Data Quality (Computer Science)

Data quality is crucial in all information systems. As a
key step in obtaining clean data, record linkage or entity resolution (ER)
groups database records by the underlying real world entities. In this pa-
per we give practical motivating examples and review the available ER
formal models. The formal model for matching and merging records de-
termines not just the power and quality, but also the algorithmic cost of
the resolution process. Starting from a naive definition that may lead to
unbounded entities or infinite loops and also discussing the shortcomings
of the standard axioms, we give algebraic properties that lead to efficient
record partitioning. Finally we describe algorithms suitable for complex
entity resolution problems that may include fuzzy clustering to split a par-
tition of records into potentially overlapping entities.

OpenStreetMap (OSM) is a free map of the world which can be edited by global volunteers. Existing studies have showed that completeness of OSM road data in some developing countries (e.g. China) is much lower, resulting in concern in utilizing the data in various applications. But very few have focused on investigating what types of road are still poorly mapped. This study aims not only to investigate the completeness of OSM road datasets in China but also to investigate what types of road (called omission roads) have not been mapped, which is achieved by referring to both Street Map and Street View. Sixteen prefecture-level divisions in the urban areas of Hubei (China) were used as study areas. Results showed that: (1) the completeness for most prefecture-level divisions was at a low-to-medium level; most roads (in the Street Map), however, with traffic conditions had already been mapped well. (2) Most of the omission OSM roads were either private roads, or public roads not having yet been named and with only one single lane, indicating their minor importance in the urban road network. We argue that although the OSM road datasets in China are incomplete, they may still be used for several applications such as traffic flow analysis, road map representation, and backbone structure detection.

Victims of road traffic accidents face severe health problems on-site or after the event when they arrive at hospital lately in their emergency cycle. Road traffic accident has negative effect on the physical, social and emotional security of human lives which often lead to mortality, illness, pain, grief and even disability. This paper proposes a scheme that reduces the severity of road traffic accidents given its inevitable occurrence. The rational is to search for nearest hospitals to the accident location using Dijkstra algorithm and Fuzzy logic to recommend suitable hospitals out of list of nearest hospitals to timely attend to the emergency situation considering factors such as distance, severity of the accident, available facilities in the hospitals and other factors. The obtained results showed the practicability of the system to recommendation of quick solution to accident emergencies.

International Conference on Data Science and Applications (DSA 2020) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Science and Applications. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest
issues and advancement in the area of Data Science & Applications

Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.

Victims of road traffic accidents face severe health problems on-site or after the event when they arrive at hospital lately in their emergency cycle. Road traffic accident has negative effect on the physical, social and emotional security of human lives which often lead to mortality, illness, pain, grief and even disability. This paper proposes a scheme that reduces the severity of road traffic accidents given its inevitable occurrence. The rational is to search for nearest hospitals to the accident location using Dijkstra algorithm and Fuzzy logic to recommend suitable hospitals out of list of nearest hospitals to timely attend to the emergency situation considering factors such as distance, severity of the accident, available facilities in the hospitals and other factors. The obtained results showed the practicability of the system to recommendation of quick solution to accident emergencies.

Victims of road traffic accidents face severe health problems on-site or after the event when they arrive at hospital lately in their emergency cycle. Road traffic accident has negative effect on the physical, social and emotional security of human lives which often lead to mortality, illness, pain, grief and even disability. This paper proposes a scheme that reduces the severity of road traffic accidents given its inevitable occurrence. The rational is to search for nearest hospitals to the accident location using Dijkstra algorithm and Fuzzy logic to recommend suitable hospitals out of list of nearest hospitals to timely attend to the emergency situation considering factors such as distance, severity of the accident, available facilities in the hospitals and other factors. The obtained results showed the practicability of the system to recommendation of quick solution to accident emergencies.

This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping them into three research perspectives: applications of OSM for studies within other disciplines, OSM data quality, and dynamics in OSM. This survey reveals that these perspectives, while being distinct, are also interrelated. This calls for the formalization of an ‘OSM science’ that will provide the conceptual grounds to advance the scientific study of OSM, not as a set of individualized efforts but as a unified approach.

