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Effective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM).

A literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle.

It is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure.

The system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry.

This paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.

Although various concepts and techniques are introduced to the built environment to achieve a substantially efficient building production, the effective application of these methods in projects is of immense significance to the field of building construction. Among these initiatives, lean construction and building information modelling (BIM) are mainstream endeavours that share many common principles to improve the productivity of the built environment. This study aims to explore and explain how BIM-based integrated data management (IDM) facilitates the achievement of leanness in a built environment project.

This research is conducted through an ethnographic-action research that relies on the design-science approach and case study through a collaborative research project. As participants of the project, the researchers of this study cooperate with the practitioners to design the project approach and production workflows. Research data and evidence are obtained via participative observation, including direct observation, results of activities, unstructured meetings and self-analysis.

In this study, the project and production perspectives clarify the building design and production process, as well as analyse how BIM facilitates the achievement of leanness in building design and construction. BIM-based frameworks for IDM have been developed to handle miscellaneous information and data, as well as enhance multidisciplinary collaboration throughout the project life cycle. The role of the integrated BIM model as an information hub between the building design and building construction has been identified.

The project and production views of building and construction are used in this study because the research purpose is to link the BIM-based IDM to lean construction. Although this mixed approach can slightly undermine the theoretical foundation of this study, a substantially comprehensive understanding can be gained as well.

This study provides a new perspective to understand how BIM-based IDM contributes to lean construction.

This study provides new insights into IDM in a built environment project with project and production views and presents BIM-based frameworks for IDM to achieve lean construction through the BIM process.

Changes in market and production profiles require a more flexible concept in manufacturing. Computer integrated manufacturing (CIM) describes an integrative concept for joining business and manufacturing islands. In this context, database technology is the key technology for implementing the CIM philosophy. However, CIM applications are more complex and thus more demanding than traditional database applications such as business and administrative applications. Systematically analyses the database requirements for CIM applications including business and manufacturing tasks. Special emphasis is given on integration requirements due to the distributed, partly isolated nature of CIM applications developed over the years. An illustrative sampling of current efforts in the database community to meet the challenge of non‐standard applications such as CIM is presented.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

This paper aims to analyze the impact of information technology (IT) system implementation on the integration of data and information between sales and accounting departments, and how data integration affects relations with supplier and customers.

The change between three different reservation and distribution systems in an airline company was assessed over 20 years based on qualitative data collected while events unfolded and interviews that traced events retrospectively.

This study finds that data integration challenges affect the capacity to use revenue and sales data for control purposes and integrating with suppliers and customers. The systems either facilitated the ability to integrate sales and accounting data or enabled integration in wider supplier and customer networks. The implementation of different reservation and distribution systems resulted in a trade-off between integration within the firm and into wider customer and supplier networks.

Data were mainly obtained from the focal firm, Air Greenland. The protracted study period meant that the data were not as concentrated as they would have been had the analysis been performed over a shorter duration or had the focus been on one implementation process.

Extant research suggests that integration challenges when implementing IT systems are caused by differences in information needs between groups with different logics. The authors illustrate how data integration is also a crucial challenge when implementing IT systems.

This paper aims to capture and manage the product lifecycle data for consumer products, especially data that occur in distribution, usage, maintenance and end‐of‐life stages, and to use them to provide information and knowledge.

A lifecycle information acquisition and management model is proposed, and an information management system framework is formulated. The information management system developed is then used in actual field trials to manage lifecycle data for refrigeration products and game consoles.

It has been demonstrated that valuable services can be delivered through a lifecycle information management system.

Lifecycle information management systems can open new horizons for product design which are sustainable and environmentally sensitive. They also contribute to the wider exploration of eco‐design and development of next generation consumer products (e.g. smart home appliances).

Existing lifecycle information systems cannot support all phases of the product lifecycle. They mainly manage the lifecycle data only during the design and manufacture stages. Lifecycle data during distribution, usage, maintenance and end‐of‐life stages are usually hard to acquire and in most cases lost. The lifecycle information management system developed can capture them, and manage them in an integrated and systematic manner to provide information and knowledge.

An adaptable, integrated full glass cockpit and flight management system has been developed and is in production for application in multiple Sikorsky rotorcraft. The entire system was conceived, designed, tested and delivered in an unusually short time period. A systematic process was used to define the avionics system attributes, major capabilities, and cost targets up‐front and track them during the development program. First flight was achieved 12 months after contract start, and production deliveries commenced 5 months after first flight. The integrated glass cockpit has accumulated more than 9,000 flight hours in customer operations to date. This flexible system architecture allowed the team of Sikorsky and Rockwell Collins to reuse several blocks of existing military and civil application software, and to interface the various Avionics subsystems using industry standards. This proved to be a critical factor in allowing us to meet the compressed design and development schedule.

