A Pre-Large Weighted-Fusion System of Sensed High-Utility Patterns

Within the current transportation infrastructure, we have seen a steady increase in the use of sensor technologies. These sensors, individually produce large amounts of data that then need to be fused and understood. Data commingling and data integration are difficult tasks when it comes to processing such data centrally, which can require costly hardware and software techniques. Over the past few years, high-utility pattern mining (HUPM) has gained popularity due to its growing capability in identifying useful information and knowledge from stored database data, as compared to the traditional frequent pattern mining. Existing works of HUPM mostly focus on mining the set of HUPs from one data source, which cannot be implemented in real-world scenarios. In this paper, we present a pre-large weighted high-utility pattern (PWHUP) fusion framework for integrating HUPs from different sensed data sources. The proposed PWHUP algorithm considers the size of the data source for discovering more relevant HUPs for integration, which is more applicable to real-life applications and scenarios in transportation and also within other data fusion scenarios. Moreover, the pre-large concept is applied to maintain the suggested pattern for later integration, which greatly improves the effectiveness of the proposed algorithm. Our in-depth experiments show that the designed approach has good performance for knowledge integration and outperforms existing non-integration solutions in precision, recall, and runtime.