In a swiftly transform global business environment, make accurate and appropriate decisions by taking advantage of business intelligence analytics is the demand on organizations. The ability to recognize challenges, spot opportunities, and adjust with agility is not a competitive edge, in contrast, is a necessary for survival. A data-driven organization has been characterized by collecting more high-quality data, have more skilled data analyst, and transparent about data. This study conducted an empirical research to analyze of influential factors for developing a data-driven organization and concentrate on exploring success and fundamental factors. Moreover, this study took advantage of collecting several papers, literature, books and case studies from different sources. This paper contains, an overview of "Characteristics of a data-driven Organization" and " Pre-requisition to building a data-driven organization" and presented six finding to support and define further about purposes of this study.

Data quality (DQ) assessment and improvement in larger information systems would often not be feasible without using suitable " DQ methods " , which are algorithms that can be automatically executed by computer systems to detect and/or correct problems in datasets. Currently, these methods are already essential, and they will be of even greater importance as the quantity of data in organisational systems grows. This paper provides a review of existing methods for both DQ assessment and improvement and classifies them according to the DQ problem and problem context. Six gaps have been identified in the classification, where no current DQ methods exist, and these show where new methods are required as a guide for future research and DQ tool development.

Data is the most valuable asset companies are proud of. When its quality degrades, the consequences are unpredictable and can lead to complete wrong insights. In Big Data context, evaluating the data quality is challenging and must be done prior to any Big data analytics by providing some data quality confidence. Given the huge data size and its fast generation, it requires mechanisms and strategies to evaluate and assess data quality in a fast and efficient way. However, checking the Quality of Big Data is a very costly process if it is applied on the entire data. In this paper, we propose an efficient data quality evaluation scheme by applying sampling strategies on Big data sets. The Sampling will reduce the data size to a representative population samples for fast quality evaluation. The evaluation targeted some data quality dimensions like completeness and consistency. The experimentations have been conducted on Sleep disorder’s data set by applying Big data bootstrap sampling techniques. The results showed that the mean quality score of samples is representative for the original data and illustrate the importance of sampling to reduce computing costs when Big data quality evaluation is concerned. We applied the Quality results generated as quality proposals on the original data to increase its quality.

Today's largest and fastest growing companies' assets are no longer physical, but rather digital (software, algorithms. . .). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises' daily maintenance data reporting (e.g., maintenance planners, plant managers.. .), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a Maintenance Reporting Quality Assessment (MRQA) dashboard that enables any company stakeholder to easily – and in real-time – assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices.

As the world is catching up with various technological developments, blockchain technology slowly catches the Malaysian attentions. In Malaysia, this technology is still in its infancy stage and is yet to gain popularity. This research is aimed at investigating the factors influencing the behavioral intention to adopt blockchain technology by the Malaysian banking institutions. Unified Theory of Acceptance and Use of Technology (UTAUT) with key determinants such as Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Condition has been adopted in this study. Using questionnaire instruments, 149 data from banking respondents in five states have been collected and analyzed. Of the four key determinants, only Effort Expectancy shows an insignificant relationship with the behavioral intention to adopt blockchain technology. This research is a contribution to scholarly writing while helping administrators’ adoption decision making.

The question of data reliability is of first importance to assess the quality of manually annotated corpora. Although Cohen ' s κ is the prevailing reliability measure used in NLP, alternative statistics have been proposed. This paper presents an experimental study with four measures (Cohen's κ, Scott's π, binary and weighted Krippendorff ' s α) on three tasks: emotion, opinion and coreference annotation. The reported studies investigate the factors of influence (annotator bias, category prevalence, number of coders, number of categories) that should affect reliability estimation. Results show that the use of a weighted measure restricts this influence on ordinal annotations. They suggest that weighted α is the most reliable metrics for such an annotation scheme.

OpenStreetMap (OSM) is currently an important source for building data, despite the existence of potential quality issues. Previous studies have assessed OSM data quality by comparing it with reference building data, which may not otherwise be readily available. This study assessed OSM building completeness using population data, and investigated the effectiveness of using population data for building reference data. We proposed various approaches, including type-based and regression-based approaches and their subtypes, and designed measures and methods to evaluate these approaches. Our evaluation examined four study areas in two countries, using global population data sets at three spatial resolutions (1-km, 100-m, and 30-m). Results showed that the type-based approach correctly classified approximately 80–99% of the assessed grid cells. The regression-based approach resulted in a high linear correlation (0.7 or greater) between the population counts and the referenced building count/building area size, with the strongest correlation present for the 1-km population dataset. We conclude that the use of population data as referenced building data is an effective method for the assessment of OSM building completeness. The paper concludes with the advantages and limitations of using both the type-based and the regression-based approaches.

