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Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Organizations rely on high quality master data as a critical component in achieving their operational and strategic performance. To accomplish high quality master data, they need to be managed properly through a systematic and holistic framework. However, prevalent master data quality management frameworks lack in providing comprehensive management practice in assuring the quality of master data. Hence, stimulates the need to develop an improved master data quality framework. Prior to the development of the framework, the identification and validation of factors that contribute to the management of master data quality must be performed. Thus, this paper underlined four elements and seven factors affecting master data quality management. Further, the identified factors were validated using a questionnaire as the validation instrument. The questionnaire consists of 95 items representing the identified seven factors that were derived from previous studies in the domain of total quality management, data quality management, and master data. Since the items are derived from the different contexts of study, content validation is a need. Previous research has suggested several techniques for performing content validation, covering both quantitative and qualitative approaches. The quantitative approach employed objective assessment and the result was statistically analysed. While the qualitative approach adopted subjective assessment such as comments, ideas, or respond. In this paper, the quantitative approach is selected over the qualitative approach, considering the effort in analyzing several items (95 items) is less complex compared to the qualitative approach which is more difficult to interpret and account for biased results. The selected panel of experts validate the instrument using a three-point scale namely "1 = not relevant", "2 = important (but not essential)", and "3 = essential". Later, using the technique proposed by Lawshe, the value of the content validity ratio (CVR) is calculated. As a result, 92 items are accepted, and 3 items are rejected. The elimination of the 3 items is due to the unsuitableness to be used in the context of the public sector. The validated items can be used as an instrument to validate the factors affecting master data quality management. The proposed factor would support the organization in managing master data quality more effectively. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Derecho de la Seguridad Social, Laborum is the property of Ediciones Laborum S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing adoption of smart factories in manufacturing sites, a large amount of raw data is being generated from manufacturers' sensors and Internet of Things devices. In the manufacturing environment, the collection of reliable data has become an important issue. When utilizing the collected data or establishing production plans based on user-defined data, the actual performance may differ from the established plan. This is particularly so when there are modifications in the physical production line, such as manual processes, newly developed processes, or the addition of new equipment. Hence, the reliability of the current data cannot be ensured. The complex characteristics of manufacturers hinder the prediction of future data based on existing data. To minimize this reliability problem, the M5P algorithm, is used to predict dynamic data using baseline information that can be predicted. It combines linear regression and decision-tree-supervised machine learning algorithms. The algorithm recommends the means to reflect the predicted data in the production plan and provides results that can be compared with the existing baseline information. By comparing the existing production plan with the planning results based on the changed master data, it provides data results that help production management determine the impact of work time and quantity and confirm production plans. This means that forecasting data directly affects production capacity and resources, as well as production times and schedules, to help ensure efficient production planning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent coal mine data classification and coding is a key part of intelligent coal mine data governance and fusion sharing standard system. However, there is no perfect methodology system. It is urgent to deeply study the technical objectives, technical principles and key technical processes of intelligent coal mine data classification and coding. Based on the analysis of the core problems to be solved in the process of data classification and coding in the advanced stage goal realization of the intelligent coal mine, the essence of intelligent coal mine data classification and coding is expounded. Intelligent coal mine data classification and coding is the recognition and construction of the intelligent coal mine information world, the optimization and mapping of the intelligent coal mine consciousness world, and the transformation and description of the intelligent coal mine physical world. This paper analyzes the goal requirements of intelligent coal mine data classification and coding from three aspects of overall strategic objectives, business strategic objectives and functional strategic objectives. Intelligent coal mine data classification and coding should promote the reshaping of the business mode of coal enterprises, change the value creation driving mode, focus on key value activities, and realize the scientific organization of intelligent coal mine data. The paper proposes that the intelligent coal mine data classification and coding should follow the basic principles of scientificity, standardization, integrity, uniqueness and effectiveness. The paper also proposes the basic idea of intelligent coal mine data classification and coding. Intelligent coal mine should adopt the data classification idea of 'benchmark- expansion' two stages. In the first stage, the logical sequence of 'business domain - data domain - object - attribute - data element' is adopted to determine the classification benchmark of intelligent coal mine data. In the second stage, the logical sequence of 'intelligent application system - intelligent business system - system functions - data resources' is adopted for data classification, verification and supplement. The hierarchical coding method of line classification should be adopted for intelligent coal mine data coding. Based on the above basic ideas, it is pointed out that in general, intelligent coal mine data classification and coding needs to focus on five key steps: determining business domain, determining data domain, identifying object classes, extracting object class attributes and defining data elements.

