Reputation and Trust Models with Data Quality Metrics for Improving Autonomous Vehicles Traffic Security and Safety

This chapter introduces abstract algebra as a means of understanding and creating data quality metrics for entity resolution, the process in which records determined to represent the same real-world entity are successively located and merged. Entity resolution is a particular form of data mining that is foundational to a number of applications in both industry and government. Examples include commercial customer recognition systems and information sharing on “persons of interest” across federal intelligence agencies. Despite the importance of these applications, most of the data quality literature focuses on measuring the intrinsic quality of individual records than the quality of record grouping or integration. In this chapter, the authors describe current research into the creation and validation of quality metrics for entity resolution, primarily in the context of customer recognition systems. The approach is based on an algebraic view of the system as creating a partition of a set of entity records based on the indicative information for the entities in question. In this view, the relative quality of entity identification between two systems can be measured in terms of the similarity between the partitions they produce. The authors discuss the difficulty of applying statistical cluster analysis to this problem when the datasets are large and propose an alternative index suitable for these situations. They also report some preliminary experimental results, and outlines areas and approaches to further research in this area.

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Prioritizing technical debt in database normalization using portfolio theory and data quality metrics

Proceedings of the 2018 International Conference on Technical Debt - TechDebt '18 ◽

10.1145/3194164.3194170 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mashel Albarak ◽

Rami Bahsoon

Keyword(s):

Data Quality ◽

Portfolio Theory ◽

Quality Metrics ◽

Technical Debt ◽

Data Quality Metrics ◽

Database Normalization

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Visual Interactive Creation, Customization, and Analysis of Data Quality Metrics

Journal of Data and Information Quality ◽

10.1145/3190578 ◽

2018 ◽

Vol 10 (1) ◽

pp. 1-26 ◽

Cited By ~ 2

Author(s):

Christian Bors ◽

Theresia Gschwandtner ◽

Simone Kriglstein ◽

Silvia Miksch ◽

Margit Pohl

Keyword(s):

Data Quality ◽

Quality Metrics ◽

Data Quality Metrics

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Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles)

Journal of Proteome Research ◽

10.1021/pr201071t ◽

2011 ◽

Vol 11 (2) ◽

pp. 1412-1419 ◽

Cited By ~ 21

Author(s):

Christopher R. Kinsinger ◽

James Apffel ◽

Mark Baker ◽

Xiaopeng Bian ◽

Christoph H. Borchers ◽

...

Keyword(s):

Mass Spectrometry ◽

Open Access ◽

Data Quality ◽

Quality Metrics ◽

Mass Spectrometry Data ◽

Open Access Data ◽

Data Quality Metrics ◽

Access Data

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Context-based Data Quality Metrics in Data Warehouse Systems

CLEI electronic journal ◽

10.19153/cleiej.20.2.3 ◽

2017 ◽

Vol 20 (2) ◽

Cited By ~ 1

Author(s):

Flavia Serra ◽

Adriana Marotta

Keyword(s):

Decision Making ◽

Literature Review ◽

Data Quality ◽

Conceptual Framework ◽

Data Warehouse ◽

Quality Metrics ◽

Research Community ◽

System Component ◽

Community Data ◽

Data Quality Metrics

The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through context-based DQ metrics.

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