Data quality drawn a major concern when dealing with data especially in the event that insightful outputs is needed. Research in data quality emerged in various topics and diversification in known knowledge and used approach is inevitable. In this paper, we apply systematic review study to explain the landscape of data quality and to identify available research gap by using categorization and mapping. Our search scope is limited to research articles from journals, conference proceedings and magazine published between 2010 until 2016. We defined three types of main categorization to map the selected research articles and to answer our research questions. These categorization focus on research topics, research type and contribution type. On average, fifty-four research articles related to data quality were published every year. This number shows the importance of data quality research in various research topics such as online users, database, web information, sensors and big data. This study also indicates that almost half of the selected articles proposed a novel solution or an essential extension of an existing data quality technique. Moreover, most of the selected research articles belongs to the model type in the contribution category. Our mapping also suggests that obvious contribution disparity happen between contribution in metric type and model type category.

In this paper we have discussed the problems of data quality which are addressed during data cleaning phase. Data cleaning is one of the important processes during ETL. Data cleaning is especially required when integrating heterogeneous data sources. This problem should be addresses together with schema related data transformation. At the end we have also discussed the Current tool which supports data cleaning.

Laser scanning is a promising geometric data collection tool for construction, facility, and infrastructure management due to its fast sampling rate (tens of thousands of measurements per second) and millimeter-level accuracy. However, laser scanned data contains inaccurate data points at spatial discontinuities (object edges). These inaccurate points, known as mixed-pixels, are commonly removed from the data prior to geometric modeling or other downstream processes. The removal of points at the edges of objects introduces error in the geometry of the objects, and object dimensions extracted from the data, such as widths and heights, are usually smaller than the actual values. In many cases, these losses due to removal of points at edges can exceed measurement accuracy tolerances specified in inspection manuals. This paper proposes a model for estimating edge loss in laser scanned data by considering the impacts of various factors, such as scanning distance, density of data and incidence angle on the edge loss. Results from a series of controlled experiments showed that the developed model successfully predicted edge losses in most test cases. Evaluation results using data collected from job sites showed that this model reduced the measurement error due to edge loss by an average of 80% for dense point clouds collected by an amplitude modulated continuous wave (AMCW) scanner, and 38% for relatively sparse point clouds collected by a pulsed time of flight (PTOF) scanner. By adding the estimated edge losses back into the raw dimensional measurements using the developed model, it is possible to significantly improve the accuracy of related measurements and hence improve the accuracy of the geometric information extracted from laser scanned data.

Ontologies are important knowledge representation and sharing tools in the workflow of biology research. The high cost of creating and maintaining ontologies encourages their sharing and reuse, and an increasing number of ontologies have been made available from different sources, with different models of curation. To enable effective selection, reuse, integration, and maintenance of these ontologies, however, one needs to have a systematic method of evaluating and connecting their quality to the context of an intended use. Based on an analysis of the activity system and Web server logs of the Morphbank biodiversity research data repository (http://www.morphbank.net/), a model was developed to evaluate ontology quality. The model connects the types of quality problems with the types of research activities and suggests relevant metrics. The paper also describes the structure of some of the research activities and the types and patterns of end-user searches in Morphbank. Contents Introduction Related research Research setup and method Analysis Constructing a quality evaluation model Conclusion

Erhebung der aktuellen Situation zur Stammdatenqualität in Unternehmen und daraus abgeleitete Trends Thomas Schäffer, Helmut Beckmann 2014 | Broschiert, fbg. | 82 S., dt. ISBN 978-3-95663-008-8 (Print) ISBN 978-3-95663-009-5 (E-Book) Bereits erschienen ERP-Studie Erfolgsfaktoren für die Integration von Unternehmenssoftware in die Unternehmens-IT Helmut Beckmann, Thomas Schäffer 2014 | Broschiert, fbg. | 60 S., dt. ISBN 978-3-943356-93-9 (Print) ISBN 978-3-95663-001-9 (E-Book)

This paper reflects on six years developing semantic data quality tools and curation systems for both large-scale social sciences data collection and a major web of data hub. This experience has led the author to believe in using organisational value as a mechanism for automation of data quality management to deal with Big Data volumes and variety. However there are many challenges in developing these automated systems and this discussion paper sets out a set of challenges with respect to the current state of the art and identifies a number of potential avenues for researchers to tackle these challenges.

