Unsupervised learning models-based CRM anomaly detection using GPU
Deep learning models have improved several business intelligence tools like Customer relationship Management(CRM) systems. However, those models have increased the need for advanced computational capacity and infrastructure. Modern accelerators are starting to have floating-point precision arithmeti...
- Autores:
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/20764
- Acceso en línea:
- http://hdl.handle.net/20.500.12010/20764
http://expeditio.utadeo.edu.co
- Palabra clave:
- Analítica de datos
Análisis de sistemas -- Tesis y disertaciones académicas
Teoría de sistemas -- Tesis y disertaciones académicas
Diseño de sistemas -- Tesis y disertaciones académicas
- Rights
- License
- Abierto (Texto Completo)
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Unsupervised learning models-based CRM anomaly detection using GPU |
| title |
Unsupervised learning models-based CRM anomaly detection using GPU |
| spellingShingle |
Unsupervised learning models-based CRM anomaly detection using GPU Analítica de datos Análisis de sistemas -- Tesis y disertaciones académicas Teoría de sistemas -- Tesis y disertaciones académicas Diseño de sistemas -- Tesis y disertaciones académicas |
| title_short |
Unsupervised learning models-based CRM anomaly detection using GPU |
| title_full |
Unsupervised learning models-based CRM anomaly detection using GPU |
| title_fullStr |
Unsupervised learning models-based CRM anomaly detection using GPU |
| title_full_unstemmed |
Unsupervised learning models-based CRM anomaly detection using GPU |
| title_sort |
Unsupervised learning models-based CRM anomaly detection using GPU |
| dc.subject.spa.fl_str_mv |
Analítica de datos |
| topic |
Analítica de datos Análisis de sistemas -- Tesis y disertaciones académicas Teoría de sistemas -- Tesis y disertaciones académicas Diseño de sistemas -- Tesis y disertaciones académicas |
| dc.subject.lemb.spa.fl_str_mv |
Análisis de sistemas -- Tesis y disertaciones académicas Teoría de sistemas -- Tesis y disertaciones académicas Diseño de sistemas -- Tesis y disertaciones académicas |
| description |
Deep learning models have improved several business intelligence tools like Customer relationship Management(CRM) systems. However, those models have increased the need for advanced computational capacity and infrastructure. Modern accelerators are starting to have floating-point precision arithmetic problems generated by highly streamlined systems, powered by the need to process an ever-increasing volume of data and increasingly complex models to attend to the necessity to identify customer data that allow consolidating products or services. We focus on CRM anomalies detection using GPU(Graphics Processor Unit) because they are a relevant source of money drain for organizations and directly affect the relationship between clients and suppliers. Our results present the combination of deep learning models with a computational structure that could access by organizations, but with a combination that reduces the number of features that achieve answers to CRM system. |
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2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-07-29T15:33:03Z |
| dc.date.available.none.fl_str_mv |
2021-07-29T15:33:03Z |
| dc.date.created.none.fl_str_mv |
2021 |
| dc.type.local.spa.fl_str_mv |
Trabajo de grado de maestría |
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info:eu-repo/semantics/masterThesis |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://hdl.handle.net/20.500.12010/20764 |
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http://expeditio.utadeo.edu.co |
