Resumo

NUNEZ-ARCIA, Yaisel; DIAZ-DE-LA-PAZ, Lisandra  e  GARCIA-MENDOZA, Juan Luis. Algorithm to correct instance level anomalies in large volumes of data using MapReduce. Rev cuba cienc informat [online]. 2016, vol.10, n.3, pp.105-118. ISSN 2227-1899.

ABSTRACT Data quality problems at instance level have a direct impact on decision making of organizations and affect their performance. As information grows unreasonably it is greater the probability that such problems occur in data. This paper presents an algorithm to correct instance level anomalies in big data sources with semi-structured or structured format. As a clustering method, K-means algorithm was used. To calculate the edit distance between strings the modification of Levenshtein was applied, and to handle the volume of the data, MapReduce model for distributed programming was used. Besides, in order to improve data quality, the following four phases were proposed: identification of the data source type, data format and the problem to be solved; pre-processing of the input data; data clustering and data cleansing.

Palavras-chave : data quality; data cleansing; big data; K-means algorithm; MapReduce.

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