Level Of Data Quality Research Articles

Analytic Model and Assessment Framework for Data Quality Evaluation in State Grid

With the construction of information infrastructure and the smart grid development, big data platform become an important component to support the construction of smart grid, as a consequence, the quality of the data contain in the big data platform become an important part of the process of big data platform construction. In this paper, a discussion about the current data quality analysis model was presented and also the typical data quality problem appears in the process of information infrastructure construction of the State Grid Corporation is analyzed. On the basis of this, the data quality assessment for the information system in the whole life cycle of the State Grid Corporation is designed. As a result, data quality can be checked by the analysis model in order to ensure the data quality level in the whole life cycle of the information system, finally a case was carried out for the preliminary analysis by using the mentioned framework, the result show that this framework can be effective in embedded into the workflow and can be helpful for the data quality assessment.

Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.

Factors Influencing Fog Computing Adoption Based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

The rapid increase of data generated has brought challenges on data quality level. Fog computing in general has been supporting the requirements of end user devices that could not be met by cloud computing solution and it is acknowledged to have a major impact on how an organisation decides to adopt for preprocessing a huge amount of data being generated by the devices. Since IoT devices generating very heterogeneous and dynamic data, there are challenges for the level of data quality. The limitation has hindered the development of fog systems framework that capable operating the dynamic execution of edge devices that handling generation and collection large amounts of data on-premise and off-premise. Thus,sufficient operations of identifying Quality of Result enable user to detect any problems when conducting the decision making. The aim of this paper is to address the factors that perceived likely to influence the adoption of fog computing in evaluating the data analysis on data transmitted from the ever increases devices.A conceptual framework has been constructed considering attributes such as heterogeneous data analysis (on-premise and off-premise) and Quality of Results (quality indicators, quality control, validity outcome and reliability outcome).Potential benefits from the implementation of this framework to organisation is it enable to provide greater value and benefits to the business process. The framework of this study could also be influencing and inhibiting the adoption of fog computing.Quality of result has higher chances to satisfy the defined industrial’s requirement. In addition, fog-computing adoption is important for serving an environment for industry to execute, monitor, and analyze a large form of data in a fog landscape.

Registration of Algal Novelties in Phycobank: Serving the scientific community and filling gaps in the global names backbone

The International Code of Nomenclature (ICN) for algae, fungi, and plants provides for nomenclatural indexing through nomenclatural repositories (Turland et al. 2018, Art. 42). Registering nomenclatural novelties and nomenclatural acts means that repositories will keep track of names (species names and names at all ranks, replacement names, names proposed for conservation or rejection, validated names) and of nomenclatural types, including lectotypes and epitypes. Thus, PhycoBank has been advocated by different players such as the International Society for Diatom Research (ISDR), the Global Biodiversity Information Facility (GBIF), and the Special Committee on Registration of Algal and Plant Names (including fossils). Aided by a grant from the German Research Foundation (DFG, JA 874/8-1), PhycoBank has been established at the BGBM Berlin as the repository for nomenclatural acts of algae. As added value, PhycoBank deals with orthographical variants in linking the published spelling of a name to the corrected one with reference to the respective article of the ICN (Turland et al. 2018, Art. 60). Almost all nomenclatural acts are the result of taxonomic issues but also have implications for the taxonomic work of specialists worldwide. The challenge for implementing a registration system like PhycoBank is to inform individual scientists as well as to feed data into data networks, to strengthen their underlying names backbone linking scientific names to occurrences. Since June 2018, PhycoBank staff are operating the registration system using a user-friendly data entry web application. This interface for data entry by volunteers has been available since March 2019. All data entered into the system undergoes a curatorial process to assure a high level of data quality. The data entry web application is complemented by a public data access portal which is available under https://www.phycobank.org (Fig. 1). PhycoBank can be searched for scientific names (including unregistered formal or informal higher rank names), for categories of types and PhycoBank identifiers. PhycoBank assigns resolvable and globally unique HTTP-based identifiers for nomenclatural acts, e.g. for the genus Iconella, https://phycobank.org/100040. Via these PhycoBank identifiers, the corresponding data and metadata can be retrieved in human- and machine-readable formats. More than ten journals have published PhycoBank identifiers so far, allowing cross-linking between their PDF and the PhycoBank system. The Pensoft journals are pioneering an automatic registration workflow modeled and specified by the PhycoBank team. Classifications are frequently subject to changes. Currently, the algal classification is under discussion because of results from phylogenetic research. PhycoBank aims to be neutral with respect to higher classification, but tracks classification information of each name that is registered into a directed graph of available higher rank names to record fragments of higher classification information and to facilitate search functionalities. All scientists, editors, and publishers involved in the publication of nomenclatural novelties are invited to contact PhycoBank (curation@phycobank.org) to influence the prototypical registration process and to improve PhycoBank’s functionality.

