Data Quality Elements Research Articles

Data quality issues in production planning and control – Linkages to smart PPC

In the era of Industry 4.0 and digitalization, planning solutions need to co-exist with each other and be able to manage higher complexity and with a higher performance. As the concept smart production planning and control is a part of industry 4.0, it is highly relevant to study and is in this paper explored on the four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning). This paper provides a framework for linking the four elements of smart PPC with data quality issues in state-of-the-art production planning and control environments. Maintaining a high standard of data quality in the business processes aids the organization to stay competitive in its market. Hence, our assumption is that a high level of data quality is needed in production planning and control for a high-performance outcome. The empirical part of our study results in a bar-chart of seven data quality problems and their occurrences together with their causes in PPC. According to the empirical data results, inaccurate data entries is the most common data quality problem related to PPC. The causes of the inaccurate data entries can be linked to human resources and organizational control. Future research should strengthen the validity of the proposed linkages between data quality problems and elements of smart PPC and implications on strategic, tactical, and operational planning levels.

Factors affecting data quality of online questionnaires: Issues and metrics for sensory and consumer research

Methodology used in sensory and consumer research has changed rapidly over the past several decades, and consumer research is now frequently executed online. This comes with advantages, but also with the potential for poor quality data due to uncontrolled elements of survey administration and execution. Published papers that utilize online data rarely report metrics of data quality or note precautions that were taken to ensure valid data. The aim of this paper is to raise awareness of the factors that influence online data quality. This is achieved by 1) identifying factors that can impact the reliability and validity of consumer data obtained with online questionnaires, 2) highlighting indices of online questionnaire data quality that can be used by researchers to assess the likelihood that their data are of poor quality, and 3) recommending a number of indices, counter-measures and best practices that can be used by sensory and consumer researchers to assess online questionnaire data quality and to ensure the highest degree of reliability and validity of their data. By making researchers aware of these sources of invalidity and by presenting available remedies and good practices, it is our intention that sensory and consumer scientists will adopt these measures and report them as a routine part of publishing online questionnaire data. Specific suggestions are offered regarding the important elements of data quality that should be included in scientific manuscripts.

Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development

One potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators is OpenStreetMap (OSM). The purpose of this paper is to provide a comprehensive evaluation of the spatial data quality elements of OSM against the national official data—the database of topographic objects at a scale of 1:10,000. Such spatial data quality elements as location accuracy, data completeness and attribute compatibility were analysed. In the conducted OpenStreetMap tests, basic land-cover classes such as roads, railroads, river network, buildings, surface waters and forests were analysed. The test area of the study consisted of five counties in Poland, which differ in terms of location, relief, surface area and degree of urbanization. The best results of the quality of OSM spatial data were obtained for highly urbanized areas with developed infrastructure and a high degree of affluence. The highest degree of completeness of OSM linear and area objects in the studied counties was acquired in Piaseczyński County (82%). The lowest degree of completeness of the line and area objects of OSM in the studied counties was obtained in the Ostrowski County (51%). The calculated correlation coefficient between the quality of OSM data and the income per capita in the county was 0.96. The study complements the previous research results in the field of quantitative analysis of the quality of OSM data, and the obtained results confirm their dependence on the geometric type of the analysed objects and characteristics of test areas, i.e., in this case counties in Poland. The obtained results of OSM data quality analysis indicate that OSM data may provide strong support for other spatial data, including official and state data. OSM stores significant amounts of geospatial data with relatively high data quality that can be a valuable source for monitoring some SDG indicators.