With the increasing adoption of smart factories in manufacturing sites, a large amount of raw data is being generated from manufacturers’ sensors and Internet of Things devices. In the manufacturing environment, the collection of reliable data has become an important issue. When utilizing the collected data or establishing production plans based on user-defined data, the actual performance may differ from the established plan. This is particularly so when there are modifications in the physical production line, such as manual processes, newly developed processes, or the addition of new equipment. Hence, the reliability of the current data cannot be ensured. The complex characteristics of manufacturers hinder the prediction of future data based on existing data. To minimize this reliability problem, the M5P algorithm, is used to predict dynamic data using baseline information that can be predicted. It combines linear regression and decision-tree-supervised machine learning algorithms. The algorithm recommends the means to reflect the predicted data in the production plan and provides results that can be compared with the existing baseline information. By comparing the existing production plan with the planning results based on the changed master data, it provides data results that help production management determine the impact of work time and quantity and confirm production plans. This means that forecasting data directly affects production capacity and resources, as well as production times and schedules, to help ensure efficient production planning.

Background: Global product identification standards and methods of product data exchange are known and widespread in the traditional market. However, it turns out that the e-commerce market needs data that have not already received much attention, for which no standards have been established in relation to their content. Furthermore, their current quality is often perceived below expectations. This paper discusses the issues of product name and highlights its problems in the context of e-commerce. Attention is also drawn to the source of liability for erroneous data. Methods: The research methodology is based on the analysis of data of products available on the Internet through product catalog services, online stores, and e-marketplaces, mainly in Poland, but addresses a global problem. Three research scenarios were chosen, comparing product names aggregated by GTIN, starting with e-commerce sites and ending with product catalogs working with manufacturers. In addition, a scenario of name-photo compatibility was included. Results: The results show that the product name, which in the real world is an integral part of the product as it appears on the label provided by the manufacturer, in the virtual world is an attribute consciously or not modified by the service provider. It turns out that discrepancies appear already at the source - at the manufacturer's level - publishing different names for the same product when working with data catalogs or publishing on product pages contributing to the so-called snowball effect. Conclusions: On the Internet, products do not have a fixed name that fully describes the product, which causes problems in uniquely identifying the same products in different data sources. This in turn reduces the quality of data aggregation, search, and reliability. This state of affairs is not solely the responsibility of e-commerce marketplace vendors, but of the manufacturers themselves, who do not take care to publicize the unambiguous and permanent name of their products in digital form. Moreover, there are no unambiguous global guidelines for the construction of a full product name. The lack of such a template encourages individual interpretations of how to describe a product. [ABSTRACT FROM AUTHOR]

Master data preparation for the introduction of business systems is often a burden. Nevertheless, a huge amount of various data is released as big data and used in several fields. However, these data exist in various formats at various websites on big data, making it difficult to utilize them directly for preparing master data. In this study, we propose a three-step master creation method: necessary data are acquired by scraping, a semi-structured common database is created by integrating these data, and individual master data is extracted from this database. Furthermore, we evaluate this proposed method by creating an English question master for e-learning and show that individual master data can be efficiently extracted from the integrated common database. Additionally, we conducted a comparative evaluation of the efficiency of the proposed method using MongoDB and MySQL, namely a semi-structured and structured database. The experimental results show that the semi-structured database is more efficient for manipulating data of various websites. [ABSTRACT FROM AUTHOR]

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

The productivity improvement of the construction sector has fallen behind other industries. One potential factor is the on-site nature of the work that may not allow for as industrial way of operations as off-site construction. Also, the division into non-optimised chain of domains of architecture, engineering, construction and facilities management may play a role. The construction objects may not be considered as products that would necessitate careful consideration of the product structure, both commercially and technically to optimise the use of platforms, assemblies, components, and materials while offering what customers desire. Furthermore, the life-cycle of building objects and the varying needs during design, construction and the use-phase are not acknowledged effectively. Addressing the life-cycle of building objects necessitates taking command of the relevant data and understanding the bigger picture. Currently the data are managed in an un-organised manner. This study aims to form the pre-requisites for managing construction object-related data. The study is realised as a combination of a literature review and analysing constructors’ offerings, requirements plans, Ministry reports, and building control documentations. A rather detailed example of construction object related data management is presented to discuss master data, and business process related data in the context of business processes and enterprise applications, and different Billsof-Materials (BOM) configurations to demonstrate the necessary considerations. The example also acknowledges the product structure, necessary parameters, and stakeholders. New contribution is provided by presenting valuable insights with a broader scope for setting up effective data management in the construction sector.

