| Paper Details: | Downloads: 445 |
| Serial Number: | P1121129662
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| Title: | Neuro Assessment of Data Quality based on Data Timeliness Criterion
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| Authors: | Eslam Omara and Taha El Said and Mervat Mousa
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| Abstract: | Data quality (DQ) is a success key of all information systems form those used to support different management levels (data warehouses, BI solutions …etc.) to systems applied to execute customer operations and mange customer relations. Quality of data produced from these systems has serious consequences on the processes depend on that data. Data quality involves all required characteristics of a data product and the concept was discussed in many fields as computer science, statistics, management …etc. Data may be available but quality defects make it useless, it also may be accurate, complete and consistent but it doesn't represent the current real world state, it represents an outdated view. Outdated or expired data is worth than no data, it may lead to delayed business actions, wrong decisions, inapplicable business plans. Evaluating the time at which the data product will expire can be used as a base line to plane update or recollection actions before reaching the expiration state. Data expiration age is calculated in this paper based on the equations used to measure the data quality dimension timeliness stated in the literature and a generalized regression neural network (GRNN) model is applied to measure a dataset expiration age
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| Keywords: | Data Quality, Data Quality Mining, Data Mining, Timeliness, GRNN
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| Journal/Conference: | International Journal of Artificial Intelligence and Machine Learning
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| Volume: | 11
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| Issue: | 1
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| Submission Date: | 7/20/2011 12:00:00 AM
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| Review Date: | 8/3/2011 12:00:00 AM
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| Publishing Date: | 9/25/2011 12:00:00 AM
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| Article Downloads: | 445
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