The paper seeks to describe the evolution of an integrated approach to health and productivity management that combines the disciplines of worksite health promotion and occupational safety and health, and to offer advice on how to implement such an integrated approach.

The paper takes the form of a review of the literature, focusing on the psychological, organizational, and human capital models that must be integrated for successful health and productivity management.

The first integrated health, safety, and productivity model was presented by DeJoy and Southern in 1993. However, occupational safety and health and worksite health promotion professionals view the workplace in different ways (from psychological and public health orientations, respectively) that may result in siloed work environments. Better communication and collaboration across these disciplines is essential for success. That can be fostered by adopting a human capital framework that views the health and safety of employees as essential ingredients for a healthy and productive work force. A practical approach for successful health and productivity management uses integrated data to investigate where challenges to worker health and safety can be found. This is followed by strategic and tactical planning to address these challenges. Programs that address problems at all levels (individual, organizational, environmental) are then adopted, followed by formal, rigorous, and continuous monitoring and evaluation.

The concept of integrated health and productivity management is new but is now being adopted by many organizations. Worksite health promotion and occupational safety and health professionals can work together to make substantial improvements to the quality of employees' lives and the economic and social health of the organizations where they practice.

Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including challenges involving variety, uncertainty, and complexity. These characteristics in big data systems demand flexible and agile integration architectures. Furthermore, a formal model is needed to support design and verification. The purpose of this paper is to resolve the two problems with a collective intelligence (CI) model.

In the conceptual CI framework as proposed by Schut (2010), a CI design should be comprised of a general model, which has formal form for verification and validation, and also a specific model, which is an implementable system architecture. After analyzing the requirements of system integration in big data environments, the authors apply the CI framework to resolve the integration problem. In the model instantiation, the authors use multi-agent paradigm as the specific model, and the hierarchical colored Petri Net (PN) as the general model.

First, multi-agent paradigm is a good implementation for reuse and integration of big data analytics modules in an agile and loosely coupled method. Second, the PN models provide effective simulation results in the system design period. It gives advice on business process design and workload balance control. Third, the CI framework provides an incrementally build and deployed method for system integration. It is especially suitable to the dynamic data analytics environment. These findings have both theoretical and managerial implications.

In this paper, the authors propose a CI framework, which includes both practical architectures and theoretical foundations, to solve the system integration problem in big data environment. It provides a new point of view to dynamically integrate large-scale modules in an organization. This paper also has practical suggestions for Chief Technical Officers, who want to employ big data technologies in their companies.

This paper aims to explore how big data analytics (BDA) collected and stored through specific data software [Construction Operations Building Information Exchange [COBie], integrated workplace management systems [IWMS], computer aided facilities management (CAFM), etc.] can play an essential role in improving the performance management system in the facility management (FM) industry. It defines the big data components and explores the benefit of BDA in any business through an extensive literature review and a pilot case study in the UAE.

The research was carried out based on a qualitative approach. It attempts to identify through a case study how the data collected and the technologies that go along with will increase the functionality and the efficiency of the FM services. The research studies the implementation of a big FM organization, hereafter referred as “M” of software that exports the data collected from COBie and the computer aided facilities management (CAFM) system and shapes them into input to improve the performance of the FM service providers. The study includes two components in anticipation of providing a complete picture: first, five semi-structured interviews with industry experts and company employees representing the hierarchy of the staff, i.e. top, middle and operational levels; one director, two managers and two operational-level employees were interviewed to determine the current situation of the company in terms of BDA; and second, detailed documents and archives records review for the data collected on a randomly chosen sample of facilities for the period 2013-2015. The interviews were designed to achieve two specific objectives. Primarily, they were aimed at collecting empirical evidence on the existing status of big data within the UAE FM context and at investigating the importance of the data collected for performance measurement in the industry as supported in the literature. Second, these interviews sought to identify any critical issues that need to be addressed within the data collection process when devising the big data platform for FM.

The paper seeks to provide a guideline to the service providers in the FM market to understand the importance of big data to be shared from the design and construction to the operational phase as it improves their operational performance.

This paper studies the impact of big data on the FM performance management, a very recent topic where only few researches were conducted earlier.