Recently, due to the advancements in the ICT (Information and Communication Technology), there has been lot of emphasis on digitization of the existing and newly developed infrastructure. In transportation infrastructure, in general, 80% of the assets are already in place and there has been tremendous push to move to the digital era. For efficient and effective design, construction, operation and maintenance of the infrastructure, due to this digitization, there is increasing research trend in data-driven decision-making algorithms that are proved to be effective because of several advantages. Since railway is the backbone of the society, the data-driven approaches will ensure the continuous operation, efficient maintenance, planning and potential future investments. The breach and leak of this potential data to the wrong hands might result in havoc, risk, trust, hazards and serious consequences. Hence, the main purpose of this paper is to stress the potential challenges, consequences, threats, vulnerabilities and risk management of data security in the railway infrastructure in context of eMaintenance. In addition, this paper also identifies the research methods to obtain and secure this data for potential possible research.

The convergence of information technology and operation technology and the associated paradigm shift toward Industry 4.0 in complex systems, such as railways has brought significant benefits in reliability, maintainability, operational efficiency, capacity, as well as improvements in passenger experience. However, with the adoption of information and communications technologies in railway maintenance, vulnerability to cyber threats has increased. It is essential that organizations move toward security analytics and automation to improve and prevent security breaches and to quickly identify and respond to security events. This paper provides a statistical review of cybersecurity incidents in the transportation sector with a focus on railways. It uses a web-based search for data collection in popular databases. The overall objective is to identify cybersecurity challenges in the railway sector.

The paper proposes a model for data quality (DQ) assessment in maintenance. Data has become an increasingly important since most of the maintenance planning and implementations are based on data analysis. Poor DQ reduces customer satisfaction, leading to poor decision making, and has negative impacts on strategy execution. To improve DQ as well as to evaluate the current status, DQ needs to be measured. A measure for DQ could be an important support for decision makers. Multi-criteria decision-making (MCDM) methods can provide a framework for DQ assessment, however, they are not used in literature for DQ assessment. In order to assess DQ, the attributes or KPIs need to be defined, their hierarchy should be designed and the assessment model is proposed to evaluate these attributes. A case study is also presented in this paper. The study shows that MCDM methods could provide qualitative estimation for the quality of DQ attributes.

Computerised maintenance management systems (CMMS) are software packages; their data include information on an organisation’s maintenance, operations and costs. MAXIMO is recognised as a leading CMMS for asset management. It helps to manage maintenance data, improving data quality, making maintenance more efficient, and supporting decision making. However, MAXIMO systems have problems of data quality, with a resulting impact on efficiency and the validity of decisions based on those data. This paper investigates the quality of maintenance data in MAXIMO using the Swedish Transport Agency (Trafikverket) as a case study. It discusses the results before and after data cleaning to show the impact of data quality problems on data analysis.

High quality data and data quality assessment can support the decision-makingprocess. In the literature, discussions of the assessment process are mainly focused on theoretical approaches to content analysis or on user evaluations. Metadata is important source for quality information in any database system, however, it is not considered for data quality assessment. Metadata contains information that describes the data in a database, including the constraints and the database schema. High quality data can be produced by designing a database system with accurate metadata descriptions. Having accurate and detailed metadata will reduce the errors in data values which can lead to data quality issues. In this study, data quality assessment model is proposed based on both content and metadata analysis. The model is validated by developing an application tool to assess the quality of the data in a database based on the proposed model. The results show that metadata can provide important information about the quality of the database and its adoption can help togive faster, more accurate and user independent assessment of data quality.

The purpose of this paper is to propose a Data Quality Measurement Model based on ISO 8000 standard. This paper deals about the concepts implied in the measurement process, not about the measures themselves. Poor quality information causes customer dissatisfaction, lost revenue and higher costs associated with additional time to reconcile information. An understanding of the characteristics of the data that determine its quality, and an ability to measure, manage and report on data quality is required. Measurement is a major activity in data quality management. In literature, there are many proposals contributing somehow to the measurement of data quality. However, these measurement methods lack the unification. ISO 8000 provides a framework for improving data quality that can be used independently or in conjunction with quality management systems. ISO 8000 defines characteristics that can be tested by any organization in the data supply chain to objectively determine conformance of the data to ISO 8000.

Purpose – The purpose of this paper is to explore the main ontologies related to eMaintenance solutions and to study their application area. The advantages of using these ontologies to improve and control data quality will be investigated.

Design/methodology/approach – A literature study has been done to explore the eMaintenance ontologies in the different areas. These ontologies are mainly related to content structure and communication interface. Then, ontologies will be linked to each step of the data production process in maintenance.

Findings – The findings suggest that eMaintenance ontologies can help to produce a high quality data in maintenance. The suggested maintenance data production process may help to control data quality. Using these ontologies in every step of the process may help to provide management tools to provide high quality data.

Research limitations/implications – Based on this study, it can be concluded that further research could broaden the investigation to identify more eMaintenance ontologies. Moreover, studying these ontologies in more technical details may help to increase the understandability and the use of these standards.

Practical implications – It has been concluded in this study that applying eMaintenance ontologies by companies needs additional cost and time. Also the lack or the ineffective use of eMaintenance tools in many enterprises is one of the limitations for using these ontologies.

Originality/value – Investigating eMaintenance ontologies and connecting them to maintenance data production is important to control and manage the data quality in maintenance.

Enterprise resource planning systems and maintenance management systems are commonly used by organisations for handling of maintenance records, through a graphical user interface. A maintenance record consists of a number of data fields, such as drop-down lists, list boxes, check boxes and text entry fields. In contrast to the other data fields, the operator has the freedom to type in any text in the text entry fields, to complement and make the maintenance record as complete as possible. Accordingly, the text entry fields of maintenance records can contain any words, in any number.Data quality is crucial in statistical analysis of maintenance records, and therefore manual analysis of maintenance records’ text entry fields is often necessary before any decision making. However, this may be a very tedious and resource consuming process.In this article, natural language processing is applied to text entry fields of maintenance records in a case study, to show how it can bring further value in the assessment of technical assets’ performance.

Increased environmental awareness in the industry combined with the globalized market economy makes increasing demands for sustainable and efficient resource utilization. In this context, maintenance plays a critical role by linking business objectives to the strategic and operational activities aimed at retaining the system’s availability performance, cost-efficiency and sustainability. Performing maintenance effectively and efficiently requires corresponding infrastructure for decision-support provided through eMaintenance solutions. A proper eMaintenance solution needs to provide services for data acquisition, data processing, data aggregation, data analysis, data visualization, context-sensing etc. To en Quality of Service (QoS) in eMaintenance solutions, the performance of both system-of-interest, enabling systems and related processes have to be measured and managed. However, the QoS has to be considered on all aggregation levels and encompass the aspects of Content Quality (CQ), Data Quality (DQ) and Information Quality (IQ). Hence, the purpose of this paper is to study and describe some aspects of DQ in eMaintenance related to process industry in northern Europe.