Time series database systems (TSDBs) are prevalent in many applications ranging from monitoring and IoT devices to scientific research. Those systems are specifically designed to efficiently manage data indexed by time. Because of the variety of workloads, the diversity of time series features, and the sophistication of existing TSDBs, there is no clear way to pick the most suitable system. In this demo, we introduce SEER, an automated, configurable, and interactive toolkit to evaluate TSDBs. SEER is based on TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. It implements an end-to-end pipeline for database benchmarking from data generation and feature contamination to workload evaluation. Users can define their portfolios by configuring and parameterizing custom queries, specifying their frequencies, controlling the type and level of data features, and indicating the type of workloads. Moreover, they can deploy new systems and/or reconfigure the pre-installed ones. SEER would process users' requests and gracefully recommend the best system on a use-case basis.

Time series databases are essential for the large-scale deployment of many critical industrial applications. In infrastructure monitoring, for instance, a database system should be able to process large amounts of sensor data in real-time, execute continuous queries, and handle complex analytical queries such as anomaly detection or forecasting. Several benchmarks have been proposed to evaluate and understand how existing systems and design choices handle specific use cases and workloads. Unfortunately, none of them fully covers the peculiar requirements of monitoring applications. Furthermore, they fall short of providing an automated way to generate representative real-world data and workloads for testing and evaluating these systems. We present TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. Our key contributions consist of (1) representative queries that meet the requirements that we collected from a water monitoring use case, and (2) a new scalable data generator method based on Generative Adversarial Networks (GAN) and Locality Sensitive Hashing (LSH). We demonstrate, through an extensive set of experiments, how TSM-Bench provides a comprehensive evaluation of the performance of seven leading time series database systems while offering a detailed characterization of their capabilities and trade-offs.

Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate choice of a detection method impacts many properties of mission-critical applications such as in monitoring a patient's health, where anomalies are inevitable but need to be detected securely. In this demo, we present a new evaluator that allows to peruse the performance of several anomaly detection techniques and supports practitioners in understanding the behavior and (dis-)advantages of each technique for a given dataset. In a simple and well-structured way, practitioners can specify the desired anomaly detection setup, and our system would tune the parameters of each technique and analyze their properties in an easily understandable report. The tool also allows recommending the most appropriate technique for each anomaly type and evaluation metric.

Time series streams are ubiquitous in many application domains, e.g., transportation, network monitoring, autonomous vehicles, or the Internet of Things (IoT). Transmitting and storing large amounts of such fine-grained data is however expensive, which makes compression schemes necessary in practice. Time series streams that are transmitted together often share properties or evolve together, making them significantly correlated. Despite the rich literature on compression methods, the state-of-the-art approaches do not typically avail correlation information when compressing times series. In this work, we demonstrate how one can leverage the correlation across several related time series streams to both drastically improve the compression efficiency and reduce the accuracy loss.We present a novel compression algorithm for time series streams called CORAD (CORelation-Aware compression of time series streams based on sparse Dictionary coding). Based on sparse dictionary learning, CORAD has the unique ability to exploit the correlation across multiple related time series to eliminate redundancy and perform a more efficient compression. To ensure the accuracy of the compressed time series, we further introduce a method to threshold the information loss of the compression. Extensive validation on real-world datasets shows that CORAD drastically outperforms state-of-the-art approaches achieving up to 40:1 compression ratios while minimizing the information loss.