Industry 4.0 enables the management of factories manufacturing products with complexity and flexibility. The corresponding logistic services must provide greater accuracy and efficiency in logistic operations. The Internet of Things (IoT) is an important aspect for smart logistics in the context of Industry 4.0. For instance, intelligent logistics models use IoT integrated technologies, e.g. radio frequency identification (RFID), wireless sensor network (WSN) and cloud computing, to enhance the traceability and decision supports of logistic processes in real-time speed, high accuracy, and flexibility. This research focuses on analyzing the related technology roadmaps for the adoption of IoT technologies in smart logistic services. A case research is conducted specifically to identify the relationship between IoT-oriented technologies and deployed advanced logistic services. The logistic operations are organized into an ontology schema based on a four level service framework. The research proposes a roadmap approach to visualize the patent allocations and evolutions corresponding to logistic services at each level. Although the patent roadmap methodology is generic, this research focuses on the two industry leaders, which are UPS and IBM. Using the roadmap methodology, the IoT enabled smart logistic patents are analyzed to identify technology-related business strengths and strategies.

Recently, the piecewise linear representation (PLR) method has been applied to the stock market for pattern matching. As such, similar patterns can be retrieved from historical data and future prices of the stock can be predicted according to the patterns retrieved. In this paper, a different approach is taken by applying PLR to decompose historical data into different segments. As a result, temporary turning points (trough or peak) of the historical stock data can be detected and inputted to the backpropagation neural network (BPN) for supervised training of the model. After this, a new set of test data can trigger the model when a buy or sell point is detected by BPN. An intelligent PLR (IPLR) model is further developed by integrating the genetic algorithm with the PLR to iteratively improve the threshold value of the PLR. Thus, it further increases the profitability of the model. The proposed system is tested on three different types of stocks, i.e., uptrend, steady, and downtrend. The experimental results show that the IPLR approach can make significant amounts of profit on stocks with different variations. In conclusion, the proposed system is very effective and encouraging in that it predicts the future trading points of a specific stock.