The rapid growth of the Internet of Things (IoT) has resulted in an explosive increase in time-series data, making time-series databases (TSDBs) such as InfluxDB and OpenTSDB essential components in IoT ecosystems.At the same time, the decreasing cost of SSDs has facilitated their increasing adoption in large-scale data centers.Traditional TSDBs are primarily based on Log-Structured Merge Tree (LSM-tree) optimized for HDDs, which convert random reads and writes into sequential ones.However, these systems fail to fully exploit the unique characteristics of SSDs, such as random I/O operations and internal parallelism.In this paper, we present ReefsDB, an LSM-tree-based TSDB that is highly optimized for SSDs and implemented using the Rust programming language.We evaluate ReefsDB using the Time Series Benchmark Suite for write-intensive workloads, and the results demonstrate that ReefsDB is 2.9×∼3.2×faster than InfluxDB in write performance, while reducing read latencies by 27%∼62%.

Abstract In simulating fluids using position‐based dynamics, the accuracy and robustness depend on numerous numerical parameters, including the time step size, iteration count, and particle size, among others. This complexity can lead to unpredictable control of simulation behaviors. In this paper, we first reformulate the problem of enforcing fluid compressibility/incompressibility into an nonlinear optimization problem, and then introduce a semi‐implicit successive substitution method (SISSM) to solve the nonlinear optimization problem by adjusting particle positions in parallel. In contrast to calculating an intermediate variable, such as pressure, to enforce fluid incompressibility within the position‐based dynamics (PBD) framework, the proposed semi‐implicit approach eliminates the necessity of such calculations. Instead, it directly employs successive substitution of particle positions to correct density errors. This method exhibits reduced dependency to numerical parameters, such as particle size and time step variations, and improves consistency and stability in simulating fluids that range from highly compressible to nearly incompressible. We validates the effectiveness of applying a variety of different techniques in accelerating the convergence rate.

Deep Neural Networks (DNNs) are crucial for enhancing environmental perception in embedded intelligent systems, particularly in applications such as drones and autonomous vehicles. These mission-critical applications necessitate both computational accuracy and real-time performance. A significant challenge arises from the unpredictable execution times of DNNs and the variable periods of perception tasks. To address this, we present RTDeepEnsemble, an innovative framework designed for the real-time execution of DNN ensembles. RTDeepEnsemble dynamically manages the DNN ensemble as an imprecise computation model, judiciously selecting the optimal DNN for execution at runtime. This framework effectively arranges the DNN execution sequence, estimating utility through a refined Boosting algorithm. Additionally, RTDeepEnsemble prioritizes mandatory subtasks based on the Earliest Deadline First scheduling and employs a dynamic programming method to schedule optional subtasks. Comprehensive evaluations on the Nvidia Jetson Xavier NX platform, within a real-time object detection context, demonstrate RTDeepEnsemble's superiority in both accuracy and deadline miss rate. Notably, while maintaining real-time performance, we observe an accuracy increase of approximately 10 %, underscoring RTDeepEnsemble's potential as a solution for real-time machine perception.

Accurately forecasting traffic flow using time-series data from multimedia sensors remains a significant challenge, despite its importance for advancing intelligent transportation systems. Recent advancements in attention-based models have shown promise in capturing spatial-temporal dependencies in traffic flow data. Yet, these models exhibit three principal limitations: (1) they employ either factorized or coupled spatial-temporal attention mechanisms, potentially failing to fully harness the potential of these distinct approaches; (2) the attention allocation for spatial nodes is predominantly data-centric, which may overlook existing knowledge about the nodes' importance within the transportation network; (3) while traditional attention-based methods effectively capture long-term dependencies, they often struggle with adapting to the disparate lengths of temporal contexts. To overcome these limitations, we introduce a multi-way framework dubbed FEST that innovatively integrates both factorized and coupled spatial-temporal attention mechanisms. We then enhance FEST by incorporating PageRank-derived node importance scores to guide focus on nodes. Moreover, a novel multi-scale temporal learning approach is proposed to improve model capability with both long- and short-term temporal dynamics. Extensive experiments on real-world datasets under long- and short-term prediction scenarios confirm the effectiveness of our method.