In lots of large digital collections the only way a user could access a digital object is via its metadata. If metadata is not precise, contains inappropriate or less information users miss the object, and data creators lost the energy they put in the creation and maintenance of it. In past years there were some efforts in different digital collections to run research projects regarding the metadata quality: define what it is, how to measure it, and suggesting methods to improve it. Europeana – Europe’s digital library, museum and archives – just published a report [4]
about the current state of art of this topic within the institution. This report states: „There was not enough scope for this Task Force to investigate elements such as metrics for metadata quality or how EDM schema validation could affect metadata quality.”. This current research would like to start at exactly this point; we would like to set up a framework, which tests incoming and existing records, checks them against quality requirements, gives the Europeana community a dashboard about metrics, showing the historical changes of those metrics, and provides tips and suggestions to data creators, aggregator institutions and different Europeana teams.

Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high-quality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

In last few years there are many changes and evolution has been done on the classification of data. Application area of technology increases then the size of data also increases. Classification of data becomes difficult because of imbalance nature and unbounded size of data. Class imbalance problem becomes the greatest issue in the data mining. Imbalance problem occurs where one of the two classes having more sample than the other classes. The most of the algorithms are focusing on the classification of major sample while ignoring minority sample. The minority samples are those samples that rarely occur but are very important. There are different methods available for the classification of imbalance data set which is divided into three categories i.e. the algorithmic approach, feature selection approach and the data processing approach. These approaches have their own advantages and disadvantages. In this paper a systematic study of each process is defined which gives the right direction for research in class imbalance problem.

"With this PhD dissertation, I intend to contribute to a better understanding of the Wiki phenomenon as a knowledge management system which aggregates private knowledge. I also wish to check to what extent information generated through anonymous and freely bestowed mass collaboration is reliable as opposed to the traditional approach.

In order to achieve that goal, I develop a comparative study between Wikipedia and Encyclopaedia Britannica with regard to accuracy, depth and detail of information in both, in order to confront the quality of the knowledge repository produced by them. That will allow me to reach a conclusion about the efficacy of the business models behind them.

I will use a representative random sample which is composed by the articles that are comprised in both encyclopedias. Each pair of articles was previously reformatted and then graded by an expert in its subject area. At the same time, I collected a small convenience sample which only integrates Management articles. Each pair of articles was graded by several experts in order to determine the uncertainty associated with having diverse gradings of the same article and apply it to the evaluations carried out by just one expert. The conclusion was that the average quality of the Wikipedia articles which were analysed was superior to its peers’ and that this difference was statistically significant.

An inquiry was conducted within the academia which certified that traditional information sources were used by a minority as the first approach to seeking information. This inquiry also made clear that reliance on these sources was considerably larger than reliance on information obtained through Wikipedia. This quality perception, as well as the diametrically opposed results of its evaluation through a blind test, reinforces the evaluating panel’s exemption.

However much the chosen sample is representative of the universe to be studied, results have depended on the evaluators’ personal opinion and chosen criteria. This means that the reproducibility of this study’s conclusions using a different grading panel cannot be guaranteed. Nevertheless, this is not enough of a reason to reject the study results obtained through more than five hundred evaluations.

This thesis is thus an attempt to help clarifying this topic and contributing to a better perception of the quality of a tool which is daily used by millions of people, of the mass collaboration which feeds it and of the collaborative software that supports it."

Victims of road traffic accidents face severe health problems on-site or after the event when they arrive at hospital lately in their emergency cycle. Road traffic accident has negative effect on the physical, social and emotional security of human lives which often lead to mortality, illness, pain, grief and even disability. This paper proposes a scheme that reduces the severity of road traffic accidents given its inevitable occurrence. The rational is to search for nearest hospitals to the accident location using Dijkstra algorithm and Fuzzy logic to recommend suitable hospitals out of list of nearest hospitals to timely attend to the emergency situation considering factors such as distance, severity of the accident, available facilities in the hospitals and other factors. The obtained results showed the practicability of the system to recommendation of quick solution to accident emergencies.

