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dc.rights.licenseAll rights reserveden_US
dc.contributor.advisorDuffany, Jeffrey
dc.contributor.authorIrizarry Rodríguez, Josué
dc.date.accessioned2020-09-16T15:42:06Z
dc.date.available2020-09-16T15:42:06Z
dc.date.issued2014
dc.identifier.citationIrizarry Rodríguez, J. (2014). Trigger files text analysis and processing time anomaly detection [Unpublished manuscript]. Graduate School, Polytechnic University of Puerto Rico.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12475/614
dc.descriptionDesign Project Article for the Graduate Programs at Polytechnic University of Puerto Ricoen_US
dc.description.abstractToday the data processing is a vital part of the system supporting organization processes and transactions. A lot of data is managed, transmitted and stored daily. The format of the data is structured, unstructured or semi structured. Information Technology researchers are all in agreement that unstructured data is at least 80 percent of all enterprise data. It is unrealistic to expect that data will be perfect. The unstructured data hides important, when it is analyzed will aid businesses to have better decisions. The proposal of this project is an application using machine learning techniques like, information retrieval, data mining, and text mining with the intention to find possible anomalous values or processing time delays in the archived trigger files corpus. Key Terms - Data Mining, Machine Learning, Text Mining, Trigger File.en_US
dc.language.isoen_USen_US
dc.publisherPolytechnic University of Puerto Ricoen_US
dc.relation.ispartofComputer Engineering
dc.relation.ispartofseriesFall-2014
dc.relation.haspartSan Juanen_US
dc.subject.lcshData miningen_US
dc.subject.lcshMachine learning
dc.subject.lcshText data mining
dc.subject.lcshPolytechnic University of Puerto Rico--Graduate students--Research
dc.titleTrigger Files Text Analysis and Processing Time Anomaly Detectionen_US
dc.typeArticleen_US
dc.rights.holderPolytechnic University of Puerto Rico, Graduate Schoolen_US


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