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2017, Procedia Computer Science
The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in cases when software architecture and/or data models are changed; (3) to gather data from different data sources and to transfer them to data warehouse.
2018
The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object’s values and procedures for data object’s analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in cases when software architecture and/or data models are changed; (3) to gather data from different data sources and to transfer them to data warehouse. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the scientific committee of the internat...
JUCS - Journal of Universal Computer Science, 2020
The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, ...
Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, 2017
The research discusses the issue how to describe data quality and what should be taken into account when developing an universal data quality management solution. The proposed approach is to create quality specifications for each kind of data objects and to make them executable. The specification can be executed step-by-step according to business process descriptions, ensuring the gradual accumulation of data in the database and data quality checking according to the specific use case. The described approach can be applied to check the completeness, accuracy, timeliness and consistency of accumulated data.
2009 Ninth International Conference on Quality Software, 2009
Inadequate levels of Data Quality (DQ) in Information Systems (IS) suppose a very important problem for organizations. In any case, they look for to assure data quality from earlier stages on information system developments. This paper proposes to incorporate mechanisms into software development methodologies, in order to integrate users DQ requirements aimed at assuring the data quality from the beginning of development. It brings a framework consisting of processes, activities and tasks, well defined, which would be incorporated in existent software development methodology, as METRICA V3; and therefore, to assure software product data quality created according to this methodology. The extension presented, is a guideline, and this can be extended and applied to other development methodologies like Unified Development Process.
Thirty years ago, software was not considered a concrete value. Everyone agreed on its importance, but it was not considered as a good or possession. Nowadays, software is part of the balance of an organization. Data is slowly following the same process. The information owned by an organization is an important part of its assets. Information can be used as a competitive advantage. However, data has long been underestimated by the software community. Usually, methods and techniques apply to software (including data schemata), but the data itself has often been considered as an external problem. Validation and verification techniques usually assume that data is provided by an external agent and concentrate only on software.
IOSR Journal of Engineering, 2013
Ensuring Data Quality for an enterprise data repository various data quality tools are used that focus on this issue. The scope of these tools is moving from specific applications to a more global perspective so as to ensure data quality at every level. A more organized framework is needed to help managers to choose these tools so that that the data repositories or data warehouses could be maintained in a very efficient way. Data quality tools are used in data warehousing to ready the data and ensure that clean data populates the warehouse, thus enhancing usability of the warehouse. This research focuses on the on the various data quality tools which have been used and implemented successfully in the preparation of examination data of University of Kashmir for the preparation of results. This paper also proposes the mapping of data quality tools with the process which are involved for efficient data migration to data warehouse.
2007
Measurement is a key activity in DQ Management. Through DQ literature, one can discover a lot of proposals contributing somehow to the measurement of DQ issues. Looking at those proposals, it can be found out that there is a lack of unification of the nomenclature: different authors call to the same concepts in different way, or even, they do not explicitly recognize some of them. This may cause a misunderstanding of the proposed measures. The main aim of this paper is to propose a Data Quality Measurement Information Model (DQMIM) which provides a standardization of the referred terms by following ISO/IEC 15939 as a basis. This paper deals about the concepts implied in the measurement process, not about the measures themselves. In order to make operative the DQMIM, we have also designed a XML Schema which can be used to outline Data Quality Measurement Plans.
Proceedings of the 16th International …, 2011
Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development.
2002
This paper describes a relational database tool, the Data Quality Knowledge Management (DQKM), which captures and organizes the metadata associated with a data warehouse project. It builds on the concept of fitness for use by describing a measurement technique for subjectively assigning a measure to a data field based on the use and quality dimension of the data within the data warehouse. This measurement can then be compared to some minimum criteria, below which it is not cost effective to enhance the quality of the data. This tool can be used to make resource allocation decisions and get the greatest benefit for the cost in utilizing the scarce resources available to enhance source data for a data warehouse.
19º Simposio Brasileiro …, 2004
To solve complex user requirements the information systems need to integrate data from several, possibly autonomous data sources. One challenge in such environment is to provide the user with data meeting his requirements in terms of quality. These requirements are difficult to satisfy because of the strong heterogeneity of the sources. In this paper we address the problem of data quality evaluation in data integration systems. We present a framework which is a first attempt to formalize the evaluation of data quality. It is based on a graph model of the data integration system which allows us to define evaluation methods and demonstrate propositions in terms of graph properties. To illustrate our approach, we also present a first experiment with the data freshness quality factor and we show how the framework is used to evaluate this factor according to different scenarios.
Commonly, DW development methodologies, paying little attention to the problem of data quality and completeness. One of the common mistakes made during the planning of a data warehousing project is to assume that data quality will be addressed during testing. In addition to the data warehouse development methodologies, we will introduce in this paper a new approach to data warehouse development. This proposal will be based on integration data quality into the whole data warehouse development phase, denoted by: integrated requirement analysis for designing data warehouse (IRADAH). This paper shows that data quality is not only an integrated part of data warehouse project, but will remain a sustained and ongoing activity
Information & Management, 1980
Until recently, data quality was poor'.y understood and seldom achieved, yet it is essential to tlihe effective use of information systems. This paper discusses/ the nature and importance of data quality. The role of dataquality is placed in the life cycle framework. Many new concepts, tools and i techniques from both programming lang,uages and database management systems are presented and rhiated to data quality. In particular, the coqcept of a databrlse constraint is considered in detail. Some current limitation/s and research directions are proposed.
