Document similarity for error prediction

In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the...

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Autores principales:	Péter Marjai, Péter Lehotay-Kéry, Attila Kiss
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	document similarity error prediction spar Telecommunication TK5101-6720 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/fb0325fcab3947a4ac4a7c7934c0c359
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id	oai:doaj.org-article:fb0325fcab3947a4ac4a7c7934c0c359
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spelling	oai:doaj.org-article:fb0325fcab3947a4ac4a7c7934c0c3592021-11-17T14:22:00ZDocument similarity for error prediction2475-18392475-184710.1080/24751839.2021.1893496https://doaj.org/article/fb0325fcab3947a4ac4a7c7934c0c3592021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/24751839.2021.1893496https://doaj.org/toc/2475-1839https://doaj.org/toc/2475-1847In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the upcoming error based on the log lines of the actual file. To achieve this, we use document similarity. One of the key concepts of information retrieval is document similarity which is an indicator of how analogous (or different) documents are. In this paper, we are studying the effectiveness of prediction based on cosine similarity, Jaccard similarity, and Euclidean distance of rows before restarts. We use different features like TFIDF, Doc2Vec, LSH, and others in conjunction with these distance measures. Since networking devices produce lots of log files, we use Spark for Big data computing.Péter MarjaiPéter Lehotay-KéryAttila KissTaylor & Francis Grouparticledocument similarityerror predictionsparTelecommunicationTK5101-6720Information technologyT58.5-58.64ENJournal of Information and Telecommunication, Vol 5, Iss 4, Pp 407-420 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	document similarity error prediction spar Telecommunication TK5101-6720 Information technology T58.5-58.64
spellingShingle	document similarity error prediction spar Telecommunication TK5101-6720 Information technology T58.5-58.64 Péter Marjai Péter Lehotay-Kéry Attila Kiss Document similarity for error prediction
description	In today's rushing world, there's an ever-increasing usage of networking equipment. These devices log their operations; however, there could be errors that result in the restart of the given device. There could be different patterns before different errors. Our main goal is to predict the upcoming error based on the log lines of the actual file. To achieve this, we use document similarity. One of the key concepts of information retrieval is document similarity which is an indicator of how analogous (or different) documents are. In this paper, we are studying the effectiveness of prediction based on cosine similarity, Jaccard similarity, and Euclidean distance of rows before restarts. We use different features like TFIDF, Doc2Vec, LSH, and others in conjunction with these distance measures. Since networking devices produce lots of log files, we use Spark for Big data computing.
format	article
author	Péter Marjai Péter Lehotay-Kéry Attila Kiss
author_facet	Péter Marjai Péter Lehotay-Kéry Attila Kiss
author_sort	Péter Marjai
title	Document similarity for error prediction
title_short	Document similarity for error prediction
title_full	Document similarity for error prediction
title_fullStr	Document similarity for error prediction
title_full_unstemmed	Document similarity for error prediction
title_sort	document similarity for error prediction
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/fb0325fcab3947a4ac4a7c7934c0c359
work_keys_str_mv	AT petermarjai documentsimilarityforerrorprediction AT peterlehotaykery documentsimilarityforerrorprediction AT attilakiss documentsimilarityforerrorprediction
_version_	1718425457213308928

Document similarity for error prediction

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