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Chemical cleaning of potable water membranes: A review [PDF]
Separation and Purification Technology, 2010 The literature on chemical cleaning of polymeric hollow fibre ultrafiltration and microfiltration membranes used in the filtration of water for municipal water supply is reviewed. The review considers the chemical cleaning mechanism, and the perceived
Simon Judd
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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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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 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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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 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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Proceedings of the 2016 International Conference on Management of Data, 2016
Detecting and repairing dirty data is one of the perennial challenges in data analytics, and failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few years, there has been a surge of interest from both industry and academia on data cleaning problems including new abstractions, interfaces, approaches for ...
Xu Chu +3 more
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Detecting and repairing dirty data is one of the perennial challenges in data analytics, and failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few years, there has been a surge of interest from both industry and academia on data cleaning problems including new abstractions, interfaces, approaches for ...
Xu Chu +3 more
openaire +1 more source