013)
Yacchirema, D., Sarabia-Jácome, D., et al.: A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access 6, 35988–36001 (2018)
Zakir, J., Seymour, T.: Big data analytics. Issues Inf. Syst. 16(2) (2015)
Bench-Capon, T.: Argument in artificial intelligence and law. Artif. Intell. Law 5(4), 249–261 (1997)
Hoeschl, H.C., Barcellos, V.: Artificial intelligence and law. In: Bramer, M., Devedzic, V. (eds.) AIAI 2004. IIFIP, vol. 154, pp. 25–34. Springer, Boston (2004). https://doi.org/10.1007/1-4020-8151-0_3
Reingold, B., Mrazik, R.: Cloud computing: whose law governs the cloud? (Part III). LegalWorks (2010)
HMRC homepage: https://www.gov.uk/government/organisations/hm-revenue-customs
IPAS Homepage. http://www.wipo.int/tad/en/activitysearchresult.jsp?vcntry=JO
Poole, B., Johnson, S.: An overview of automated enforcement systems and their potential for improving pedestrian and bicyclist safety (2017)
Blomberg, T., Bales, W., et al.: Validation of the COMPAS risk assessment classification instrument. College of Criminology and Criminal Justice, Florida State University (2010)
Wang, Z., Ren, J., et al.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287, 68–83 (2018)
Ren, J., Jiang, J.: Hierarchical modeling and adaptive clustering for real-time summarization of rush videos. IEEE Trans. Multimed. 11(5), 906–917 (2009)
Han, J., Zhang, D., et al.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote Sens. 53(6), 3325–3337 (2015)
Chen, J., Ren, J.: Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos. Multimed. Tools Appl. 54(2), 219–239 (2011)
Ren, J., Vlachos, T.: Immersive and perceptual human–computer interaction using computer vision techniques. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 66–72. IEEE (2010)
Yan, Y., Ren, J., et al.: Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos. Cognit. Comput. 10, 1–11 (2017)
Yan, Y., Ren, J., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recognit. 79, 65–78 (2018)
Acknowledgements
This work has been supported by HJSW and Research & Development plan of Shaanxi Province (Program No. 2017ZDXM-GY-094,2015KTZDGY04-01) and Central Fund of High Education, The Legal Issue of Silk Road (Program No. 3102017JC19003).
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Li, N., Zheng, J., Feng, M. (2018). A Big Data Analytics Platform for Information Sharing in the Connection Between Administrative Law and Criminal Justice. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_64
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DOI: https://doi.org/10.1007/978-3-030-00563-4_64
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