Anomaly detection, particularly the detection of anomalous behaviors in dynamic and heterogeneous social networks, is becoming more and more crucial in real life. Traditional rule-based and feature-based methods cannot well capture the structural and temporal patterns of ever-changing user behaviors. Moreover, most of the existing works based on network embedding either rely on discretized snapshots, which have ignored accurate temporal relations among user behaviors and weakened the impact of new edges, or fail to utilize dynamic and heterogeneous information simultaneously to distinguish varying effects of new edges on existing nodes. In this paper, we propose an end-to-end continuous-time model, named Temporal Heterogeneous Graph Neural Network (THGNN), to detect anomalous behaviors (edges) in dynamic heterogeneous social networks. Specifically, the model constantly updates node embeddings by propagating the information of a new edge to its source and target nodes as well as their neighbors. In this process, heterogeneous encoders are employed to handle different types of nodes and edges. What is more, a novel dual-level distributive attention mechanism is designed to allocate the influence degree of a currently interacting node to its multiple neighbors, considering the combined effect of edge type and time interval information. That can be regarded as an extension of the classical aggregative attention mechanism in the opposite direction. Extensive experiments on four real-world datasets demonstrate that THGNN outperforms all the baselines on the task of anomalous edge detection, achieving an average AUC gain of 6% across all datasets.

Designing scheduling policies for multiprocessor real-time systems is challenging since the multiprocessor scheduling problem is NP-complete. The existing heuristics are customized policies that may achieve poor performance under some specific task loads. Thus, a new design pattern is needed to make the multiprocessor scheduling policies perform well under various task loads. In this paper, we investigate a new realtime scheduling policy based on reinforcement learning. For any given real-time task set, our policy can automatically derive a high performance by online learning. Specifically, we model the real-time scheduling process as a multi-agent cooperative game and propose multi-agent self-cooperative learning that overcomes the curse of dimensionality and credit assignment problems. Simulation results show that our approach can learn high-performance policies for various task/system models.

Plain text documents created and distributed on the Internet are ever changing in various forms. Mining topics of these documents has significant applications in many domains. Most of the literature is devoted to topic modeling, while sequential patterns of topics in document streams are ignored. Moreover, traditional sequential pattern mining algorithms mainly focused on frequent patterns for deterministic data sets, and thus not suitable for document streams with topic uncertainty and rare patterns. In this paper, we formulate and handle the mining problem of rare Sequential Topic Patterns (STPs) for Internet document streams, which are rare on the whole but relatively often for specific users, so also interesting. Since this type of rare STPs reflects users’ specific behaviors, our work can be applied in many fields, such as personalized context-aware recommendation and real-time monitoring on abnormal user behaviors on the Internet. We propose a novel approach to discovering user-related rare STPs based on the temporal and probabilistic information of concerned topics. After extracting topics from documents by LDA and sorting the document stream into sessions for different users during different time periods, the proposed algorithms discover rare STPs by (1) mining STP candidates for each user through an efficient algorithm based on pattern-growth, and (2) generating user-related rare STPs by pattern rarity analysis. Experiments on both synthetic and real data sets show that our approach can discover interesting rare STPs very effectively and efficiently.

A low cost on-line admission controller is required by hard real-time system working in dynamic circumstances. In this paper, we propose a new utilization based constant-time admission control algorithm, called AC for aperiodic tasks under EDF scheduling. We prove that given the same processor state, AC is safe and has stronger admission capability than the best existing utilization-based admission control algorithm with constant-time complexity. Simulation results show that AC also has good performance in success ratio and efficiency.

In this paper, we introduce an approach to visualize the inference process in a rule engine -- Drools, which employs Rete as its pattern matching algorithm. As a software visualization work, our approach is focused on both static structure of the Rete network and dynamic behavior of the inference process. Since logic programming is distinct from other traditional programming paradigms, our approach is also different from traditional program/algorithm visualization methods. In this paper, we first introduce the target we choose to visualize, and then provide a description of the problem and our visualization approach. Finally, with an implementation and an interesting case -- sudoku solving, we show that the visualization work is helpful to understanding not only the Rete algorithm, but also the rules used in the inference. Besides, our work supports debugging, tracing and analyzing the rule engine, which is useful in finding errors and optimization.

This work presents a novel approach for development of complex real-time systems, called the documentation-driven development (DDD) approach. This approach can enhance integration of computer aided software development activities, which encompass the entire life cycle. DDD will provide a mechanism to monitor and quickly respond to changes in requirements and provide a friendly communication and collaboration environment to enable different stakeholders to be easily involved in development processes and, therefore, significantly improve the agility of software development for complex real-time systems. DDD will also support automated software generation based on a computational model and some relevant techniques. DDD includes two main parts: a documentation management system (DMS) and a process measurement system (PMS). DMS will create, organize, monitor, analyze, and transform all documentation associated with the software development process. PMS will monitor the frequent changes in requirements and assess the effort and success possibility of development. A case study was conducted by a tool set that realized part of the proposed approach.