Reinforcement learning-based cell selection in sparse mobile crowdsensing

Sparse Mobile Crowdsensing (MCS) is a novel MCS paradigm which allows us to use the mobile devices to collect sensing data from only a small subset of cells (sub-areas) in the target sensing area while intelligently inferring the data of other cells with quality guarantee. Since selecting sensed data from different cell sets will probably lead to diverse levels of inference data quality, cell selection (i.e., choosing which cells in the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of data quality. To address this issue, this paper proposes the reinforcement learning-based cell selection algorithm for Sparse MCS. First, we model the key concepts in reinforcement learning including state, action, and reward, and then propose a Q-learning based cell selection algorithm. To deal with the large state space, we employ the deep Q-network to learn the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Then, we modify the Q-network to a deep recurrent Q-network with LSTM to catch the temporal patterns and handle partial observability. Furthermore, we leverage the transfer learning techniques to relieve the dependency on a large amount of training data. Experiments on various real-life sensing datasets verify the effectiveness of our proposed algorithms over the state-of-the-art mechanisms in Sparse MCS by reducing up to 20% of sensed cells with the same data inference quality guarantee.

Variability in X-ray line ratios in helium-like ions of massive stars: the wind-driven case (Corrigendum)

High spectral resolution and long exposure times are providing unprecedented levels of data quality of massive stars at X-ray wavelengths. A key diagnostic of the X-ray emitting plasma are the fir lines for He-like triplets. In particular, owing to radiative pumping effects, the forbidden-to-intercombination line luminosity ratio, R=f/i, can be used to determine the proximity of the hot plasma to the UV-bright photospheres of massive stars. Moreover, the era of large observing programs additionally allows for investigation of line variability. This contribution is the second to explore how variability in the line ratio can provide new diagnostic information about distributed X-rays in a massive star wind. While there are many ways to drive variability in the line ratio, we use variable mass loss as an illustrative example. The f/i ratio can be significantly modulated owing to evolving wind properties. We evaluate how variable mass loss might bias measures of f/i.

Overview and quality assurance for the oral health component of the National Health and Nutrition Examination Survey (NHANES), 2011\u20132014

BackgroundFollowing implementation in 2009–2010 to the oral health component for the National Health and Nutrition Examination Survey (NHANES), a full-mouth periodontal examination was continued during 2011–2014. Additionally, a comprehensive dental caries assessment was re-introduced in 2011 after a 6-year absence from NHANES. This report provides oral health content information and results of dental examiner reliability statistics for key intraoral assessments conducted by dentists during 2011–2014.MethodsDuring the 2011–2014 NHANES 17,463 persons age 1 and older representing the US civilian, non-institutionalized population received an oral health examination. From this group, 387 individuals underwent a repeat examination conducted by the survey reference examiner. A combination of examiner training and calibration, electronic data capture, and ongoing performance evaluation with statistical monitoring was used to ensure conformance with NHANES protocols and data comparability to prior data collection periods.ResultsDuring 2011–2014, the Kappa statistics for the tooth count assessment ranged from 0.96 to 1.00, for untreated dental caries Kappa scores were 0.93 to 1.00. The overall Kappa statistics for identifying combined moderate-severe periodontitis using the CDC/AAP case definition was 0.66 and 0.69 with percent agreement of 83 to 85% during 2011–2014. When evaluating inter-examiner agreement using information collected from 3 periodontal sites for comparability to the NHANES 2003–04 periodontal examination protocols, Kappa scores for combined moderate-severe periodontitis was 0.65 and 0.80 during 2011–2014. For total mean attachment loss and pocket depth across all 6 periodontal sites, the inter-class coefficients (ICCs) ranged from 0.80–0.90 and 0.79–0.86 respectively. Site-specific mean attachment loss ICCs were generally higher for the 4 interproximal measurements compared to the 2 mid-site probing measurements and this observation was similar in 2009–2010.ConclusionDuring 2011–2014, results overall indicate a high level of data quality and substantial examiner reliability for tooth count and dentition; reliability for periodontal disease, across various assessments, was at least moderate. When comparing the 2011–2014 examiner performance to findings from 2003 to 2004, comparable concordance between the examiners and the reference examiner exists.

