Handling Failures in Data Quality Measures

Successful data quality (DQ) measure is important for many data consumers (or data guardians) to decide on the acceptability of data of concerned. Nevertheless, little is known about how “failures” of DQ measures can be handled by data guardians in the presence of factor(s) that contributes to the failures. This paper presents a review of failure handling mechanisms for DQ measures. The failure factors faced by existing DQ measures will be presented, together with the research gaps in respect to failure handling mechanisms in DQ frameworks. We propose ways to maximise the situations in which data quality scores can be produced when factors that would cause the failure of currently proposed scoring mechanisms are present. By understanding how failures can be handled, a systematic failure handling mechanism for robust DQ measures can be designed

A STUDY OF DATA QUALITY REQUIREMENTS FOR EMPIRICAL DATA IN THE FOOD SCIENCES

Data quality is a critical issu in scientific databases since the reliability of empirical data can have a major impact on the formation of scientific theories and policy decisions. Yet while several conceptual frameworks for data quality have been proposed, there is still a lack of general tools and metrics to measure and control the quality of empirical data in practice. As a first step in this direction, we carried out a detailed study of data quality requirements in a system designed to support food scientists by managing data about food composition. Our users included system designers and developers as well as food compilers and project managers. In addition to determining which dimensions of data quality specified in existing conceptual frameworks users consider important in assessing the reliability of data, we also asked users to assess the importance of various criteria related specifically to empirical data. These factors were based around the four steps typical in the life-cycle of empirical data, namely sampling, analysis, data acquisition and data processing. Another novel feature of our study was to investigate not only the different dimensions of data quality considered to be important but also how this depends on the role of users

Methoden zur Identifikation relevanter Datenquellen: Eine Literaturanalyse

Die zunehmende Anzahl an zu verarbeitenden Unternehmensdaten und die steigende strategische Relevanz des Informationsmanagements formulieren einen Bedarf zur Systematisierung des Prozesses zur Identifikation und Selektion relevanter Datenquellen. Obwohl die Datenquellenauswahl eine zentrale Aufgabe im Management der Informationswirtschaft (im Rahmen des strategischen Informationsmanagements) darstellt, fehlt es einer aktuellen Betrachtung zum Stand der Wissenschaft im Hinblick vorhandener Methoden zur Selektion relevanter Datenquellen. Der vorliegende Beitrag schließt diese Forschungslücke und stellt den aktuellen Stand der Wissenschaft zu vorhandenen Methoden zur Identifikation und Selektion relevanter Datenquellen dar. Mittels einer Literaturanalyse wurden insgesamt 37 wissenschaftliche Beiträge identifiziert, welche acht Methoden zur Datenquellenauswahl beschreiben. Die identifizierten Methoden wurden anschließend den Kategorien automatisierte, semi-automatisierte und manuelle Verfahren zugeordnet. Dabei konnte ebenfalls eine zunehmende Tendenz zu automatisierten Methoden zur Datenquellenauswahl beobachtet werden

Modelling continuance intention of citizens in government Facebook page: A complementary PLS approach

The main purpose of this paper is to examine the continuance intention (CI) of citizens in following government Facebook page. Applying theories of expectation-confirmation, and information system success on a sample of 362 students in Malaysia, and using Partial Least Squares-Structural Equation Modelling (PLS-SEM), the study finds that CI and satisfaction of government Facebook page is contingent upon information quality (IQ) of the Facebook page per se. IQ is found as a second order construct of five first order factors: reliability, completeness, relevancy, timeliness, and understandability. Satisfaction of government Facebook page is also found as a partial mediator to the relationship between IQ and CI of following government Facebook page. In addition, applying PLS multi-group analysis, the results show that different government Facebook pages moderate the relationships between IQ and satisfaction of government Facebook page, IQ and CI of following government Facebook page as well as satisfaction of government Facebook page and CI of following government Facebook page

Incorporating Domain-Specific Information Quality Constraints into Database Queries

The range of information now available in queryable repositories opens up a host of possibilities for new and valuable forms of data analysis. Database query languages such as SQL and XQuery offer a concise and high-level means by which such analyses can be implemented, facilitating the extraction of relevant data subsets into either generic or bespoke data analysis environments. Unfortunately, the quality of data in these repositories is often highly variable. The data is still useful, but only if the consumer is aware of the data quality problems and can work around them. Standard query languages offer little support for this aspect of data management. In principle, however, it should be possible to embed constraints describing the consumer’s data quality requirements into the query directly, so that the query evaluator can take over responsibility for enforcing them during query processing. Most previous attempts to incorporate information quality constraints into database queries have been based around a small number of highly generic quality measures, which are defined and computed by the information provider. This is a useful approach in some application areas but, in practice, quality criteria are more commonly determined by the user of the information not by the provider. In this paper, we explore an approach to incorporating quality constraints into databas

