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IEEE Transactions on Knowledge and Data Engineering, 2022
Classical data cleaning performs a minimal set of operations on the data to satisfy the given integrity constraints. Often, this minimization is equivalent to vertex cover, for example when tuples can be removed due to the violation of functional dependencies. Classically, the uncertainty of tuples and constraints is ignored.
Henning Koehler, Sebastian Link
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Classical data cleaning performs a minimal set of operations on the data to satisfy the given integrity constraints. Often, this minimization is equivalent to vertex cover, for example when tuples can be removed due to the violation of functional dependencies. Classically, the uncertainty of tuples and constraints is ignored.
Henning Koehler, Sebastian Link
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2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), 2018
In this paper, we motivate the need to include context in data cleaning in order to account for the subjective nature of data quality. Based on our recent work on incorporating ontologies into Functional Dependencies, we argue that ontologies are a rich source of context, and an effective tool for modeling domain concepts and relationships for data ...
Morteza Alipour Langouri +4 more
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In this paper, we motivate the need to include context in data cleaning in order to account for the subjective nature of data quality. Based on our recent work on incorporating ontologies into Functional Dependencies, we argue that ontologies are a rich source of context, and an effective tool for modeling domain concepts and relationships for data ...
Morteza Alipour Langouri +4 more
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Proceedings of the VLDB Endowment, 2016
Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. Data cleaning exercise often consist of two phases: error detection and error repairing.
Xu Chu, Ihab F. Ilyas
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Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. Data cleaning exercise often consist of two phases: error detection and error repairing.
Xu Chu, Ihab F. Ilyas
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Cleaning Data with Forbidden Itemsets
IEEE Transactions on Knowledge and Data Engineering, 2020Methods for cleaning dirty data typically employ additional information about the data such as user-provided constraints specifying when data is dirty, e.g., domain restrictions, illegal value combinations, or logical rules. However, real-world scenarios usually only have dirty data available, without known constraints.
Joeri Rammelaere, Floris Geerts
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
Addressed in this paper is the issue of 'email data cleaning' for text mining. Many text mining applications need take emails as input. Email data is usually noisy and thus it is necessary to clean it before mining. Several products offer email cleaning features, however, the types of noises that can be eliminated are restricted. Despite the importance
Jie Tang 0001 +3 more
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Addressed in this paper is the issue of 'email data cleaning' for text mining. Many text mining applications need take emails as input. Email data is usually noisy and thus it is necessary to clean it before mining. Several products offer email cleaning features, however, the types of noises that can be eliminated are restricted. Despite the importance
Jie Tang 0001 +3 more
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Data quality and data cleaning
Proceedings of the 2003 ACM SIGMOD international conference on Management of data, 2003Data quality is a serious concern in any data-driven enterprise, often creating misleading findings during data mining, and causing process disruptions in operational databases. The manifestations of data quality problems can be very expensive- "losing" customers, "misplacing" billions of dollars worth of equipment, misallocated resources due to ...
Theodore Johnson, Tamraparni Dasu
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E-Clean: A Data Cleaning Framework for Patient Data
2011 First International Conference on Informatics and Computational Intelligence, 2011We need to prepare quality data by pre-processing the raw data. Data cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and inconsistencies from data in order to improve the quality of data. Data cleaning system are needed to support any changes in the structure, representation or content of data.
Hasimah Hj Mohamed +3 more
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