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http://hdl.handle.net/20.500.12010/20764 http://expeditio.utadeo.edu.co |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Akhtar, M.S., Chauhan, D.S., Ekbal, A., 2020. A deep multi-task contextual attention framework for multi-modal affect analysis. ACM Trans. Knowl. Discov. Data 14. doi:10.1145/3380744. Navarro, C.A., Carrasco, R., Barrientos, R.J., Riquelme, J.A., Vega, R., 2021. Gpu tensor cores for fast arithmetic reductions. IEEE Transactions on Parallel and Distributed Systems 32, 72–84. doi:10.1109/ TPDS.2020.3011893. Nevin, J., 1995. Relationship marketing and distribution channels: Exploring fundamental issues. Journal of the Academy of Marketing Science 23, 327–334. doi:10.1177/009207039502300413. Ordóñez, F., Roggen, D., 2016. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors (Switzerland) 16. doi:10.3390/s16010115. Ozar, B., . Sql-server-first-responder-kit. https://github.com/ BrentOzarULTD/SQL-Server-First-Responder-Kit/blob/dev/sp_ ineachdb.sql. Pang, G., Shen, C., Cao, L., Hengel, A.V.D., 2021. Deep learning for anomaly detection: A review. ACM Comput. Surv. 54. doi:10.1145/ 3439950. Payne, A., Frow, P., 2005. A strategic framework for customer relationship management. Journal of Marketing 69, 167–176. doi:10. 1509/jmkg.2005.69.4.167. Racherla, P., Hu, C., 2008. ecrm system adoption by hospitality organizations: A technology-organization-environment (toe) framework. Journal of Hospitality&Leisure Marketing 17, 30–58. URL: https:// doi.org/10.1080/10507050801978372, doi:10.1080/10507050801978372. Raina, R., Madhavan, A., Ng, A., 2009. Large-scale deep unsupervised learning using graphics processors, in: Proceedings of the 26th International Conference On Machine Learning, ICML 2009, p. 110. doi:10.1145/1553374.1553486. Sadhu, V., Misu, T., Pompili, D., 2019. Deep multi-task learning for anomalous driving detection using can bus scalar sensor data, in: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2038–2043. doi:10.1109/IROS40897.2019. 8967753. Sagheer, A., Kotb, M., 2019. Unsupervised pre-training of a deep lstm-based stacked autoencoder for multivariate time series forecasting problems. Scientific Reports 9, 19038. URL: https://doi.org/ 10.1038/s41598-019-55320-6, doi:10.1038/s41598-019-55320-6. Trainor, K., Andzulis, J., Rapp, A., Agnihotri, R., 2014. Social media technology usage and customer relationship performance: A capabilities-based examination of social crm. Journal of Business Research 67, 1201–1208. doi:10.1016/j.jbusres.2013.05.002. Wang, R., Nie, K., Wang, T., Yang, Y., Long, B., 2020. Deep learning for anomaly detection, in: Proceedings of the 13th International Conference on Web Search and Data Mining, p. 894–896. doi:10.1145/3336191.3371876. Wirth, R., Hipp, J., 2000. Crisp-dm: Towards a standard process model for data mining, in: Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, Springer-Verlag London, UK. pp. 29–39. Zachariadis, O., Satpute, N., Gómez-Luna, J., Olivares, J., 2020. Accelerating sparse matrix–matrix multiplication with gpu tensor cores. Computers & Electrical Engineering 88, 106848. doi:https://doi. org/10.1016/j.compeleceng.2020.106848. Zhang, S., Wu, P., 2019. High accuracy low precision qr factorization and least square solver on gpu with tensorcore. ArXiv abs/1912.05508. Zheng, Z., Pu, J., Liu, L., Wang, D., Mei, X., Zhang, S., Dai, Q., 2020. Contextual anomaly detection in solder paste inspection with multi-task learning. ACM Trans. Intell. Syst. Technol. 11. URL: https://doi.org/10.1145/3383261, doi:10.1145/3383261. Brereton, R.G., Lloyd, G.R., 2016. Re-evaluating the role of the mahalanobis distance measure. Journal of Chemometrics 30, 134–143. doi:https://doi.org/10.1002/cem.2779. Chen, I., Popovich, K., 2003. Understanding customer relationship management (crm): People, process and technology. Business Process Management Journal 9, 672–688. doi:10.1108/ 14637150310496758. Chen, Z.Y., Fan, Z.P., Sun, M., 2012. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research 223, 461–472. doi:https://doi.org/10.1016/j.ejor.2012.06.040. |
