The paper proposes an innovative solution that employs online machine learning to continuously train and update models using sensor data from ships and ports. The proposed solution enhances the efficiency of ballast water management systems (BWMS), which are automated systems that utilize ultraviolet light and filters to purify and disinfect the ballast water that ships carry for maintaining their stability and balance. The solution allows it to grasp the complex and evolving patterns of ballast water quality and flow rate, as well as the diverse conditions of ships and ports. The solution also offers probabilistic forecasts that consider the uncertainty of future events that could impact the performance of ballast water management systems. An online machine learning architecture is proposed that can accommodate probabilistic based machine learning models and algorithms designed for specific training objectives and strategies. Three training methodologies are introduced: continuous training, scheduled training and threshold-triggered training. The effectiveness and reliability of the solution are demonstrated using actual data from ship and port performances. The results are visualized using time-based line charts and maps.

Reducing unplanned downtime requires monitoring of equipment health. This may not be possible in many cases as traditional health monitoring systems often rely on the use of historical data and maintenance information which is not always available, especially for small and medium-sized enterprises. This paper presents a practical approach that uses sensor data for real-time equipment health indication. The methodology proposed consists of a set of steps. It starts with feature engineering which may include feature extraction to transform raw sensor data into a format more suitable for analysis. Anomaly detection follows next, where various techniques are employed to find any deviations in the engineered features indicating potential equipment deterioration or abrupt failures. Then comes the most important stages equipment health indication and alert generation. These stages provide timely information about the equipment’s condition and any necessary interventions. These steps make it possible for such an approach to be effective even when there is little or no historical data available. The applicability of this approach is validated through a lab-based case study.

Smart manufacturing technologies (Industry 4.0) as solutions to enhance productivity and improve efficiency are a priority to manufacturing industries worldwide. Such solutions have the ability to extract, integrate, analyze and visualize sensor and data from other legacy systems in order to enhance the operational performance. This paper proposes a solution to the challenge of real-time analysis and visualization of sensor and ERP data. Dynamic visualization is achieved using a machine learning approach. The combination of real-time visualization and machine learning allows for early detection and prevention of undesirable situations or outcomes. The prototype system has so far been tested by a smart manufacturing company with promising results.

Due to the emergence of the fourth industrial revolution, manufacturing business all over the world is changing dramatically; it needs enhanced efficiency, competency and productivity. More and more manufacturing machines are equipped with sensors and the sensors produce huge volume of data. Most of the companies do neither realize the value of data nor how to capitalize the data. The companies lack techniques and tools to collect, store, process and analyze the data. The objective of this paper is to propose data analytic techniques to analyze manufacturing data. The analytic techniques will provide both descriptive and predictive analysis. In addition, data from the company's ERP system is integrated in the analysis. The proposed techniques will help the companies to improve operational efficiency and achieve competitive benefits.

In recent years, real-time processing and analytics systems for big data--in the context of Business Intelligence (BI)--have received a growing attention. The traditional BI platforms that perform regular updates on daily, weekly or monthly basis are no longer adequate to satisfy the fast-changing business environments. However, due to the nature of big data, it has become a challenge to achieve the real-time capability using the traditional technologies. The recent distributed computing technology, MapReduce, provides off-the-shelf high scalability that can significantly shorten the processing time for big data; Its open-source implementation such as Hadoop has become the de-facto standard for processing big data, however, Hadoop has the limitation of supporting real-time updates. The improvements in Hadoop for the real-time capability, and the other alternative real-time frameworks have been emerging in recent years. This paper presents a survey of the open source technologies that support big data processing in a real-time/near real-time fashion, including their system architectures and platforms.

In order to keep more detailed data available for longer periods, old data has to be reduced gradually to save space and improve query performance, especially on resource-constrained systems with limited storage and query processing capabilities. In this regard, some hand-coded data aggregation solutions have been developed; however, their actual usage have been limited, for the reason that hand-coded data aggregation solutions have proven themselves too complex to maintain. Maintenance need to occur as requirements change frequently and the existing data aggregation techniques lack flexibility with regards to efficient requirements change management. This paper presents an effective rule-based tool for data reduction based on gradual granular data aggregation. With the proposed solution, data can be maintained at different levels of granularity. The solution is based on high-level data aggregation rules. Based on these rules, data aggregation code can be auto-generated. The solution is effective, easy-to-use and easy-to-maintain. In addition, the paper also demonstrates the use of the proposed tool based on a farming case study using standard database technologies. The results show productivity of the proposed tool-based solution in terms of initial development time, maintenance time and alteration time as compared to a hand-coded solution.