Assessing Brunei Darussalam Public and Private Sector Readiness Towards Big Data Application

This exploratory study aims to assess and investigate Brunei Darussalam’s readiness in developing and applying big data technologies for its public and private sectors, using Social, Technological, Environmental and Policy (STEP) framework. The results show that the population are digitally literate (Social) and utilises smart devices as well as internet network connectivity that is widely offered by the local telecommunications company (Technology). The government of Brunei Darussalam established multiple digital transformation initiatives including implementation of 5G connectivity as well as digital economy masterplan to digitally transformed in the near future (Environment). Regardless of the absence of national digital data privacy policy (Policy) in Brunei, the recent nation’s successful big data application in public sector – BruHealth Application – to contain Covid-19 community spread was achieved. Alas, the existence of such policy in the near future will create opportunities for the local private sectors to capitalise big data technologies to their business strategies.

Assessing Brunei Darussalam Public and Private Sector Readiness Towards Big Data Application

This exploratory study aims to assess and investigate Brunei Darussalam’s readiness in developing and applying big data technologies for its public and private sectors, using Social, Technological, Environmental and Policy (STEP) framework. The results show that the population are digitally literate (Social) and utilises smart devices as well as internet network connectivity that is widely offered by the local telecommunications company (Technology). The government of Brunei Darussalam established multiple digital transformation initiatives including implementation of 5G connectivity as well as digital economy masterplan to digitally transformed in the near future (Environment). Regardless of the absence of national digital data privacy policy (Policy) in Brunei, the recent nation’s successful big data application in public sector – BruHealth Application – to contain Covid-19 community spread was achieved. Alas, the existence of such policy in the near future will create opportunities for the local private sectors to capitalise big data technologies to their business strategies.

Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting

To improve the safety and the performance of operators involved in risky and demanding missions (like drone operators), human-machine cooperation should be dynamically adapted, in terms of dialogue or function allocation. To support this reconfigurable cooperation, a crucial point is to assess online the operator’s ability to keep performing the mission. The article explores the concept of Operator Functional State (OFS), then it proposes to operationalize this concept (combining context and physiological indicators) on the specific activity of drone swarm monitoring, carried out by 22 participants on simulator SUSIE. With the aid of supervised learning methods (Support Vector Machine, k-Nearest Neighbors, and Random Forest), physiological and contextual are classified into three classes, corresponding to different levels of OFS. This classification would help for adapting the countermeasures to the situation faced by operators.

Preliminary Benefits of Big Data in the Construction Industry: A Case Study

Big data applications consist of i) data collection using big data sources, ii) storing and processing the data, and iii) analysing data to gain insights for creating organisational benefit. The influx of digital technologies and digitization in the construction process includes big data as one newly emerging digital technology adopted in the construction industry. Big data application is in a nascent stage in construction, and there is a need to understand the tangible benefit(s) that big data can offer the construction industry. This study explores the benefits of big data in the construction industry. Using a qualitative case study design, construction professionals in an Australian Construction firm were interviewed. The research highlights that the benefits of big data include reduction of litigation amongst projects stakeholders, enablement of near to real-time communication, and facilitation of effective subcontractor selection. By implication, on a broader scale, these benefits can improve contract management, procurement, and management of construction projects. This study contributes to an ongoing discourse on big data application, and more generally, digitization in the construction industry.

Power System Data Application Based on Data Association Rules

With the continuous development and progress of society, electricity has been integrated into people’s lives. However, power transmission is a very complex process that requires power transmission and power system conversion. A large amount of data will be generated during the operation of the power system. Through these data, we can use electricity better and more efficiently. This paper aims to study the power system data application based on data association rules. Based on the analysis of data association rules related algorithms and the application of data association algorithms in the power system, a power failure prediction system is designed and the performance of the system is analyzed. The test results show that the system has a very high transaction success rate, the longest response time does not exceed 20 seconds, and the CPU is operating normally, reaching the expected expectations.