Without robust quality control, insights gained by exploiting linked data will be incomplete, unreliable and misleading. Much of what passes for quality control, however, is not designed to verify complete end-to-end real world processes but only certain isolated steps, leaving gaps for errors which too often are not only tolerated but considered "normal". This paper presents the alarming results of a series of entity reconciliation tests in the domain of Nazi looted art. Only 6% of plundered Jewish collectors were correctly matched to LOD entities. The causes were multiple: missing or confusing Wikimedia content, LOD or authority files, dysfunctions across languages, redirects, problematic labelling, and a bias towards fictional characters over real people. It is not clear who, if anyone, is responsible for verifying and correcting these complex errors stemming from the intersection of multiple jurisdictions, raising the question of governance, in particular where marginalized communities are concerned. Conceived as a first step in an ongoing, iterative process, the paper concludes with some suggestions on how to monitor and improve the usefulness and reliability of linked data, as well as the urgency of doing so.

Background: The ubiquity of health wearables and the consequent production of patient-generated health data (PGHD) are rapidly escalating. However, the utilization of PGHD in routine clinical practices is still low because of data quality issues. There is no agreed approach to PGHD quality assurance; therefore, realizing the promise of PGHD requires in-depth discussion among diverse stakeholders to identify the data quality assurance challenges they face and understand their needs for PGHD quality assurance.

Open data are a basic infrastructure for the creation of business, products, and services. To analyze their usefulness, the dif-
ferences between data access, and dissemination, and reuse must be taken into account. The objective of this research is to
analyze how Berners-Lee’s 5-star model and other factors help evaluate the quality of data and its use in the Barcelona open
data portal, including its relationship to downloads and themes. Results show that it may be of interest to include aspects
such as data updating frequency and geolocation in the models that measure the quality of data for reuse.

Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream, validation or quality assessment of the CS data and their appropriate usage for evidence-based policy making, needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or ‘pillars’. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However,...

Executive Summary/Abstract: In an effort to improve the data quality levels of applications developed by McKesson Specialty Patient Services, the Quality Assurance team has implemented a data quality approach to system testing. The QA team uses system testing activities to identify and resolve data quality defects. Data quality findings reports are developed to form the basis for long term solutions to data quality issues. The details of the current process as well as plans for process improvement are provided in this presentation.

Within the context of the smart city, data are an integral part of the digital economy and are used as input for decision making, policy formation, and to inform citizens, city managers and commercial organisations. Reflecting on our experience of developing real-world software applications which rely heavily on urban data, this article critically examines the veracity of such data (their authenticity and the extent to which they accurately (precision) and faithfully (fidelity, reliability) represent what they are meant to) and how they can be assessed in the absence of quality reports from data providers. While data quality needs to be considered at all aspects of the data lifecycle and in the development and use of applications, open data are often provided 'as-is' with no guarantees about their veracity, continuity or lineage (documentation that establishes provenance and fit for use). This allows data providers to share data with undocumented errors, absences, and biases. If left unchecked these data quality issues can propagate through multiple systems and lead to poor smart city applications and unreliable 'evidence-based' decisions. This leads to a danger that open government data portals will come to be seen as untrusted, unverified and uncurated data-dumps by users and critics. Drawing on our own experiences we highlight the process we used to detect and handle errors. This work highlights the necessary janitorial role carried out by data scientists and developers to ensure that data are cleaned, parsed, validated and 2 transformed for use. This important process requires effort, knowledge, skill and time and is often hidden in the resulting application and is not shared with other data users. In this paper, we propose that rather than lose this knowledge, in the absence of data providers documenting them in metadata and user guides, data portals should provide a crowdsourcing mechanism to generate and record user observations and fixes for improving the quality of urban data and open government portals.