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Colombia2021-07-29T15:33:03Z2021-07-29T15:33:03Z2021http://hdl.handle.net/20.500.12010/20764http://expeditio.utadeo.edu.coDeep learning models have improved several business intelligence tools like Customer relationship Management(CRM) systems. However, those models have increased the need for advanced computational capacity and infrastructure. Modern accelerators are starting to have floating-point precision arithmetic problems generated by highly streamlined systems, powered by the need to process an ever-increasing volume of data and increasingly complex models to attend to the necessity to identify customer data that allow consolidating products or services. We focus on CRM anomalies detection using GPU(Graphics Processor Unit) because they are a relevant source of money drain for organizations and directly affect the relationship between clients and suppliers. Our results present the combination of deep learning models with a computational structure that could access by organizations, but with a combination that reduces the number of features that achieve answers to CRM system.#AnáliticaDeDatosLos modelos de aprendizaje profundo han mejorado varias herramientas de inteligencia empresarial, como los sistemas de gestión de relaciones con el cliente (CRM). Sin embargo, esos modelos han aumentado la necesidad de infraestructura y capacidad computacional avanzada. Los aceleradores modernos están comenzando a tener problemas aritméticos de precisión de punto flotante generados por sistemas altamente optimizados, impulsados por la necesidad de procesar un volumen cada vez mayor de datos y modelos cada vez más complejos para atender la necesidad de identificar datos de clientes que permitan consolidar productos o servicios. . Nos enfocamos en la detección de anomalías de CRM utilizando GPU (Graphics Processor Unit) porque son una fuente relevante de drenaje de dinero para las organizaciones y afectan directamente la relación entre clientes y proveedores. Nuestros resultados presentan la combinación de modelos de aprendizaje profundo con una estructura computacional a la que podrían acceder las organizaciones, pero con una combinación que reduce la cantidad de funcionalidades que logran respuestas al sistema CRM.12 páginasapplication/pdfengUniversidad de Bogotá Jorge Tadeo LozanoMaestría en Ingeniería y Analítica de Datosinstname:Universidad de Bogotá Jorge Tadeo Lozanoreponame:Expeditio Repositorio Institucional UJTLAnalítica de datosAnálisis de sistemas -- Tesis y disertaciones académicasTeoría de sistemas -- Tesis y disertaciones académicasDiseño de sistemas -- Tesis y disertaciones académicasUnsupervised learning models-based CRM anomaly detection using GPUTrabajo de grado de maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/resource_type/c_2df8fbb1Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Akhtar, M.S., Chauhan, D.S., Ekbal, A., 2020. A deep multi-task contextual attention framework for multi-modal affect analysis. ACM Trans. Knowl. Discov. Data 14. doi:10.1145/3380744.Navarro, C.A., Carrasco, R., Barrientos, R.J., Riquelme, J.A., Vega, R., 2021. Gpu tensor cores for fast arithmetic reductions. IEEE Transactions on Parallel and Distributed Systems 32, 72–84. doi:10.1109/ TPDS.2020.3011893.Nevin, J., 1995. Relationship marketing and distribution channels: Exploring fundamental issues. Journal of the Academy of Marketing Science 23, 327–334. doi:10.1177/009207039502300413.Ordóñez, F., Roggen, D., 2016. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors (Switzerland) 16. doi:10.3390/s16010115.Ozar, B., . Sql-server-first-responder-kit. https://github.com/ BrentOzarULTD/SQL-Server-First-Responder-Kit/blob/dev/sp_ ineachdb.sql.Pang, G., Shen, C., Cao, L., Hengel, A.V.D., 2021. Deep learning for anomaly detection: A review. ACM Comput. Surv. 54. doi:10.1145/ 3439950.Payne, A., Frow, P., 2005. A strategic framework for customer relationship management. Journal of Marketing 69, 167–176. doi:10. 