Methodologies for data quality assessment and improvement

Abstract. Information systems integration is an essential instrument for organizations to attain advantage in today's growing and fast changing business and technology landscapes. Integration solutions generate added value by combining the functionality and services of heterogeneous and diverse systems. Existing integration environments tend to rely heavily on technical, platformdependent skills. Consequently, the solutions that they enable are not optimally aligned with the envisioned business goals of the organization. Furthermore, the gap between the goals and the solutions complicates the task of evaluating the quality of integration solutions. To address these challenges, we propose a quality-driven, model-driven methodology for designing and developing integration solutions. The methodology spans organizational and systems design details, providing a holistic view of the integration solution and its underlying business goals. A multi-view meta-model provides the basis for the integration design. Quality factors that affect various aspects of the integration solution guide and inform the progress of the methodology. An example business case is presented to demonstrate the application of the methodology.

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“…Research on quality can be approached from two perspectives: assessment or improvement [14]. Assessing quality (of information, processes, etc.)…”

Section: Quality In Systems Integrationmentioning

confidence: 99%

A Quality-Driven Methodology for Information Systems Integration

Zikra

Stirna

Zdravković

2017

CSIMQ

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“…For example, the United States government enacted the Data Quality Act in 2002 [20]. There are several theoretical approaches and tools, such as record linkage and business rules [21,22], which have been introduced to evaluate and improve the quality of data.…”

Section: History Of Trustworthiness Frameworkmentioning

confidence: 99%

“…It first computes the current and intermediate trust scores and eventually converts them into final trust scores by using an iterative framework. Advancing Lim's et al's work, Batini et al [44] presented a lightweight provenance encoding and decoding scheme by exploiting bloom filters. Considering the specific security challenges of sensor networks, they have proposed network, data, provenance, and threat models.…”

Section: Some Other Prominent Frameworkmentioning

confidence: 99%

Comparative Study of Evaluating the Trustworthiness of Data Based on Data Provenance

2016

J Inf Process Syst

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Due to the proliferation of data being exchanged and the increase of dependency on this data for critical decision-making, it has become imperative to ensure the trustworthiness of the data at the receiving end in order to obtain reliable results. Data provenance, the derivation history of data, is a useful tool for evaluating the trustworthiness of data. Various frameworks have been proposed to evaluate the trustworthiness of data based on data provenance. In this paper, we briefly review a history of these frameworks for evaluating the trustworthiness of data and present an overview of some prominent state-of-the-art evaluation frameworks. Moreover, we provide a comparative analysis of two key frameworks by evaluating various aspects in an executional environment. Our analysis points to various open research issues and provides an understanding of the functionalities of the frameworks that are used to evaluate the trustworthiness of data.

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“…Data Quality [23,4] is the discipline that studies the most appropriate and relevant features to describe the value of data.…”

Section: Data Quality and Linked Datamentioning

confidence: 99%

“…Geo-spatial data and information related to entities located in the physical world are among the first sources that are published openly -and often also freely -on the Web; valuable examples are Ordnance Survey location data in the UK 4 , GeoLinkedData.es in Spain 5 , GeoNames geographical database 6 and the community-driven OpenStreetMap 7 . The Semantic Web community also has showed interest in geo-spatial data: OpenStreetMap was turned into Linked Data by the LinkedGeoData project [26] and the Open Geospatial Consortium is standardizing GeoSPARQL 8 , a spatial extension of the SPARQL language.…”

Section: Introductionmentioning

confidence: 99%

Linking Smart Cities Datasets with Human Computation – The Case of UrbanMatch

Celino

Contessa

Corubolo

et al. 2012

The Semantic Web – ISWC 2012

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Abstract.To realize the Smart Cities vision, applications can leverage the large availability of open datasets related to urban environments. Those datasets need to be integrated, but it is often hard to automatically achieve a high-quality interlinkage. Human Computation approaches can be employed to solve such a task where machines are ineffective. We argue that in this case not only people's background knowledge is useful to solve the task, but also people's physical presence and direct experience can be successfully exploited. In this paper we present UrbanMatch, a Game with a Purpose for players in mobility aimed at validating links between points of interest and their photos; we discuss the design choices and we show the high throughput and accuracy achieved in the interlinking task.

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Methodologies for data quality assessment and improvement

Cited by 824 publications

References 31 publications

A Quality-Driven Methodology for Information Systems Integration

A Quality-Driven Methodology for Information Systems Integration

Comparative Study of Evaluating the Trustworthiness of Data Based on Data Provenance

Linking Smart Cities Datasets with Human Computation – The Case of UrbanMatch

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