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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.

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.

The various problems of medical data governance and data availability in the context of real-world research on a specific disease type were analyzed. The definitions and concepts of the data governance were provided, such as hospital data governance, regional data governance, data governance of disease-specific alliances, governance of medical annotation data and knowledge-based data. Here, the common methodologies and personality methodologies of data governance were summarized. Furthermore, the technologies of master data management, metadata management and data quality control in data governance were further discussed. Finally, the basic framework of medical big data standards was given, and the medical data quality was simply evaluated which based on the existing data governance evaluation standards.

Integrated network management system is becoming more and more necessary for telecom carriers. Base on the features of network management systems, and according to the practice in area of integrated resource management, a way to build fusion model in integrated network management system for intensive network management was explained, which would solve the problem of stability and extensibility of the core data model.

The progress of research and practice in data quality standardization was reviewed,and the framework of data quality was introduced,which was put forward by the international standard of ISO 8000 data quality.The master data quality,transaction data quality and product data quality were discussed.The method and measurement theory of the large data quality standardization for sharing were discussed.At last,suggestions for China’s government in the big data quality control and standardization were put forward.