Data Quality Management

Data quality is crucial in measuring and analyzing science, technology and innovation adequately, which allows for the proper monitoring of research efficiency, productivity and even strategic decision making. In this chapter, the concept of data quality will be defined in terms of the different dimensions that together determine the quality of data. Next, methods will be discussed to measure these dimensions using objective and subjective methods. Specific attention will be paid to the management of data quality through the discussion of critical success factors in operational, managerial and governance processes including training that affect data quality. The chapter will be concluded with a section on data quality improvement, which examines data quality issues and provides roadmaps in order to improve and follow-up on data quality, in order to obtain data that can be used as a reliable source for quantitative and qualitative measurements of research

Identifying the core topics and themes of data and information quality research

As data and information quality research has evolved towards becoming a unified body of knowledge, the importance of defining the identity of this research area has also grown. Our paper presents the results of a preliminary study with the aim of helping to define this identity from its core topics and themes. To do so we analyze the abstracts of 324 journal articles and conference proceedings published over the past ten years. Latent semantic analysis is used with these abstracts to develop term-to-term semantic similarities and term-to-factor loadings, from which six core topics and fifteen core themes of data quality research are identified. This paper presents a quantitatively based and reproducible method for identifying topics and themes. In further research, this method will be used to analyze the frequency of papers published in each topic and theme to show their level of activity. Applying this method for publications within discrete periods of time can show how topics and themes change and evolve. This research has the potential to help define the identity of data and information quality research, find the topics and themes receiving the greatest attention, and reveal trends occurring in this growing area

Traceability System’s Impact On Process Mining in Production

From the perspective of manufacturing companies, data handling is gaining more attention as it is becoming a strategic resource in digital ecosystems. Market forces such as rising amounts of product variants and decreasing batch sizes lead to higher complexity in manufacturing processes. Therefore, production management’s demand for data-based process transparency is growing continuously as well as the number of companies turning to process mining to address these challenges. The increased use of process mining has uncovered many documented data quality issues that hamper output quality. In response to data usage and quality problems, research in the field of Big Data has turned to sophisticated data value chains as a promising approach to optimize data usage. This paper presents the application of the data value chain concept on a manufacturing use case, delivering an assessment of traceability systems and their effect on data quality issues. This assessment reviews commonly known quality issues and investigates how traceability systems can influence and facilitate better data quality. The results support manufacturing companies in their use of traceability systems to improve the reliability of their process mining input data and, hence, their output performance indicators to meet the demand for more data-based process transparency

Fruit fly optimization algorithm for network-aware web service composition in the cloud

Service Oriented Computing (SOC) provides a framework for the realization of loosely coupled service oriented applications. Web services are central to the concept of SOC. Currently, research into how web services can be composed to yield QoS optimal composite service has gathered significant attention. However, the number and spread of web services across the cloud data centers has increased, thereby increasing the impact of the network on composite service performance experienced by the user. Recently, QoS-based web service composition techniques focus on optimizing web service QoS attributes such as cost, response time, execution time, etc. In doing so, existing approaches do not separate QoS of the network from web service QoS during service composition. In this paper, we propose a network-aware service composition approach which separates QoS of the network from QoS of web services in the Cloud. Consequently, our approach searches for composite services that are not only QoS-optimal but also have optimal QoS of the network. Our approach consists of a network model which estimates the QoS of the network in the form of network latency between services on the cloud. It also consists of a service composition technique based on fruit fly optimization algorithm which leverages the network model to search for low latency compositions without compromising service QoS levels. The approach is discussed and the results of evaluation are presented. The results indicate that the proposed approach is competitive in finding QoS optimal and low latency solutions when compared to recent techniques

Sustainable built asset management performance indicators and attributes : a UK social housing case study example

This paper aims to identify key performance indicators (KPI), and their corresponding attributes, required to successfully manage asset management sustainably in a built environment context. Improving the sustainability of existing housing stock is a major challenge facing the UK social housing sector. There is a lack of support to navigate the growing and often incongruent information relating to sustainable development and how to operationalise it. The problem is twofold; firstly, the current (single criterion) condition-based approach to maintenance planning constrains Asset Managers and does not fully address the social, environmental and economic aspects of sustainability. Secondly, the toolkits available for assessing the sustainability of housing are often generic and are time consuming and expensive to implement. This paper reports the findings of a participatory research project with a leading London based housing association, using a series of landlord and tenant workshops to derive a set of attributes associated with key performance indicators (KPIs) to fully reflect the local requirements of the landlord and their interpretation of the sustainability agenda. Five KPIs considered to be measurable, directly affected by maintenance work and independent of each other were identified by this landlord (comfort, running costs, adaptability, maintenance costs and community).The resulting outputs, in a policy context, will provide a clear route-map to social housing landlords of how to improve the sustainability of their housing stock with the additional benefits of addressing fuel poverty, carbon emissions targets whilst at the same time help create and maintain housing in which people want to live. The proposed approach is flexible enough to incorporate the individual requirements of landlords, be able to adapt to changes in government policy (local and central) in a timely, robust, transparent and inclusive format

An approach for measuring rdf data completeness

International audienc

Üzleti intelligencia megoldások alkalmazásának sikertényezői - A hazai szolgáltató szektor nagyvállalatainak körében végzett mélyinterjús kutatás

A vállalatok működése szempontjából a döntéstámogató funkció folyamatos fejlesztése, monitorozása kiemelt jelentőségű, hiszen az vezetést támogató eszközként segíti a menedzsmentfeladatok ellátását. Az üzleti intelligencia (business intelligence, BI) olyan infokommunikációs megoldás, mely a vállalati rendszerekből különböző adatforrásokat felhasználva képes az adatok összekapcsolására és elemzésére. A napi üzletmenet gördülékeny biztosítása céljából alkalmazott tranzakciós rendszerektől eltérően a BI-eszközök beszámolás orientáltak, a fókusz a döntéstámogatásra helyeződik. A kutatás a fogalmak tisztázását követően képet ad a legfrissebb üzleti intelligencia trendekről. A tanulmány szakmai mélyinterjúk elemzésén keresztül betekintést nyújt az üzleti intelligencia megoldások világába. A kutatás eredményeként az olvasó képet kaphat a BI bevezetésétől várt eredményekről, az implementáció és a hosszú távú működtetés sikerkritériumait illetően