Quality Assurance for Spatial Data Collected in Fit-for-Purpose Land Administration Approaches in Colombia

The Fit-For-Purpose Land Administration (FFPLA) approach uses flexible techniques under basic regulations, avoiding complicated systems and aiming to fulfill the objective of land tenure security for all. In addition, a land administration system should evolve, starting as a simple system in rural areas and gradually evolving into a more complex system in more populated areas where requirements and quality increase progressively. The system can develop to a precision system. Implementing the FFPLA methodology in Colombia has allowed processes to be developed for data capture in the field using real-time technology and efficient methods for information management. These processes are under quality control by applying technical specifications in alignment with the FFPLA principles. This article presents the results of creating a FFPLA quality assurance model, which includes the application of the ISO 19100 family of technical standards based on the product’s life cycle and quality model concepts. Furthermore, the article documents essential aspects for controlling the quality of the parcel boundary data collected in the field, using direct and indirect methods to measure the applicable spatial data quality elements (logical consistency and positional accuracy) preserving FFPLA principles.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

The Rise of the Personal Data Quality Principle. Is it Legal and Does it Have an Impact on the Right to Rectification?

EU data protection law anchors the principle of data accuracy and connects it to the right to rectification. Both provisions have remained underexplored in literature, leaving a signifi-cant academic gap. In the present paper I will argue that there is a tendency to replace ‘data accuracy’ with ‘data quality,’ which seems to subsume and go beyond classical accuracy elements, i.e. it is broader than accuracy. It also includes requirements on the quality of pro-cessing, not simply on the data pieces. Elements of data quality, as derived from non-legal disciplines, could be traced to different principles of EU data protection law. As a result, the scope of the right to rectification could be broader than it is currently argued to be and could give rise to a broader range of legitimate claims by data subjects.

Geospatial data quality (ISO 19157-1): evolve or perish

Given the circumstance that the process for the revision of the international standard ISO 19157 is currently open, this article presents a critical reflection on its content, application and some challenges posed by the new types of data (e.g. big data, BIM data, etc.), that also have a geospatial component and to which, therefore, this international standard can be applied as well. Proposals are put forward going along three lines of improvement, on the one hand the consideration of new data quality elements and on the other, the reinforcement of the interoperability of this international standard with other standards related to data quality, and finally various improvements (e.g. standardization of evaluation methods, clearly introducing the life cycle, improvement of the definition of metaquality, etc.) of the standard, which come from experience.

Geospatial data quality (ISO 19157-1): evolve or perish

Given the circumstance that the process for the revision of the international standard ISO 19157 is currently open, this article presents a critical reflection on its content, application and some challenges posed by the new types of data (e.g. big data, BIM data, etc.), that also have a geospatial component and to which, therefore, this international standard can be applied as well. Proposals are put forward going along three lines of improvement, on the one hand the consideration of new data quality elements and on the other, the reinforcement of the interoperability of this international standard with other standards related to data quality, and finally various improvements (e.g. standardization of evaluation methods, clearly introducing the life cycle, improvement of the definition of metaquality, etc.) of the standard, which come from experience.

Quality Control of “As Built” BIM Datasets Using the ISO 19157 Framework and a Multiple Hypothesis Testing Method Based on Proportions

Building information model (BIM) data are digital and geometric-based data that are enriched thematically, semantically, and relationally, and are conceptually very similar to geographic information. In this paper, we propose both the use of the international standard ISO 19157 for the adequate formulation of the quality control for BIM datasets and a statistical approach based on a binomial/multinomial or hypergeometric (univariate/multivariate) model and a multiple hypothesis testing method. The use of ISO 19157 means that the definition of data quality units conforms to data quality elements and well-defined scopes, but also that the evaluation method and conformity levels use standardized measures. To achieve an accept/reject decision for quality control, a statistical model is needed. Statistical methods allow one to limit the risks of the parties (producer and user risks). In this way, several statistical models, based on proportions, are proposed and we illustrate how to apply several quality controls together (multiple hypothesis testing). All use cases, where the comparison of a BIM dataset versus reality is needed, are appropriate situations in which to apply this method in order to supply a general digital model of reality. An example of its application is developed to control an “as-built” BIM dataset where sampling is needed. This example refers to a simple residential building with four floors, composed of a basement garage, two commercial premises, four apartments, and an attic. The example is composed of six quality controls that are considered simultaneously. The controls are defined in a rigorous manner using ISO 19157, by means of categories, scopes, data quality elements, quality measures, compliance levels, etc. The example results in the rejection of the BIM dataset. The presented method is, therefore, adequate for controlling BIM datasets.