Background: In developing countries, despite large public companies’ reliance on master data for decision-making, there is scant evidence to demonstrate their effective use of transactional data in decision-making because of its volatility and complexity. For the state-owned enterprise (SOE) studied, the complexity of generating high-quality transactional data manifests in relationships between customer call transactional data related to an electricity supply problem (captured by call centre agents, i.e. data creators) and technician-generated feedback (i.e. data consumers). Objectives: To establish the quality of customer calls transactional data captured using source system measurements. To compare this data set with field technicians’ downstream system transactions that indicated incorrect transactional data. Method: The study compared historical customer calls transactional data (i.e. source system data) with field technician-generated feedback captured on work orders (i.e. receiving system) in a power generation SOE, to ascertain transactional data quality generated and whether field technicians responded to authentic customer calls exclusively to mitigate operational expenses. Results: Mean values of customer call transactional data quality from the source system and technician-generated feedback on work orders varied by 1.26%, indicating that data quality measurements at the source system closely resembled data quality experiences of data consumers. The SOE’s transactional data quality from the source system was 80.05% and that of historical data set from evaluating feedback was 81.31% – percentages that exceeded average data quality measurements in literature. Conclusion: Using a feedback control system (FCS) to integrate feedback generated by data consumers to data creators presents an opportunity to increase data quality to higher levels than its current norm.

In the process of manufacturing, a large amount of manufacturing data is produced by different departments and different domain. In order to realise data sharing and linkage among supply chains, master data management method has been used. Through master data management, the key data can be shared and distributed uniformly. However, since these cross-domain data form a data network through the association of master data, how to evaluate the effectiveness and rationality of this network becomes the major issue in the proposed method. In this paper, a model of the master data network is built based on the theory of set pair analysis. In order to verify the master data, an evaluation method for the network is proposed. Finally, a case was presented to validate this network model and evaluation method. [ABSTRACT FROM AUTHOR]

Background: In developing countries, despite large public companies' reliance on master data for decision-making, there is scant evidence to demonstrate their effective use of transactional data in decision-making because of its volatility and complexity. For the state-owned enterprise (SOE) studied, the complexity of generating high-quality transactional data manifests in relationships between customer call transactional data related to an electricity supply problem (captured by call centre agents, i.e. data creators) and technician-generated feedback (i.e. data consumers). Objectives: To establish the quality of customer calls transactional data captured using source system measurements. To compare this data set with field technicians' downstream system transactions that indicated incorrect transactional data. Method: The study compared historical customer calls transactional data (i.e. source system data) with field technician-generated feedback captured on work orders (i.e. receiving system) in a power generation SOE, to ascertain transactional data quality generated and whether field technicians responded to authentic customer calls exclusively to mitigate operational expenses. Results: Mean values of customer call transactional data quality from the source system and technician-generated feedback on work orders varied by 1.26%, indicating that data quality measurements at the source system closely resembled data quality experiences of data consumers. The SOE's transactional data quality from the source system was 80.05% and that of historical data set from evaluating feedback was 81.31% – percentages that exceeded average data quality measurements in literature. Conclusion: Using a feedback control system (FCS) to integrate feedback generated by data consumers to data creators presents an opportunity to increase data quality to higher levels than its current norm. [ABSTRACT FROM AUTHOR]

Productivity in the construction industry (both houses and infrastructure) has not been improving as expected, while other industries have been able to improve their productivity significantly. The appropriate use of building information modelling (BIM) technologies brings several benefits and advantages to construction projects. The main challenges of project efficiency emerge in the form of numerous requests for information during the construction project, which are considered to be waste in the processes. This highlights the need for a practical process model to plan the information flow for BIM-based projects. The main aim of this study is to propose a model to plan the flow of project information among primary stakeholders especially in infrastructure projects. Our main findings are firstly, the foundation for data management starts from defining unified one data for the product and the for the process. Unified data means one single repository of data – all stakeholders use the same unified data. It is also essential that data responsibilities and ownership are defined. Secondly, we found that the biggest challenges are that the data needs are not planned beforehand, resistance to change, difficulty receiving existing data and data must be modified before use. As a whole, it seems sometimes that the technology on data transfer is more important that what has been transferred and why. Finally our construction, the life cycle model for data flow originates from one data to all stakeholders, single data repository must be updated along the life-cycle of the object covering also the operations and maintenance, where the data has to be updated through the whole life-cycle. This new approach is intended to enable the early involvement of maintenance stakeholders in designing product data for a project life-cycle perspective. The model helps to change the current information flow and gain the benefits that a BIM-based process can offer. This study is based on case studies and is qualitative in nature and naturally needs more validation. [ABSTRACT FROM AUTHOR]