Copyright and Licensing
Articles accepted for publication will be licensed under the Creative Commons BY-NC. Authors must sign a non-exclusive distribution agreement after article acceptance.
Experiences from a Data Analysis of Crimes against Humanity
DOI:
https://doi.org/10.24215/16666038.21.e3Keywords:
Data Analysis, ETL Process, Spatial Data Mining, Crimes Against HumanityAbstract
Data analysis is a widely researched field, where innumerable applications allow to discover domain particularities that are specially useful. In this paper, we introduce the data analysis process that we applied to two different systems storing information about statements and testimonies of crimes against Humanity. We describe the activities, design decisions and lessons learned from implementing a specific goal, which involves transforming text data into georeferenced information.
Downloads
Download data is not yet available.
References
P. Neelamadhab, M. Pragnyaban, and P. Rasmita, “The survey of data mining applications and feature scope,” International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), vol. 2, 11 2012.
D. Troncoso, A. Buccella, and A. Cechich, “Decisiones y lecciones aprendidas en un proceso etl aplicado a sistemas con testimonios de delitos de lesa humanidad,” in Proceedings of the CACIC’20: XXVI Congreso Argentino de Ciencias de la Computaci´on, (Universidad Nacional de La Matanza), RedUnci, 2020.
S. Luj´an-Mora, P. Vassiliadis, and J. Trujillo, “Data mapping diagrams for data warehouse design with uml,” in Conceptual Modeling – ER 2004 (P. Atzeni, W. Chu, H. Lu, S. Zhou, and T.-W. Ling, eds.), (Berlin, Heidelberg), pp. 191–204, Springer Berlin Heidelberg, 2004.
A. Simitsis, D. Skoutas, and M. Castellanos, “Natural language reporting for etl processes,” in Proceedings of the ACM 11th International Workshop on Data Warehousing and OLAP, DOLAP ’08, (New York, NY, USA), p. 65–72, Association for Computing Machinery, 2008.
J. Trujillo and S. Luj´an-Mora, “A uml based approach for modeling etl processes in data warehouses,” in Conceptual Modeling - ER 2003 (I.-Y. Song, S. W. Liddle, T.-W. Ling, and P. Scheuermann, eds.), (Berlin, Heidelberg), pp. 307–320, Springer Berlin Heidelberg, 2003.
P. Vassiliadis, A. Simitsis, and S. Skiadopoulos, “Conceptual modeling for etl processes,” in Proceedings of the 5th ACM International Workshop on Data Warehousing and OLAP, DOLAP ’02, (New York, NY, USA), p. 14–21, Association for Computing Machinery, 2002.
Z. El Akkaoui, J.-N. Maz´on, A. Vaisman, and E. Zim´anyi, “Bpmn-based conceptual modeling of etl processes,” in Data Warehousing and Knowledge Discovery (A. Cuzzocrea and U. Dayal, eds.), (Berlin, Heidelberg), pp. 1–14, Springer Berlin Heidelberg, 2012.
J.-N. Maz´on, E. Zim´anyi, Z. El Akkaoui, and J. Trujillo, “A bpmn-based design and maintenance framework for etl processes,” Int. J. Data Warehous. Min., vol. 9, p. 46–72, July 2013.
D. Skoutas, A. Simitsis, and T. Sellis, Ontology-Driven Conceptual Design of ETL Processes Using Graph Transformations, pp. 120–146. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
A. Simitsis, “Mapping conceptual to logical models for etl processes,” in Proceedings of the 8th ACM International Workshop on Data Warehousing and OLAP, DOLAP ’05, (New York, NY, USA), p. 67–76, Association for Computing Machinery, 2005.
M. Niinim¨aki and T. Niemi, An ETL Process for OLAP Using RDF/OWL Ontologies, pp. 97–119. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
M. Perumal, B. Velumani, A. Sadhasivam, and K. Ramaswamy, “Spatial data mining approaches for gis – a brief review,” in Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2 (S. C. Satapathy, A. Govardhan, K. S. Raju, and J. K. Mandal, eds.), (Cham), pp. 579–592, Springer International Publishing, 2015.
S. Tay, W. Hsu, K. Lim, and L. Yap, “Spatial data mining: Clustering of hot spots and pattern recognition,” vol. 6, pp. 3685 – 3687 vol.6, 08 2003.
T. H. Grubesic, “On the application of fuzzy clustering for crime hot spot detection,” Journal of Quantitative Criminology, vol. 22, no. 1, pp. 77–105, 2006.
R. T. Ng and J. Han, “Efficient and effective clustering methods for spatial data mining,” in Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, (San Francisco, CA, USA), p. 144–155, Morgan Kaufmann Publishers Inc., 1994.
I. S. Sitanggang, T. Fuad, and Annisa, “K-means clustering visualization of web-based olap operations for hotspot data,” in 2010 International Symposium on Information Technology, vol. 1, pp. 1–4, 2010.
L. Fattouh and M. Alharbi, “Using modified partitioning around medoids clustering technique in mobile network planning,” International Journal of Computer Science Issues, vol. 9, 02 2013.
M. B. C. Imas Sukaesih Sitanggang and Shofyan, “Data mining approach for outlier detection on hotspot data as forest and land fire indicator: A case study in riau province indonesia,” ARPN Journal of Engineering and Applied Sciences, vol. 12, no. 13, 2017.
Downloads
Published
2021-04-17
How to Cite
Manrique, D. ., Troncoso, D. ., Buccella, A., & Cechich, A. (2021). Experiences from a Data Analysis of Crimes against Humanity. Journal of Computer Science and Technology, 21(1), e3. https://doi.org/10.24215/16666038.21.e3
Issue
Section
Original Articles
License
Copyright (c) 2021 Agustina Buccella, Daniela Manrique, David Troncoso, Alejandra Cechich
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