ExpoQual: Evaluating measured and modeled human exposure data

Recent rapid technological advances are producing exposure data sets for which there are no available data quality assessment tools. At the same time, regulatory agencies are moving in the direction of data quality assessment for environmental risk assessment and decision-making. A transparent and systematic approach to evaluating exposure data will aid in those efforts. Any approach to assessing data quality must consider the level of quality needed for the ultimate use of the data. While various fields have developed approaches to assess data quality, there is as yet no general, user-friendly approach to assess both measured and modeled data in the context of a fit-for-purpose risk assessment. Here we describe ExpoQual, an instrument developed for this purpose which applies recognized parameters and exposure data quality elements from existing approaches for assessing exposure data quality. Broad data streams such as quantitative measured and modeled human exposure data as well as newer and developing approaches can be evaluated. The key strength of ExpoQual is that it facilitates a structured, reproducible and transparent approach to exposure data quality evaluation and provides for an explicit fit-for-purpose determination. ExpoQual was designed to minimize subjectivity and to include transparency in aspects based on professional judgment. ExpoQual is freely available on-line for testing and user feedback (exposurequality.com).

ACCURACY DIMENSIONS IN REMOTE SENSING

Abstract. The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs. The present paper gives the theoretic overview of the issue, besides selected, practice-oriented approaches are evaluated too, finally widely-used dimension metrics like Root Mean Squared Error (RMSE) or confusion matrix are discussed. The authors present data quality features of well-defined and poorly defined object. The central part of the study is the land cover mapping, describing its accuracy management model, presented relevance and uncertainty measures of its influencing quality dimensions. In the paper theory is supported by a case study, where the remote sensing technology is used for supporting the area-based agricultural subsidies of the European Union, in Hungarian administration.

Assessment of OpenStreetMap (OSM) Data: The Case of Abu Dhabi City, United Arab Emirates

Geographic Information Systems (GIS) data are crucial for many applications such as disaster response, damage assessment, and recovery efforts. The 2010 Haiti earthquake destroyed buildings where GIS data were stored and many of the relief organizations ultimately depended on data retrieved from outside the country to do their work. The lesson learnt from this experience is that open access data is necessary in assisting tasks in such situations and therefore the evaluation of such data for its quality is important. This research evaluates OpenStreetMap (OSM) data that can be and is used by various organizations. The methodology followed is based on the International Organization for Standardization (ISO) Geographic Information-Quality Principles (ISO19113). According to the ISO19113 standard, the data quality elements are: completeness, logical consistency, positional accuracy, thematic accuracy, and temporal accuracy. The first four elements noted above were tested in this study. OSM data was compared with official data from Abu Dhabi city. Results show that the positional accuracy of OSM data meets the American Society for Photogrammetry and Remote Sensing (ASPRS) standards for 1:20,000-scale maps. This indicates that OSM data could be used for many applications, taking into account the hurdles in GIS data acquisition, cost, availability, licensing, distribution policies, and documentation. The article concludes with a call for incorporation of geospatial data services and data quality initiatives in libraries.

Quality control and new data-quality measures for the aesthetics of a Croatian topographic map at the scale of 1:25,000

ABSTRACTA brief overview is given of the quality control of a 1:25,000 scale topographic map, carried out by the Croatian Geodetic Institute (CGI). As the topic of the aesthetics of the topographic map has, in the authors’ opinion, not been extensively covered, the paper concentrates on this topic. The paper focuses on devising an aesthetic standard relating mainly to the representation of relief. In fact, aesthetics can be controlled indirectly in terms of relief and water course alignment, the smoothness of contour lines, the non-existence of bonded contour lines and rock representation justification. A comparison between quality control at the CGI and quality elements according to contemporary standards was carried out. The presence of data-quality elements was registered in accordance with the ISO guidelines. A proposal for several data-quality measures for aesthetics related to relief representation is also given.

