Improving data quality through effective use of data semantics

Madnick, Stuart E.; Zhu, Hongwei

doi:10.1016/j.datak.2005.10.001

Cited by 65 publications

(25 citation statements)

References 18 publications

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“…", we need to clarify whether the purchasing relationship between MIT and IBM should be interpreted as direct purchasing (i.e., purchased directly from IBM) or indirect purchasing through other channels (e.g., third-party brokers, distributors, retailers). In some cases, only the direct purchasing from IBM to MIT are considered, whereas in other cases indirect purchasing through other channels also needs to be included (Madnick and Zhu 2006).…”

Section: Classification Of Data Misinterpretation Problemsmentioning

confidence: 99%

A Context-Based Approach to Reconciling Data Interpretation Conflicts in Web Services Composition

Madnick

Zhu

2013

ACM Trans. Internet Technol.

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Web services composition is often hampered by various types of data misinterpretation problems. In this paper, we present a comprehensive classification of the data misinterpretation problems. To address them, we develop an approach to automatic detection and reconciliation of data interpretation conflicts in Web services composition. The approach uses a lightweight ontology augmented with modifiers, contexts, and atomic conversions between the contexts, implemented using XPath functions and external services. The WSDL descriptions of Web services are annotated to establish correspondences to the ontology and contexts. Given the naive Business Process Execution Language (BPEL) specification of the desired Web services composition with possible data interpretation conflicts, the reconciliation approach can automatically detect the conflicts and produce the corresponding mediated BPEL by incorporating appropriate conversions into the composition. Finally, we develop a prototype to validate and evaluate the reconciliation approach.

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Section: Classification Of Data Misinterpretation Problemsmentioning

confidence: 99%

A Context-Based Approach to Reconciling Data Interpretation Conflicts in Web Services Composition

Madnick

Zhu

2013

ACM Trans. Internet Technol.

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“…The risk of poor data quality (DQ) increases as larger and more complex information resources are being collected and maintained (Madnick and Zhu, 2006;Parssian and Jacob, 2004). Because most modern companies tend to collect increasing amounts of data, good data management is becoming ever more important.…”

Section: Introductionmentioning

confidence: 99%

“…As a response, in the last two decades, the issues of DQ have received a lot of attention, both by organisations worldwide and in academic literature. Several studies are exploring DQ challenges, focusing on DQ measurement and improvement (Batini and Scannapieco, 2006;Cappiello et al, 2006;Chen and Tseng, 2010;Chengalur-smith et al, 1999;Dejaeger et al, 2010;Delone and McLean, 1992;Eppler and Wittig, 2000;Fisher and Ballou, 2003;Jarke and Vassiliou, 1997;Kahun et al, 2002;Lee et al, 2002Lee et al, , 2006Madnick and Zhu, 2006;Maydanchik, 2007;Moraga et al, 2009;Paul et al, 1996;Panse and Ritter, 2009;Parssian and Jacob, 2004;Pipino et al, 2002;Raghunathan, 1999;Rahm and Do, 2000;Redman, 1998;Shankaranarayanan and Cai, 2006;Shankaranarayanan et al, 2003;Strong et al, 1997;Tayi and Ballou, 1998;Wand and Wang, 1996;Wang, 1998;Wang et al, 1995;Wang and Strong, 1996;Ware and Gandek, 1998;Watts et al, 2009). In practice, decision makers differentiate information from data intuitively, and describe information as data that has been processed.…”

Section: Introductionmentioning

confidence: 99%

A total data quality management for credit risk: new insights and challenges

Moges

Dejaeger

Lemahieu

et al. 2012

IJIQ

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Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only broadly defined as 'fitness for use'. While capturing its essence, a more precise interpretation of DQ is required during measurement. While there is a growing consensus on the multi-dimensional nature of DQ, no exact DQ definition has been put forward due to its context dependency. On the contrary, it is often stated that its constituting dimensions should be identified and defined in relation to the task at hand. Answering this call, we identify the DQ dimensions important to the credit risk assessment environment. In addition, we explore key DQ challenges and report on the causes of DQ problems in financial institutions. Statistical tests indicated nine most important DQ dimensions.

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“…Implicit assumptions made in each source need to be explicitly described and used to reconcile conflicts when data from these systems are combined [3]. Ontology plays an important role on making domain assumptions unambiguous or uniquely identifies the meaning of concepts in a specific domain of interest.…”

Section: Introductionmentioning

confidence: 99%

Semantic Conflicts Reconciliation as a Viable Solution for Semantic Heterogeneity Problems

Ismail¹,

Nasr²,

Sultan³

et al. 2013

IJACSA

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Abstractt-Achieving semantic interoperability is a current challenge in the field of data integration in order to bridge semantic conflicts occurring when the participating sources and receivers use different or implicit data assumptions. Providing a framework that automatically detects and resolves semantic conflicts is considered as a daunting task for many reasons, it should preserve the local autonomy of the integrated sources, as well as provides a standard query language for accessing the integrated data on a global basis. Many existing traditional and ontology-based approaches have tried to achieve semantic interoperability, but they have certain drawbacks that make them inappropriate for integrating data from a large number of participating sources.We propose semantic conflicts reconciliation (SCR) framework, it is ontology-based system in which all data semantics explicitly described in the knowledge representation phase and automatically taken into account through the interpretation mediation service phase, so conflicts detected and resolved automatically at the query time.

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Improving data quality through effective use of data semantics

Cited by 65 publications

References 18 publications

A Context-Based Approach to Reconciling Data Interpretation Conflicts in Web Services Composition

A Context-Based Approach to Reconciling Data Interpretation Conflicts in Web Services Composition

A total data quality management for credit risk: new insights and challenges

Semantic Conflicts Reconciliation as a Viable Solution for Semantic Heterogeneity Problems

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