Variability in X-ray line ratios in helium-like ions of massive stars: the wind-driven case

Context. High spectral resolution and long exposure times are providing unprecedented levels of data quality of massive stars at X-ray wavelengths. Aims. A key diagnostic of the X-ray emitting plasma are the fir lines for He-like triplets. In particular, owing to radiative pumping effects, the forbidden-to-intercombination line luminosity ratio, R = f∕i, can be used to determine the proximity of the hot plasma to the UV-bright photospheres of massive stars. Moreover, the era of large observing programs additionally allows for investigation of line variability. Methods. This contribution is the second to explore how variability in the line ratio can provide new diagnostic information about distributed X-rays in a massive star wind. We focus on wind integration for total line luminosities, taking account of radiative pumping and stellar occultation. While the case of a variable stellar radiation field was explored in the first paper, the effects of wind variability are emphasized in this work. Results. We formulate an expression for the ratio of line luminosities f∕i that closely resembles the classic expression for the on-the-spot result. While there are many ways to drive variability in the line ratio, we use variable mass loss as an illustrative example for wind integration, particularly since this produces no variability for the on-the-spot case. The f∕i ratio can be significantly modulated owing to evolving wind properties. The extent of the variation depends on how the timescale for the wind flow compares to the timescale over which the line emissivities change. Conclusions. While a variety of factors can ellicit variable line ratios, a time-varying mass-loss rate serves to demonstrate the range of amplitude and phased-dependent behavior in f∕i line ratios. Importantly, we evaluate how variable mass loss might bias measures of f∕i. For observational exposures that are less than the timescale of variable mass loss, biased measures (relative to the time-averaged wind) can result; if exposures are long, the f∕i ratio is reflective of the time-averaged spherical wind.

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

Can Field Crews Telecommute? Varied Data Quality from Citizen Science Tree Inventories Conducted Using Street-Level Imagery

Street tree inventories are a critical component of urban forest management. However, inventories conducted in the field by trained professionals are expensive and time-consuming. Inventories relying on citizen scientists or virtual surveys conducted remotely using street-level photographs may greatly reduce the costs of street tree inventories, but there are fundamental uncertainties regarding the level of data quality that can be expected from these emerging approaches to data collection. We asked 16 volunteers to inventory street trees in suburban Chicago using Google Street ViewTM imagery, and we assessed data quality by comparing their virtual survey data to field data from the same locations. We also compared virtual survey data quality according to self-rated expertise by measuring agreement within expert, intermediate, and novice analyst groups. Analyst agreement was very good for the number of trees on each street segment, and agreement was markedly lower for tree diameter class and tree identification at the genus and species levels, respectively. Interrater agreement varied by expertise, such that experts agreed with one another more often than novices for all four variables assessed. Compared to the field data, we observed substantial variability in analyst performance for diameter class estimation and tree identification, and some intermediate analysts performed as well as experts. Our findings suggest that virtual surveys may be useful for documenting the locations of street trees within a city more efficiently than field crews and with a high level of accuracy. However, tree diameter and species identification data were less reliable across all expertise groups, and especially novice analysts. Based on this analysis, virtual street tree inventories are best suited to collecting very basic information such as tree locations, or updating existing inventories to determine where trees have been planted or removed. We conclude with evidence-based recommendations for effective implementation of this type of approach.

Evaluating the utility of elemental measurements obtained from factory-calibrated field-portable X-Ray fluorescence units for aquatic sediments

We assessed factory-calibrated field-portable X-ray fluorescence (pXRF) data quality for use with minimally-prepared aquatic sediments, including the precision of replicate pXRF measurements, accuracy of factory-calibrated pXRF values as compared to total digestion/ICP-OES concentrations, and comparability of calibrated pXRF values to extractable concentrations. Data quality levels for precision, accuracy, and comparability were not equivalent for element/analyzer combinations. All analyses of elements that were assessed for precision and accuracy on a single analyzer were both precise (<10% relative standard deviation) and accurate (r2 > 0.85) for K, Ca, Ti, Mn, Fe, and Zn. Calibrated pXRF values for Al, K, Ca, Ti, Mn, Fe, Cu, Zn, and Pb were within ∼10% relative difference of total digestion/ICP-OES concentrations. Calibrated pXRF values for Fe, Cu, Zn, As, and Pb were within ∼20% relative difference of extractable concentrations. Some elements had a higher level of data quality using specific analyzers, but in general, no pXRF analyzer had the highest level of data quality in all categories. Collectively, our data indicate that a wide range of factory-calibrated pXRF units are capable of providing high-quality total concentrations for the analysis of aquatic sediments.