Quality Assessment of Crowdsourced Data against Custom Recorded Map Data

Web 2.0 has impelled crowdsourcing among Internet users. Crowdsourcing has resulted into humongous Wikification of maps, where contributors vary from commercial navigation map providers to nave users exploring cartography. As OpenStreetMap (OSM) does not enforce minimum quality model onto contributed data, therefore data is more prone to errors and incomplete coverage. Though data is espoused by various commercial applications yet it lacks standardization, completeness and needed quality. During this research a GPS and GLONASS enabled smartphone has been used to record GPS coordinates and Open Source Tool JOSM used for editing. The OSM dataset compared against ground data. The datasets analysed based on specific elements of data quality defined by ISO/TC211, such as on coverage, accuracy and completeness. The comparison suggested notable variance in the datasets analysed.

A THEORETICAL FRAMEWORK OF DATA QUALITY IN PARTICIPATORY SENSING: A CASE OF MHEALTH

Research in data quality is important in participatory sensing area to provide integrity of the data contributed by participants in mHealth participatory campaign. Many factors can influence the integrity of data contribution. One of major concerns is the possibility of data truthfulness of being uncertain due to incompleteness, imprecision, vagueness, and fragmentary. In participatory sensing, the interpretation of data quality is rather loose and there is no established theoretical framework that represents the elements of data quality in mHealth participatory sensing system. Therefore, the objective of this paper is two-fold: First, to investigate the variables of data quality that suits participatory sensing system. Second to propose a theoretical framework of data quality in mHealth participatory sensing. The finding will serve a guideline of data quality in mhealth participatory sensing.

Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany

Volunteered Geographic Information (VGI) such as data derived from the OpenStreetMap (OSM) project is a popular data source for freely available geographic data. Normally, untrained contributors gather these data. This fact is frequently a cause of concern regarding the quality and usability of such data. In this study, the quality of OSM land use and land cover (LULC) data is investigated for an area in southern Germany. Two spatial data quality elements, thematic accuracy and completeness are addressed by comparing the OSM data with an authoritative German reference dataset. The results show that the kappa value indicates a substantial agreement between the OSM and the authoritative dataset. Nonetheless, for our study region, there are clear variations between the LULC classes. Forest covers a large area and shows both a high OSM completeness (97.6%) and correctness (95.1%). In contrast, farmland also covers a large area, but for this class OSM shows a low completeness value (45.9%) due to unmapped areas. Additionally, the results indicate that a high population density, as present in urbanized areas, seems to denote a higher strength of agreement between OSM and the DLM (Digital Landscape Model). However, a low population density does not necessarily imply a low strength of agreement.

ONTOLOGY BASED QUALITY EVALUATION FOR SPATIAL DATA

Abstract. Many institutions will be providing data to the National Spatial Data Infrastructure (NSDI). Current technical background of the NSDI is based on syntactic web services. It is expected that this will be replaced by semantic web services. The quality of the data provided is important in terms of the decision-making process and the accuracy of transactions. Therefore, the data quality needs to be tested. This topic has been neglected in Turkey. Data quality control for NSDI may be done by private or public “data accreditation” institutions. A methodology is required for data quality evaluation. There are studies for data quality including ISO standards, academic studies and software to evaluate spatial data quality. ISO 19157 standard defines the data quality elements. Proprietary software such as, 1Spatial’s 1Validate and ESRI’s Data Reviewer offers quality evaluation based on their own classification of rules. Commonly, rule based approaches are used for geospatial data quality check. In this study, we look for the technical components to devise and implement a rule based approach with ontologies using free and open source software in semantic web context. Semantic web uses ontologies to deliver well-defined web resources and make them accessible to end-users and processes. We have created an ontology conforming to the geospatial data and defined some sample rules to show how to test data with respect to data quality elements including; attribute, topo-semantic and geometrical consistency using free and open source software. To test data against rules, sample GeoSPARQL queries are created, associated with specifications.

