scholarly journals User-Oriented Approach to Data Quality Evaluation

2020 ◽  
Vol 26 (1) ◽  
pp. 107-126
Author(s):  
Anastasija Nikiforova ◽  
Janis Bicevskis ◽  
Zane Bicevska ◽  
Ivo Oditis
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Open Data ◽  
Evaluation Process ◽  
Specific Model ◽  
Third Party ◽  
Quality Model ◽  
Levels Of Abstraction ◽  
Quality Requirements ◽  
Data Object

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, analysing their quality. At least 3 advantages were highlighted: (1) a graphical data quality model allows the definition of data quality by non-IT and non-data quality experts as the presented diagrams are easy to read, create and modify, (2) the data quality model allows an analysis of "third-party" data without deeper knowledge on how the data were accrued and processed, (3) the quality of the data can be described at least at two levels of abstraction - informally using natural language or formally by including executable artefacts such as SQL statements.

A Framework for the Quality Evaluation of MDWE Methodologies and Information Technology Infrastructures

2012 ◽  
pp. 295-307
Author(s):  
Francisco José Domínguez-Mayo ◽  
María José Escalona ◽  
Manuel Mejías ◽  
Isabel Ramos ◽  
Luis Fernández
Keyword(s):  
Information Technology ◽  
Quality Evaluation ◽  
Objective Evaluation ◽  
Specific Model ◽  
Evaluation Framework ◽  
Levels Of Abstraction ◽  
Web Engineering ◽  
Technology Infrastructure ◽  
Model Driven ◽  
Independent Model

Diverse development web methodologies currently exist in the field of Model-Driven Web Engineering (MDWE), each of which covers different Levels of Abstraction on Model-Driven Architecture (MDA): Computation Independent Model (CIM), Platform Independent Model (PIM), Platform Specific Model (PSM), and Code. Given the high number of methodologies available, it has become necessary to define objective evaluation tools to enable development teams to improve their methodological environment and help designers of web methodologies design new effective and efficient tools, processes and techniques. Since proposals are constantly evolving, the need may arise not only to evaluate the quality but also to find out how it can be improved. This paper presents an approach named QuEF (Quality Evaluation Framework) oriented towards evaluating, through objectives measures, the quality of information technology infrastructure, mainly in MDWE methodology environments.

Data Stream Quality Evaluation for the Generation of Alarms in the Health Domain

2015 ◽  
Vol 24 (3) ◽  
pp. 361-369
Author(s):  
Saúl Fagúndez ◽  
Joaquín Fleitas ◽  
Adriana Marotta
Keyword(s):  
Data Quality ◽  
Data Streams ◽  
Data Stream ◽  
Quality Evaluation ◽  
False Alarms ◽  
Health It ◽  
Quality Model ◽  
Quality Of Data ◽  
Stream Quality

AbstractThe use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to monitor their health. It focuses on processing the sensors’ data streams, taking into account data quality. In order to achieve this, a data quality model for this kind of data streams and an architecture for the monitoring system are proposed. Moreover, our work introduces a mechanism for avoiding false alarms generated by data quality problems.

Power System Data Quality Evaluation Based on the Analytic Hierarchy Process

2013 ◽  
Vol 712-715 ◽  
pp. 2611-2614
Author(s):  
Xi Liang Wang ◽  
Xuan Qin ◽  
Dao Xin Liu ◽  
Zi Jian Wang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Data Quality ◽  
Electric Power ◽  
Quality Evaluation ◽  
Statistical Data ◽  
Evaluation Process ◽  
Analytic Hierarchy ◽  
System Data ◽  
Hierarchy Process ◽  
Specific Evaluation

The demand for electric power data is more and more widely, and put forward higher requirements to the quality of statistical data. This paper combined with the features of electric power data. Evaluate data quality from the accuracy, completeness, uniqueness, consistency, accuracy, efficiency and timeliness seven aspects. And put forward the specific evaluation methods of each evaluation index. Then build a whole data quality evaluation process on this basis, quantitative analysis the data in the database, to acquaintance the data quality condition.

scholarly journals FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3172
Author(s):  
Yihuai Liang ◽  
Yan Li ◽  
Byeong-Seok Shin
Keyword(s):  
Mobile Devices ◽  
Quality Evaluation ◽  
Evaluation Process ◽  
Third Party ◽  
Smart Contracts ◽  
Trusted Third Party ◽  
Sensing Data ◽  
Execution Environment ◽  
Trusted Execution Environment

Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve evaluating the quality of sensing data, finishing payment, and storing sensing data and so forth. The requirements which are codified as smart contracts are executed to evaluate the quality of sensing data in a blockchain. However, regardless of the fact that the quality of sensing data may actually be sufficient, one key challenge is that malicious requesters can deliberately publish abnormal requirements that cause failure to occur in the quality evaluation process. If requesters control a miner node or full node, they can access the data without making payment; this is because of the transparency of data stored in the blockchain. This issue promotes unfair dealing and severely lowers the motivation of workers to participate in crowdsensing tasks. We (i) propose a novel crowdsensing scheme to address this issue using Trusted Execution Environments; (ii) offer a solution for the confidentiality and integrity of sensing data, which is only accessible by the worker and corresponding requester; (iii) and finally, report on the implementation of a prototype and evaluate its performance. Our results demonstrate that the proposed solution can guarantee fairness without a significant increase in overhead.

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