Indicators for smart cities: tax illicit analysis through data mining
The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to d...
- Autores:
-
Silva, Jesús
Solano, Darwin
Fernández, Claudia
Nieto Ramos, Lainet
Urdanegui, Rosella
Herz, Jeannette
Mercado, Alberto
Ovallos-Gazabon, David
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7707
- Acceso en línea:
- https://hdl.handle.net/11323/7707
https://doi.org/10.1007/978-981-15-7234-0_88
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data mining
Anomalous data
Algorithms
Automatic learning
Big data
Noise
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Indicators for smart cities: tax illicit analysis through data mining |
| title |
Indicators for smart cities: tax illicit analysis through data mining |
| spellingShingle |
Indicators for smart cities: tax illicit analysis through data mining Data mining Anomalous data Algorithms Automatic learning Big data Noise |
| title_short |
Indicators for smart cities: tax illicit analysis through data mining |
| title_full |
Indicators for smart cities: tax illicit analysis through data mining |
| title_fullStr |
Indicators for smart cities: tax illicit analysis through data mining |
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Indicators for smart cities: tax illicit analysis through data mining |
| title_sort |
Indicators for smart cities: tax illicit analysis through data mining |
| dc.creator.fl_str_mv |
Silva, Jesús Solano, Darwin Fernández, Claudia Nieto Ramos, Lainet Urdanegui, Rosella Herz, Jeannette Mercado, Alberto Ovallos-Gazabon, David |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Solano, Darwin Fernández, Claudia Nieto Ramos, Lainet Urdanegui, Rosella Herz, Jeannette Mercado, Alberto Ovallos-Gazabon, David |
| dc.subject.spa.fl_str_mv |
Data mining Anomalous data Algorithms Automatic learning Big data Noise |
| topic |
Data mining Anomalous data Algorithms Automatic learning Big data Noise |
| description |
The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms. |
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2021 |
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2021-01-18T17:35:25Z |
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2021-01-18T17:35:25Z |
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2021 |
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https://doi.org/10.1007/978-981-15-7234-0_88 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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1. Newton K, Norris P (2000) Confidence in public institutions, faith, culture and performance? In: Pharr SJ, Putnam RD (eds) Disaffected democracies Princeton. Princeton University Press, New Jersey, pp 52–73 2. León Medina FJ (2014) Mecanismos generadores de la confianza en la institución policial. Indret: Revista para el Análisis del Derecho 2:15–30 3. Liu B, Xu G, Xu Q, Zhang N (2012) Outlier detection data mining of tax based on cluster. In: 2012 international conference on medical physics and biomedical engineering (ICMPBE2012) 33 (Supplement C), pp 1689–1694. 4. Malone MFT (2010)The verdict is in: the impact of crime on public trust in Central American Justice Systems. J Politics Lat Am 2:99–128 5. Bedoya Velasco ÁY, Rojas Cruz AE, Sandoval Rozo O (2019) El derecho a la defensa técnica en el proceso jurisdiccional de cobro coactivo adelantado por la dirección de impuestos y aduanas nacionales de colombia 6. Bottia Rengifo, RE (2019) Apoyo en las actividades de los programas posconsumo en la coordinación de inventarios y almacén de la Unidad Administrativa Especial Dirección de Impuestos y Aduanas Nacionales (DIAN) 7. Lis-Gutiérrez JP, Reyna-Niño HE, Gaitán-Angulo M, Viloria A, Abril JES (2018) Hierarchical ascending classification: an application to contraband apprehensions in Colombia (2015–2016). In: International conference on data mining and big data, June 2018. Springer, Cham, pp 168–178 8. Amelec V, Carmen V (2015) Relationship between variables of performance social and financial of microfinance institutions. Adv Sci Lett 21(6):1931–1934 9. Romero R, Milena S, Torres González B (2019) Núcleo de apoyo contable y fiscal, NAF convenio DIAN Universidad Cooperativa de Colombia-campus Neiva 10. Godoy Godoy DL, González Gómez LA (2019) Big data para la priorización de zonas de atención a emergencias causadas por inundaciones en Bogotá Colombia: uso de las redes sociales 11. Becerra G, Alurralde JPL (2017) Big data y Data mining. Un analisis crítico acerca de su significación para las ciencias psicosociales a partir de un estudio de caso. {PSOCIAL} 3(2):66–85 12. Magnani E (2017) Big data y política: El poder de los algoritmos. Nueva sociedad, (269) 13. Segovia C, Haye A, González R, Manzi J (2008) Confianza en instituciones políticas en Chile: un modelo de los componentes centrales de juicios de confianza. Revista de Ciencia Política 28(2):39–60 14. Tankebe J (2008) Police effectiveness and police trustworthiness in Ghana: an empirical appraisal. Criminol Crim Justice 8:185–202 15. DANE (2019) Proyecciones de Población [database]. DANE, Bogotá 16. Rojas Nonzoque JD, Ramírez Barbosa N (2019) El Impuesto a la Renta y Complementarios en Colombia Desde el Punto de Vista del Contribuyente Persona Natural, Ley 1819 De 2016 17. Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: data mining toolbox in python. J Mach Learn Res 14(Aug):2349–2353 18. Viloria A, Neira-Rodado D, Pineda Lezama OB (2019) Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40 2019:1249–1254 19. Tarazona LTA, Gómez YH, Granados CM (2019) Caracterización y creación del manual de los procesos y procedimientos de importación de gráneles sólidos en el puerto marítimo de Buenaventura Colombia 20. Vargas D, Lineht L, Santis Criado AC (2019) Aplicación de las disposiciones tributarias actuales en las entidades sin ánimo de lucro en Colombia respecto al impuesto de renta y complementarios |