Moving psychological assessment out of the controlled laboratory setting: Practical challenges.

Behavioral assessment using smart devices affords novel methods, notably remote self-administration by the individuals themselves. However, this new approach requires navigating complex legal and technical terrain. Given the limited empirical data that currently exists, we provide and discuss anecdotes of the methodological, technical, legal, and cultural issues associated with an implementation in both U.S. and European settings of a mobile software application for regular psychological monitoring purposes. The tasks required participants to listen, watch, speak, and touch to interact with the smart device, thus assessing cognition, motor skill, and language. Four major findings merit mention: First, moving assessment out of the hands of a trained investigator necessitates excellent usability engineering, such that the tool is easily usable by the participant and the resulting data relevant to the investigator. Second, remote assessment requires that the data are transferred safely back to the investigator, and that risk of compromising participant confidentiality is minimized. Third, frequent data collection over long periods of time is associated with a possibility that participants may choose to withdraw consent for participation thus requiring data retraction. Fourth, data collection and analysis across international borders creates new challenges and new opportunities because of important cultural and language issues that may inform the underlying behavioral constructs of interest. In conclusion, the new technological frameworks provide unprecedented opportunities for remote self-administered behavioral assessments but will be most productive in multidisciplinary teams to ensure the highest level of user satisfaction and data quality, and to guarantee the highest level of data protection. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Is observation uncertainty masking the signal of land use change impacts on hydrology?

Analysis of hydrological impacts of land use change raises questions about whether, and how much, such impacts are misrepresented because of errors in river flow observations. In this paper, land use change impacts (represented by changes in watershed storage) and different ranges of discharge measurement error are compared to assess how errors in discharge measurement can potentially mask a land use change impact. Using a watershed from the Ethiopian highlands to exemplify this, we simulated five different levels of land use change impacts with five levels of watershed storage reductions (from 10% to 50% change) and the associated time series of runoff. Different levels of observation error were then introduced into these artificial time series. Comparison was made between every pair, i.e. a time series derived from a certain level of land use change (storage reduction) versus a time series corresponding to a given level of observation error, using a step-change t-test. Significant step-changes between pairs define the detectability of land use change impact. The analysis was made for the entire 30-year time series as well as for the most extreme annual weather conditions. The results showed that for the average year and wettest year, 75% or more error in observed discharge masks the maximum simulated land use change impact on hydrology. In dry years, a 50% error in discharge is enough to mask the same impact. Knowing (and improving) the level of data quality contributes to a better understanding of hydrological uncertainties and improves the precision in assessing land use change impacts. Both of these are essential elements in water resources development planning.

Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?

Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.

An overview of primary registries of WHO's international clinical trial registry platform

Introduction:WHO's International Clinical Trials Registry Platform (ICTRP) has 17 primary registries that collect the information on the minimum set of items of trial information that appear in the register and these registries are also endorsed by the International Committee of Medical Journal Editors.Objective:The objective of this study is to describe the profile of all the primary registries including Clinical Trial Registry‑India (CTRI), through features such as magnitude, domain of registration, flagging, audit trail, language, mandatory requirements, and result disclosure.Methodology:The profiling of all registries was based on countries and zones, year of establishment, registrant, flagging, conflict of interest, language, documents, result disclosure, type of study, mode of registration, mandate of registration, quality check method, individual patient data statement and translation of content facility. The mode of search used was online which included advanced search, basic search and also from the audio/video manual on their website.Results:There are 17 primary registries of ICTRP, the first one International Standard Randomised Controlled Trial Number (ISRCTN) of England being initiated in year 2000 and the most recent being Lebanese registry, in September 2019. The trials registered with these registries range from 301 in Cuba to 53972 in European union's EU Clinical Trials Register. The primary registries in WHO registry network are diverse in functionalities and practices. The characteristics of online registers vary in content and features and to achieve coordinated level of data quality, across all the different registries and to keep a balance in standards of the data collected and validation of that data, the registries are adhering to the minimum data set items laid down by ICTRP.Conclusion:The very process of registering the clinical studies helps in promoting the research methods and also raising the standards of research, especially among young researchers. It also helps in reducing the duplicity of research.

Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets

Infrastructure forms the backbone of a nation's economy, and there is a growing demand for a technology that offers quick, safe, and reliable infrastructure performance monitoring data, which would lead to proactive maintenance or repairs of the infrastructure to ensure its quality performance as per the original design. The increase in the applications of unmanned aerial vehicles (UAVs) or drones for such monitoring purposes have been propelled by advancements in aerial vehicle platforms and image-analyses software. Evaluating UAVs for high-quality infrastructure-distress data monitoring has been a major focus for this research. Photogrammetry is the science of measuring distances from two or multiple images of an object and close range photogrammetry (CRP) can be accomplished by using an optical camera mounted on a UAV platform and taking the images of the infrastructure object. This technology, hereafter termed as UAV-CRP technology, utilizes UAVs coupled with high quality camera lens and a global navigation satellite system (GNSS) device to collect high quality data with fewer ground control points (GCPs). The various pieces of equipment need to have their aerial technology compatibility tested in different environmental conditions and evaluate the imagery that is used for image analyses and interpretations. A comprehensive set of calibration checks were executed using the total system comprising of UAV and accessories to evaluate the imagery quality level of transportation and geotechnical infrastructure data. All the checks performed ensured that the data obtained from the UAV-CRP technology meets the quality requirements for infrastructure data. A case study of pavement infrastructure data was provided to validate the high accuracy UAV-CRP data obtained following the guidelines developed in this paper.

Implementation of the bin hierarchy method for restoring a smooth function from a sampled histogram

We present BHM, a tool for restoring a smooth function from a sampled histogram using the bin hierarchy method. It is particularly useful for the analysis of data from large-scale numerical simulations of physical systems, such as diagrammatic Monte Carlo simulations of quantum many-body problems. The theoretical background of the method is presented in Goulko et al., (2018). The code automatically generates a smooth polynomial spline with the minimal acceptable number of knots from the input data. It works universally for any sufficiently regular shaped distribution and any level of data quality (provided that the data are uncorrelated or correlations have been accounted for), requiring almost no external parameter specification. This paper explains the details of the implementation and the use of the program, including a physical example of the restoration of the Fröhlich polaron Green’s function from data sampled with diagrammatic Monte Carlo. Program summaryProgram Title: BHMProgram Files doi:http://dx.doi.org/10.17632/dvj8gxsxpk.1Licensing provisions: GPLv3Programming language: C++External routines/libraries: CMake, GSLNature of problem: Restoring a smooth function from a sampled histogram.Solution method: To make use of all information contained in the sampled data, the BHM algorithm generates a hierarchy of overlapping bins of different sizes from the initially supplied fine histogram. The bin hierarchy is fitted to a polynomial spline with the minimal acceptable number of knots, the positions of which are determined automatically. The output is a smooth function with error band.

Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

Sensor networks empower Internet of Things (IoT) applications by connecting them to physical world measurements. However, the necessary use of limited bandwidth networks and battery-powered devices makes their optimal configuration challenging. An over-usage of periodic sensors (i.e. too frequent measurements) may easily lead to network congestion or battery drain effects, and conversely, a lower usage is likely to cause poor measurement quality. In this paper we propose a middleware that continuously generates and exposes to the sensor network an energy-efficient sensors configuration based on data live observations. Using a live learning process, our contributions dynamically act on two configuration points: (i) sensors sampling frequency, which is optimized based on machine-learning predictability from previous measurements, (ii) network usage optimization according to the frequency of requests from deployed software applications. As a major outcome, we obtain a self-adaptive platform with an extended sensors battery life while ensuring a proper level of data quality and freshness. Through theoretical and experimental assessments, we demonstrate the capacity of our approach to constantly find a near-optimal tradeoff between sensors and network usage, and measurement quality. In our experimental validation, we have successfully scaled up the battery lifetime of a temperature sensor from a monthly to a yearly basis.

Open-data-driven embeddable quality management services for map-based web applications

ABSTRACTVarious map-centered web services facilitate citizens’ lives. Web-map applications exist for many years already. Due to simplification and improvement of technologies supporting WebGIS, map-based services become more popular and important nowadays. Data quality assurance for such services is a significant challenge. Since many of such applications intensively use open data, approaches focused on open solutions are required. This work proposes a data-quality concept, which is based on intrinsic and comparable approaches. OpenStreetMap (OSM) allows intrinsic data evaluation. Moreover, it is used as a reference dataset for quality assessment of public-sector-information Open Data layers. Equidistant point (EDP)-based statistics enables to filter out low-quality Open Data features. A data-type model carries out the inventory of OSM data. The comparison of raster web-map tile file sizes and calculation of a simplified data quality indicator make it possible to specify acceptable data quality levels. Embeddable instances of quality assurance web services incorporate data features with acceptable quality. This work provides all required software and data for the deployment of such services under liberal licenses. Concrete instructions allow users to adopt the proposed solutions for their platforms. Some generic use cases illustrate the advantages of the introduced shared web services.

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.