1509/jmkg.2005.69.4.167.Racherla, P., Hu, C., 2008. ecrm system adoption by hospitality organizations: A technology-organization-environment (toe) framework. Journal of Hospitality&Leisure Marketing 17, 30–58. URL: https:// doi.org/10.1080/10507050801978372, doi:10.1080/10507050801978372.Raina, R., Madhavan, A., Ng, A., 2009. Large-scale deep unsupervised learning using graphics processors, in: Proceedings of the 26th International Conference On Machine Learning, ICML 2009, p. 110. doi:10.1145/1553374.1553486.Sadhu, V., Misu, T., Pompili, D., 2019. Deep multi-task learning for anomalous driving detection using can bus scalar sensor data, in: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2038–2043. doi:10.1109/IROS40897.2019. 8967753.Sagheer, A., Kotb, M., 2019. Unsupervised pre-training of a deep lstm-based stacked autoencoder for multivariate time series forecasting problems. Scientific Reports 9, 19038. URL: https://doi.org/ 10.1038/s41598-019-55320-6, doi:10.1038/s41598-019-55320-6.Trainor, K., Andzulis, J., Rapp, A., Agnihotri, R., 2014. Social media technology usage and customer relationship performance: A capabilities-based examination of social crm. Journal of Business Research 67, 1201–1208. doi:10.1016/j.jbusres.2013.05.002.Wang, R., Nie, K., Wang, T., Yang, Y., Long, B., 2020. Deep learning for anomaly detection, in: Proceedings of the 13th International Conference on Web Search and Data Mining, p. 894–896. doi:10.1145/3336191.3371876.Wirth, R., Hipp, J., 2000. Crisp-dm: Towards a standard process model for data mining, in: Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, Springer-Verlag London, UK. pp. 29–39.Zachariadis, O., Satpute, N., Gómez-Luna, J., Olivares, J., 2020. Accelerating sparse matrix–matrix multiplication with gpu tensor cores. Computers & Electrical Engineering 88, 106848. doi:https://doi. org/10.1016/j.compeleceng.2020.106848.Zhang, S., Wu, P., 2019. High accuracy low precision qr factorization and least square solver on gpu with tensorcore. ArXiv abs/1912.05508.Zheng, Z., Pu, J., Liu, L., Wang, D., Mei, X., Zhang, S., Dai, Q., 2020. Contextual anomaly detection in solder paste inspection with multi-task learning. ACM Trans. Intell. Syst. Technol. 11. URL: https://doi.org/10.1145/3383261, doi:10.1145/3383261.Brereton, R.G., Lloyd, G.R., 2016. Re-evaluating the role of the mahalanobis distance measure. Journal of Chemometrics 30, 134–143. doi:https://doi.org/10.1002/cem.2779.Chen, I., Popovich, K., 2003. Understanding customer relationship management (crm): People, process and technology. Business Process Management Journal 9, 672–688. doi:10.1108/ 14637150310496758.Chen, Z.Y., Fan, Z.P., Sun, M., 2012. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research 223, 461–472. doi:https://doi.org/10.1016/j.ejor.2012.06.040.Bastidas Betancourt, DanielGarcía-Bedoya, OlmerGranados, Oscar M.Magíster en Ingeniería y Analítica de DatosTHUMBNAILAnomalias_CRM.pdf.jpgAnomalias_CRM.pdf.jpgIM Thumbnailimage/jpeg4165https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/20764/4/Anomalias_CRM.pdf.jpg3d77b1cd567326e2d8094e8709074d5dMD54open accessORIGINALAnomalias_CRM.pdfAnomalias_CRM.pdfVer documentoapplication/pdf1371071https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/20764/1/Anomalias_CRM.pdf601dd2e6d0dffdfddb7562807aabd4a8MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/20764/2/license.txtbaba314677a6b940f072575a13bb6906MD52open accessSingOGFOR-EFE-GDB-007_AUTORIZACION_DE_PUBLICACION_DE_TESIS_O_TRABAJO_DE_GRADO-signed.pdfSingOGFOR-EFE-GDB-007_AUTORIZACION_DE_PUBLICACION_DE_TESIS_O_TRABAJO_DE_GRADO-signed.pdfLicenciaapplication/pdf368996https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/20764/3/SingOGFOR-EFE-GDB-007_AUTORIZACION_DE_PUBLICACION_DE_TESIS_O_TRABAJO_DE_GRADO-signed.pdf50a873854afeb86f214a6cf60e3c3cdfMD53open access20.500.12010/20764oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/207642021-07-29 23:01:33.512open accessRepositorio Institucional - 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