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Silva, JesúsSolano, DarwinFernández, ClaudiaNieto Ramos, LainetUrdanegui, RosellaHerz, JeannetteMercado, AlbertoOvallos-Gazabon, David2021-01-18T17:35:25Z2021-01-18T17:35:25Z2021https://hdl.handle.net/11323/7707https://doi.org/10.1007/978-981-15-7234-0_88Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The anomalies in the data coexist in the databases and in the non-traditional data that can be accessed and produced by a tax administration, whether these data are of internal or external origin. The analysis of certain anomalies in the data could lead to the discovery of patterns that respond to different causes, being able to evidence these causes certain illicit by taxpayers or acts of corruption when there is the connivance of the taxpayer with the public employee or public official. The purpose of this research is the theoretical development of the causal analysis of certain anomalies of tax data, demonstrating that the data mining methodology contributes to evidence of illicit and corrupt acts, through the application of certain algorithms.Silva, JesúsSolano, DarwinFernández, ClaudiaNieto Ramos, LainetUrdanegui, RosellaHerz, JeannetteMercado, AlbertoOvallos-Gazabon, Davidapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_88Data miningAnomalous dataAlgorithmsAutomatic learningBig dataNoiseIndicators for smart cities: tax illicit analysis through data miningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Newton K, Norris P (2000) Confidence in public institutions, faith, culture and performance? In: Pharr SJ, Putnam RD (eds) Disaffected democracies Princeton. Princeton University Press, New Jersey, pp 52–732. León Medina FJ (2014) Mecanismos generadores de la confianza en la institución policial. Indret: Revista para el Análisis del Derecho 2:15–303. Liu B, Xu G, Xu Q, Zhang N (2012) Outlier detection data mining of tax based on cluster. In: 2012 international conference on medical physics and biomedical engineering (ICMPBE2012) 33 (Supplement C), pp 1689–1694.4. Malone MFT (2010)The verdict is in: the impact of crime on public trust in Central American Justice Systems. J Politics Lat Am 2:99–1285. Bedoya Velasco ÁY, Rojas Cruz AE, Sandoval Rozo O (2019) El derecho a la defensa técnica en el proceso jurisdiccional de cobro coactivo adelantado por la dirección de impuestos y aduanas nacionales de colombia6. Bottia Rengifo, RE (2019) Apoyo en las actividades de los programas posconsumo en la coordinación de inventarios y almacén de la Unidad Administrativa Especial Dirección de Impuestos y Aduanas Nacionales (DIAN)7. Lis-Gutiérrez JP, Reyna-Niño HE, Gaitán-Angulo M, Viloria A, Abril JES (2018) Hierarchical ascending classification: an application to contraband apprehensions in Colombia (2015–2016). In: International conference on data mining and big data, June 2018. Springer, Cham, pp 168–1788. Amelec V, Carmen V (2015) Relationship between variables of performance social and financial of microfinance institutions. Adv Sci Lett 21(6):1931–19349. Romero R, Milena S, Torres González B (2019) Núcleo de apoyo contable y fiscal, NAF convenio DIAN Universidad Cooperativa de Colombia-campus Neiva10. Godoy Godoy DL, González Gómez LA (2019) Big data para la priorización de zonas de atención a emergencias causadas por inundaciones en Bogotá Colombia: uso de las redes sociales11. Becerra G, Alurralde JPL (2017) Big data y Data mining. Un analisis crítico acerca de su significación para las ciencias psicosociales a partir de un estudio de caso. {PSOCIAL} 3(2):66–8512. Magnani E (2017) Big data y política: El poder de los algoritmos. Nueva sociedad, (269)13. Segovia C, Haye A, González R, Manzi J (2008) Confianza en instituciones políticas en Chile: un modelo de los componentes centrales de juicios de confianza. Revista de Ciencia Política 28(2):39–6014. Tankebe J (2008) Police effectiveness and police trustworthiness in Ghana: an empirical appraisal. Criminol Crim Justice 8:185–20215. DANE (2019) Proyecciones de Población [database]. DANE, Bogotá16. Rojas Nonzoque JD, Ramírez Barbosa N (2019) El Impuesto a la Renta y Complementarios en Colombia Desde el Punto de Vista del Contribuyente Persona Natural, Ley 1819 De 201617. Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: data mining toolbox in python. J Mach Learn Res 14(Aug):2349–235318. Viloria A, Neira-Rodado D, Pineda Lezama OB (2019) Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40 2019:1249–125419. Tarazona LTA, Gómez YH, Granados CM (2019) Caracterización y creación del manual de los procesos y procedimientos de importación de gráneles sólidos en el puerto marítimo de Buenaventura Colombia20. Vargas D, Lineht L, Santis Criado AC (2019) Aplicación de las disposiciones tributarias actuales en las entidades sin ánimo de lucro en Colombia respecto al impuesto de renta y complementariosPublicationORIGINALIndicators for Smart Cities.pdfIndicators for Smart Cities.pdfapplication/pdf92614https://repositorio.cuc.edu.co/bitstreams/f9ca1273-e4e9-4d7d-b8df-e493def0cb86/downloadc80c35001743e84811d4c1805c1624a3MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/036c167e-4da6-4698-a29b-10f9f305eaaa/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/93cf6759-98b1-498c-b69c-b23057bf1f5b/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILIndicators for Smart Cities.pdf.jpgIndicators for Smart 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