Dieses Kapitel gibt einen Überblick über das Datenqualitätsmanagement. Es listet einige Ansätze zum Thema und seine grundlegenden Definitionen auf. Die Datenqualität hängt immer vom Kontext und Zweck der Daten ab, daher haben ver-schiedene Bereiche unterschiedliche Metriken zur Messung der Daten geschaffen. Es werden einige relevante Bereiche, wie Forschungsdaten, verknüpfte Daten, Da-tenjournalismus, untersucht, um ihre Messprinzipien und besten Praktiken hervor-zuheben. Schließlich werden einige praktische Beispiele, darunter Europeana (die europäische Kulturerbe-Plattform) und Forschungsdaten-Repositorien gezeigt.

Without robust quality control, insights gained by exploiting linked data will be incomplete, unreliable and misleading. Much of what passes for quality control, however, is not designed to verify complete end-to-end real world processes but only certain isolated steps, leaving gaps for errors which too often are not only tolerated but considered “normal”. This paper presents the alarming results of a series of entity reconciliation tests in the domain of  Nazi looted art. Only 6% of plundered Jewish collectors were correctly matched to LOD entities. The causes were multiple: missing or confusing Wikimedia content, LOD or authority files, dysfunctions across languages, redirects, prob-lematic labelling, and a bias towards fictional characters over real people. It is not clear who, if anyone, is responsible for verifying and correcting these com- plex errors stemming from the intersection of multiple jurisdictions, raising the question of governance, in particular where marginalized communities are con-cerned. Conceived as a first step in an ongoing, iterative process, the paper con-cludes with some suggestions on how to monitor and improve the usefulness and reliability of linked data, as well as the urgency of doing so.

Victims of road traffic accidents face severe health problems on-site or after the event when they arrive at hospital lately in their emergency cycle. Road traffic accident has negative effect on the physical, social and emotional security of human lives which often lead to mortality, illness, pain, grief and even disability. This paper proposes a scheme that reduces the severity of road traffic accidents given its inevitable occurrence. The rational is to search for nearest hospitals to the accident location using Dijkstra algorithm and Fuzzy logic to recommend suitable hospitals out of list of nearest hospitals to timely attend to the emergency situation considering factors such as distance, severity of the accident, available facilities in the hospitals and other factors. The obtained results showed the practicability of the system to recommendation of quick solution to accident emergencies.

The buildup of huge data within business intelligence is essential because such data includes complete conceptual and technological stack in addition to raw and processed data, data management, and analytics. Evaluating Data Quality Model Based-In-Use has gained more ground since business value could be only estimated in its used context. Despite the numerous data quality models used for regular data quality assessment, none of them have been amended to big data. For this reason, we propose four efficiencies and four metabolism processes as data quality indicators usable in big data researches. This model appropriately obtained the quality in use levels of the entry data for big data analytics, and those adequacies of Data Quality Model Based-In-Use levels could be comprehended as dependability indicators and adequacy of big data investigation. Besides, we have demonstrated the practical examples along with a proposed method, the stacked recurrent neural network for data quality assessment. Therefore, this model being independent of any pre-conditions or technologies could be integrated into various big data research.

Incomplete data is a crucial challenge to data exploration, analytics, and visualization recommendation. Incomplete data would distort the analysis and reduce the benefits of any data-driven approach leading to poor and misleading recommendations. Several data imputation methods have been introduced to handle the incomplete data challenge. However, it is well-known that those methods cannot fully solve the incomplete data problem, but they are rather a mitigating solution that allows for improving the quality of the results provided by the different analytics operating on incomplete data.

Open access (OA) has mostly been studied by relying on publication data from selective international databases, notably Web of Science (WoS) and Scopus. The aim of our study is to show that it is possible to achieve a national estimate of the number and share of OA based on institutional publication data providing a comprehensive coverage of the peer-reviewed outputs across fields, publication types and languages. Our data consists of 48,177 journal, conference and book publications from 14 Finnish universities in 2016-2017, including information about OA status, as self-reported by researchers and validated by data-collection personnel through their Current Research Information System (CRIS). We investigate the WoS, Scopus and DOI coverage, as well as the share of OA outputs between different fields, publication types, languages, OA mechanisms (gold, hybrid and green), and OA information sources (DOAJ, Bielefeld list and Sherpa/Romeo). We also estimate the role of the largest international commercial publishers compared to the not-for-profit Finnish national publishers of journals and books. We conclude that institutional data, integrated at national and international level, provides one of the building-blocks of a large-scale data infrastructure needed for comprehensive assessment and monitoring of OA across countries, for example at the European level. 2