Management of Diffused Information in Databases Using Similarity Measures
Abstract The different subjective concept that people have when dealing with similar data makes the transcription of information a problem in many police databases. Concepts such as colour or height are actually fuzzy values, as there are different degrees within each of these values. The collected information is often stored with discrete values, when in reality they are a representation of information that could be called, following concepts used in sciences dealing with information management fuzzy. When transforming fuzzy information into these discrete values, information can be lost or even misrepresented if it is not properly valued and the certainty of the source or the training of the person in charge of managing it is not checked. Within mathematics, the study of fuzzy logic attempts to establish relationships between these values in order to provide a solution to the problem at hand. Similarity relations are perhaps the best measure to be able to properly assess the existing data and help researchers. In this article, three case studies are presented: fuzzy information, partial information and misinformation. Examples of colour processing in databases, the evaluation of number plates with partial data or the detection of anomalies in people’s records will be used for this purpose.
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Codd, E. F. (1990). The relational model for database management: Versión 2. Addison-Wesley Longman Publishing Co. Inc.
Cordero, P., Enciso, M., Mora, A., De Guzmán, I. P. y Rodríguez-Jiménez, J. M. (2011). Specification and inference of fuzzy attributes. En Lectures Notes in Computer Science, 6692, 492-499). Springer. https://doi.org/10.1007/978-3-642-21498-1_52
Cuadras, C. M., Arenas, C. y Fortiana, J. (1996). Some computational aspects of a distance-based model for prediction. Communications in Statitstics – Simuation and Computation, 25(2), 395-410. https://doi.org/10.1080/03610919608813336
Ester, M., Kriegel, H. P., Sander, J., y Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. En Proceedings of the Second International Conference on Knowledge Discovery and Data Mining KDD-96, 226-231. AAAI
Ganter, B., Stumme, G. y Wille, R. (2005). Formal Concept Analysis. Foundations and Applications. Berlin Heidelberg. Springer. https://doi.org/10.1007/11528784
Levenshtein, V. I. (1965). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10(8), 707-710.
Mélida Lledó, P. L. (2023). Retos de la investigación científico-forense. Ciencia Policial, 179, 11-43.
Rodríguez-Jiménez, J. M., Cordero, P., Enciso, M. y Mora, A. (2014). Negative attributes and implications in formal concept analysis. Procedia Computer Science, 31, 1054-1063. https://doi.org/10.1016/j.procs.2014.05.373
Walsh, J. K. (2006). RGB to color name mapping (triplet and hex). Recuperado el 25 de junio de 2025 de https://web.njit.edu/~walsh/rgb.html
Zadeh, L. A (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1971). Similarity relations and fuzzy orderings. Information Sciences, 3(2), 177-200. https://doi.org/10.1016/0020-0255(71)90012-0
Zadeh, L. A. (1992). Knowledge representation in fuzzy logic. En An introduction to fuzzy logic applications in intelligent systems. Springer.
Zadeh, L. A. (1996). Fuzzy logic=computing with words. IEEE transactions on fuzzy systems, 4(2), 103-111. https://doi.org/10.1109/91.485416
Rodríguez-Jiménez, J. M. (2026). Management of Diffused Information in Databases Using Similarity Measures. Ciencia Policial, 185, 69–104. https://doi.org/10.14201/cp.32374
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