Design and Implementation of a Peer-to-Peer Data Quality Broker

Abstract Data quality is becoming an increasingly important issue in environments characterized by extensive data replication. Among such environments, this paper focuses on Cooperative Information Systems (CISs), for which it is very important to declare and access quality of data. Indeed, a system in the CIS will not easily exchange data with another system without a knowledge on its quality, and cooperation becomes dicult without data exchanges. Also, when poor quality data are exchanged, there is a progressive deterioration of the quality of data stored in the whole CIS. In this paper, we describe the detailed design and implementation of a peer-to-peer service for exchanging and improving data quality in CISs. Such a service allows to access data and related quality distributed in the CIS and improves quality of data by comparing dierent copies of the same data. Some experiments on real data will show the eectiveness of the service and the performance behavior

A semantic-enhanced quality-based approach to handling data sources in enterprise service bus

Data quality plays an important role in success of organizations. Poor data quality might significantly affect organizations’ businesses since wrong decisions can be made based on data with poor quality. It is therefore necessary to make data quality information available to data users and allow them to select data sources based on their given requirements. Enterprise Service Bus (ESB) can be used to tackle data integration issues. However, data sources are maintained out of the ESB’s control. This leads to a problem faced by users when it comes to selecting the most suitable data source among available ones. In this article, we present an approach to handling data sources in ESB based on data-quality and semantic technology. This introduces a new level of abstraction that can improve the process of data quality handling with the help of semantic technologies. We evaluate our work using three different scenarios within the wind energy domain.publishedVersionNivå

Emergent semantics in distributed knowledge management

Organizations and enterprises have developed complex data and information exchange systems that are now vital for their daily operations. Currently available systems, however, face a major challenge. On todays global information infrastructure, data semantics is more and more context- and time-dependent, and cannot be fixed once and for all at design time. Identifying emerging relationships among previously unrelated information items (e.g., during data interchange) may dramatically increase their business value. This chapter introduce and discuss the notion of Emergent Semantics (ES), where both the representation of semantics and the discovery of the proper interpretation of symbols are seen as the result of a selforganizing process performed by distributed agents, exchanging symbols and adaptively developing the proper interpretation via multi-party cooperation and conflict resolution. Emergent data semantics is dynamically dependent on the collective behaviour of large communities of agents, which may have different and even conflicting interests and agendas. This is a research paradigm interpreting semantics from a pragmatic prospective. The chapter introduce this notion providing a discussion on the principles, research area and current state of the art

Knowledge quality effect on small and medium-sized enterprises’ competitiveness through improvisational creativity, compositional creativity and innovation

The concept of competitiveness involves the level of creative actions and ability to produce quality goods and services. For Small and Medium Enterprises (SME), competitive advantage is contingent upon their timely decisions and speedto- market production capabilities. Many researchers have considered competitiveness as the degree of creativity and innovation. In recent years, the concept of quality has been synthesized with data, information, and knowledge while advancements in knowledge management concepts have made it necessary to consider knowledge quality (KQ) as well. A sample of 358 Malaysian SMEs was used applying partial least squares (PLS) approach which is a variance based structural equation modeling method. This thesis proposes that organizational factors such as absorptive capacity (AC), functional diversity (FD), knowledge network (KN), organizational structure (OS), organizational culture (OC), and technology utilization (TU) influence the sense-making activities (KQ dimensions) of business entities. This research combined theories of sense making, creativity, and organizational improvisation and developed a cogent model helping to understand and examine the structural relationships between organizational factors, KQ, and competitiveness. The findings indicate that TU, AC, FD, and OC are significant contributors to sense-making activities of Malaysian SMEs and TU, AC, and OC are found to be indirectly significant with improvisational creativity (IC), compositional creativity (CC), and innovation. Actionable KQ and accessibility KQ are found as mediators to the relationship between intrinsic KQ, contextual KQ, and IC and CC. The results of PLS-multi group analysis show a discrepancy between the results of Malay and Chinese ethnic groups. Finally, importance-performance map analysis indicates that IC and actionable KQ have the highest importance on Malaysian SMEs’ innovation