With the prevalence of in-database AI-powered analytics, there is an increasing demand for database systems to efficiently manage the ever-expanding number and size of deep learning models. However, existing database systems typically store entire models as monolithic files or apply compression techniques that overlook the structural characteristics of deep learning models, resulting in suboptimal model storage overhead. This paper presents NeurStore, a novel in-database model management system that enables efficient storage and utilization of deep learning models. First, NeurStore employs a tensor-based model storage engine to enable fine-grained model storage within databases. In particular, we enhance the hierarchical navigable small world (HNSW) graph to index tensors, and only store additional deltas for tensors within a predefined similarity threshold to ensure tensor-level deduplication. Second, we propose a delta quantization algorithm that effectively compresses delta tensors, thus achieving a superior compression ratio with controllable model accuracy loss. Finally, we devise a compression-aware model loading mechanism, which improves model utilization performance by enabling direct computation on compressed tensors. Experimental evaluations demonstrate that NeurStore achieves superior compression ratios and competitive model loading throughput compared to state-of-the-art approaches.

In the realm of transactional stream processing (TSP), the challenge lies in providing a unified execution model that seamlessly integrates transactional and stream-oriented capabilities. Existing TSP engines (TSPEs) largely employ non-adaptive scheduling techniques, leaving multicore parallelism underutilized due to intricate workload dependencies. We demonstrate MorphStream, a state-of-the-art TSPE built for unprecedented scalability on multicores. MorphStream distinguishes itself by employing an adaptive scheduling algorithm, explicitly designed to unlock the full potential of multicore architectures even under complex workload conditions. This enables MorphStream to make optimal trade-offs in performance metrics under varying workload characteristics. To enhance user engagement, the demonstration will showcase MorphStream's graphical user interface, specifically engineered to simplify the implementation and deployment of complex streaming applications while providing detailed and comprehensive performance monitoring and analytics for the job execution runtime.

Oil resources are becoming increasingly scarce. Pure electric vehicles have huge advantages, in terms of energy efficiency and emission reduction. In cities, the locations of required charging stations and the number of required charging piles are determined according to the traffic flow on a road. Unreasonable allocation not only creates safety problems due to high electrical loads, but also increases the cost of the placements. Such allocations will involve the many-to-many (M2M) assignment in the process, which is necessary to establish an optimal model for distributing. Thus, this paper formalizes the charging pile siting problem (CPSP) via the group multirole assignment (GMRA) model, which is one of the most important methods to deal with the M2M problem. Based on GMRA, this paper proposes a role negotiation method by using a spectral clustering K-Means++ Algorithm based on location. The formalization of GMRA makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX) via the Integer Programming (IP). All the proposed approaches are verified by simulation experiments, which have been proved to be efficient, feasible and practicable.

Community-based Question Answering (CQA) sites have become popular since they allow users to get answers to complex, detailed and personal question from other users directly. However, since answering a question depends on the ability and willingness of other users to address the askers' real needs, a significant fraction of the questions remain unanswered. To decrease the unanswered question rate and then improve the user experience, in this paper, a multidimensional scaling (MDS) based data reorganization method is proposed. By using this method, the CQA system can predict the askers' intention and accordingly provide related previous question/answer pairs to help them find useful information. The method has been evaluated on an off-line dataset extracted from Baidu Zhidao and the result has shown its promising potential in knowledge management in CQA systems.