International Journal of Business Information Systems, 2016
Data quality has significance to companies, but is an issue that can be challenging to approach and operationalise. This study focuses on data quality from the perspective of operationalisation by analysing the practices of a company that is a world leader in its business. A model is proposed for managing data quality to enable evaluation and operationalisation. The results indicate that data quality is best ensured when organisation specific aspects are taken into account. The model acknowledges the needs of different data domains, particularly those that have master data characteristics. The proposed model can provide a starting point for operationalising data quality assessment and improvement. The consequent appreciation of data quality improves data maintenance processes, IT solutions, data quality and relevant expertise, all of which form the basis for handling the origins of products.
2005
The exploration of data to extract information or knowledge to support decision making is a critical success factor for an organization in today's society. However, several problems can affect data quality. These problems have a negative effect in the results extracted from data, affecting their usefulness and correctness. In this context, it is quite important to know and understand the data problems. This paper presents a taxonomy of data quality problems, organizing them by granularity levels of occurrence. A formal definition is presented for each problem included. The taxonomy provides rigorous definitions, which are information-richer than the textual definitions used in previous works. These definitions are useful to the development of a data quality tool that automatically detects the identified problems.
Proceedings of the 8th international workshop on Software quality - WoSQ '11, 2011
This industrial contribution describes a tool support approach to assessing the quality of relational databases. The approach combines two separate audits-an audit of the database structure as described in the schema and an audit of the database content at a given point in time. The audit of the database schema checks for design weaknesses, data rule violations and deviations from the original data model. It also measures the size, complexity and structural quality of the database. The audit of the database content compares the state of selected data attributes to identify incorrect data and checks for missing and redundant records. The purpose is to initiate a data clean-up process to ensure or restore the quality of the data.
Data warehouses are complex systems that have to deliver highly-aggregated, high quality data from heterogeneous sources to decision makers. Due to the dynamic change in the requirements and the environment, data warehouse system rely on meta databases to control their operation and to aid their evolution. In this paper, we present an approach to assess the quality of the data warehouse via a semantically rich model of quality management in a data warehouse. The model allows stakeholders to design abstract quality goals that are translated to executable analysis queries on quality measurements in the data warehouse's meta database. The approach is being implemented using the ConceptBase meta database system.
… ACM twelfth international workshop on Data …, 2009
Many data quality projects are integrated into data warehouse projects without enough time allocated for the data quality part, which leads to a need for a quicker data quality process implementation that can be easily adopted as the first stage of data warehouse implementation. We will see that many data quality rules can be implemented in a similar way, and thus generated based on metadata tables that store information about the rules. These generated rules are then used to check data in designated tables and mark erroneous records, or to do certain updates of invalid data. We will also store information about the rules violations in order to provide analysis of such data. This could give a significant insight into our source systems. Entire data quality process will be integrated into ETL process in order to achieve load of data warehouse that is as automated, as correct and as quick as possible. Only small number of records would be left for manual inspection and reprocessing.
International Journal of …, 2010
Data quality is a critical factor for the success of data warehousing projects. If data is of inadequate quality, then the knowledge workers who query the data warehouse and the decision makers who receive the information cannot trust the results. In order to obtain clean and reliable data, it is imperative to focus on data quality. While many data warehouse projects do take data quality into consideration, it is often given a delayed afterthought. Even QA after ETL is not good enough the Quality process needs to be incorporated in the ETL process itself. Data quality has to be maintained for individual records or even small bits of information to ensure accuracy of complete database. Data quality is an increasingly serious issue for organizations large and small. It is central to all data integration initiatives. Before data can be used effectively in a data warehouse, or in customer relationship management, enterprise resource planning or business analytics applications, it needs to be analyzed and cleansed. To ensure high quality data is sustained, organizations need to apply ongoing data cleansing processes and procedures, and to monitor and track data quality levels over time. Otherwise poor data quality will lead to increased costs, breakdowns in the supply chain and inferior customer relationship management. Defective data also hampers business decision making and efforts to meet regulatory compliance responsibilities. The key to successfully addressing data quality is to get business professionals centrally involved in the process. We have analyzed possible set of causes of data quality issues from exhaustive survey and discussions with data warehouse groups working in distinguishes organizations in India and abroad. We expect this paper will help modelers, designers of warehouse to analyse and implement quality warehouse and business intelligence applications.
Information & Management, 1980
Iq, 2008
Nowadays, data plays a key role in organizations, and management of its quality is becoming an essential activity. As part of such required management, organizations need to draw up processes for measuring the data quality (DQ) levels of their organizational units, taking into account the particularities of different scenarios, available resources, and characteristics of the data used in them. Given that there are not many works in the literature related to this objective, this paper proposes a methodology-abbreviated MMPROto develop processes for measuring DQ. MMPRO is based on ISO/IEC 15939. Despite being a standard of quality software, we believe it can be successfully applied in this context because of the similarities between software and data. The proposed methodology consists of four activities: (1) Establish and sustain the DQ measurement commitment, (2) Plan the DQ Measurement Process, (3) Perform the DQ Measurement Process, and (4) Evaluate the DQ Measurement Process. These four activities are divided into tasks. For each task, input and output products are listed, as well as a set of useful techniques and tools, many of them borrowed from the Software Engineering field.
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