A REPRESENTATION-DRIVEN ONTOLOGY FOR SPATIAL DATA QUALITY ELEMENTS, WITH ORTHOIMAGERY AS RUNNING EXAMPLE

Abstract. The many facets of what is encompassed by such an expression as “quality of spatial data” can be considered as a specific domain of reality worthy of formal description, i.e. of ontological abstraction. Various ontologies for data quality elements have already been proposed in literature. Today, the system of quality elements is most generally used and discussed according to the configuration exposed in the “data dictionary for data quality” of international standard ISO 19157. Our communication proposes an alternative view. This is founded on a perspective which focuses on the specificity of spatial data as a product: the representation perspective, where data in the computer are meant to show things of the geographic world and to be interpreted as such. The resulting ontology introduces new elements, the usefulness of which will be illustrated by orthoimagery examples.

Evaluation of the timeliness and completeness of communicable disease reporting: Surveillance in The Cuban Hospital, Qatar.

Public health surveillance systems should be evaluated periodically, and should involve an assessment of system attributes. Objective: Evaluate hospital-based surveillance of communicable diseases using the elements of timeliness and data quality. Method: Descriptive study was conducted of communicable diseases reported at The Cuban Hospital, Qatar during January 2012 to December 2013. The completeness of notifications were assessed for contact number, address, place of work, and date of symptom onset. Time between the symptoms onset and physician notification, time between physician and Supreme Council of Health notification and time between physician notification and lab confirmation were calculated for each case. Analysis: Percentage of cases with documented essential information and 95% confidence interval (CI) were determined. Mean and standard deviation (SD) of time were calculated. Results: 1065 patients were reported, 75% were male, 80% non-qataries and 91.5% were group 1 (high priority) diseases. Symptom onset date was documented in 91.5% (95% CI, 89.8; 93.2) of cases; contact number in 84.7% (82.5;86.8), with lower frequencies for address (68.1%, 65.3;70.9) and place of work (60.5%, 57.5;63.4). Diagnostic time for tuberculosis was 61.7 days (SD 93.0), acute hepatitis 18.5 days (SD 17.6), typhoid fever 17.0 days (SD 11.6 days), other diseases of sexual transmission 300.2 days, chronic hepatitis 165 days and AIDS 154.5 days. The time of notification to the Supreme Council of Health for group 1 diseases was 1.2 days (SD 1.4). Conclusion: Our results show that the quality of essential data and timeliness is not sufficient to meet the needs of the health system. Additional studies should focus on the evaluation of time delay for diagnosis of high priority diseases.

The role of AVO in prospect risk assessment

Essentially all companies exploring for oil and gas should perform a risk analysis to understand the uncertainties in their interpretations and to properly value order prospects in a company’s drilling portfolio. For conventional exploration in clastic environments, primarily sands encased in shales, a key component of the risk analysis process is evaluating direct hydrocarbon indicators, which can have a significant impact on the final risk value. We investigate the role AVO plays in the risk assessment process as a portion of a comprehensive and systematic DHI evaluation. Documentation of the geologic context and quantification of data quality and DHI characteristics, including AVO characteristics, is necessary to properly assess a prospect’s risk. A DHI consortium database of over 230 drilled prospects provides statistics to determine the importance of data quality elements, primarily in class 2 and 3 geologic settings. The most important AVO interpretation characteristics are also identified based on statistical results and correlated with well success rates. A significant conclusion is the relevance of AVO in risk analysis when it is the dominant component in the DHI portion of the risk. Critical in the risk assessment process is understanding the role AVO and DHI analysis play when prospects approach class 1 geologic settings. The impact that hydrocarbons have on the seismic response is significantly diminished in this setting versus the other AVO classes. All of these observations confirm the necessity of properly evaluating a prospect’s geologic setting and implementing a consistent and systematic risk analysis process including appropriate DHI and AVO components.