In time series visualization, sampling is used to reduce the number of points while retaining the visual features of the raw time series. Area-based Largest Triangle Sampling (LTS) excels at preserving perceptually critical points. However, the heuristic solution to LTS by sequentially sampling points with the locally largest triangle area (a.k.a. Largest-Triangle-Three-Buckets, LTTB) suffers from suboptimal solution and query inefficiency. We address the shortcomings by contributing a novel Iterative Largest Triangle Sampling (ILTS) algorithm with convex hull acceleration. It refines the sampling results iteratively, capturing a broader perspective by integrating more points in each iteration. Remarkably, we prove that the largest triangle can always be found in the precomputed convex hulls, making the iterative sampling still efficient. Experiments demonstrate increased visual quality over state-of-the-art baselines and significant speedups over the brute force approach.

A typical industrial scenario encounters thousands of devices with millions of sensors, consistently generating billions of data points. It poses new requirements of time series data management, not well addressed in existing solutions, including (1) device-defined ever-evolving schema, (2) mostly periodical data collection, (3) strongly correlated series, (4) variously delayed data arrival, and (5) highly concurrent data ingestion. In this paper, we present a time series database management system, Apache IoTDB. It consists of (i) a time series native file format, TsFile, with specially designed data encoding, and (ii) an IoTDB engine for efficiently handling delayed data arrivals and processing queries. We introduce a native distributed solution with distributed queries optimized by parallel operators. We also explore efficient TsFile synchronization mechanisms, ensuring seamless data integration without the need for ETL processes. The system achieves a throughput of 10 million inserted values per second. Queries such as 1-day data selection of 0.1 million points and 3-year data aggregation over 10 million points can be processed in 100 ms. Comparisons with InfluxDB, TimescaleDB, KairosDB, Parquet and ORC over real world data loads demonstrate the superiority of IoTDB and TsFile.

When analyzing time series, often interactively, the analysts frequently demand to visualize instantly large-scale data stored in databases. M4 visualization selects the first, last, bottom and top data points in each pixel column to ensure pixel-perfectness of the two-color line chart visualization. While M4 already shows its preciseness of encasing time series in different scales into a fixed size of pixels, how to efficiently support M4 representation in a time series native database is still absent. It is worth noting that, to enable fast writes, the commodity time series database systems, such as Apache IoTDB or InfluxDB, employ LSM-Tree based storage. That is, a time series is segmented and stored in a number of chunks, with possibly out-of-order arrivals, i.e., disordered on timestamps. To implement M4, a natural idea is to merge online the chunks as a whole series, with costly merge sort on timestamps, and then perform M4 representation as in relational databases. In this study, we propose a novel chunk merge free approach called M4-LSM to accelerate M4 representation and visualization. In particular, we utilize the metadata of chunks to prune and avoid the costly merging of any chunk. Moreover, intra-chunk indexing and pruning are enabled for efficiently accessing the representation points, referring to the special properties of time series. Remarkably, the time series database native operator M4-LSM has been implemented in Apache IoTDB, an open-source time series database, and deployed in companies across various industries. In the experiments over real-world datasets, the proposed M4-LSM operator demonstrates high efficiency without sacrificing preciseness.

A typical industrial scenario encounters thousands of devices with millions of sensors, consistently generating billions of data points. It poses new requirements of time series data management, not well addressed in existing solutions, including (1) device-defined ever-evolving schema, (2) mostly periodical data collection, (3) strongly correlated series, (4) variously delayed data arrival, and (5) highly concurrent data ingestion. In this paper, we present a time series database management system, Apache IoTDB. It consists of (i) a time series native file format, TsFile, with specially designed data encoding, and (ii) an IoTDB engine for efficiently handling delayed data arrivals and processing queries. The system achieves a throughput of 10 million inserted values per second. Queries such as 1-day data selection of 0.1 million points and 3-year data aggregation over 10 million points can be processed in 100 ms. Comparisons with InfluxDB, TimescaleDB, KairosDB, Parquet and ORC over real world data loads demonstrate the superiority of IoTDB and TsFile.

The amount of time-series data that is generated has exploded due to the growing popularity of Internet of Things (IoT) devices and applications. These applications require efficient management of the time-series data on both the edge and cloud side that support high throughput ingestion, low latency query and advanced time series analysis. In this demonstration, we present Apache IoTDB managing time-series data to enable new classes of IoT applications. IoTDB has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and time-series database with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, down-sampling and sub-sequence similarity search. An edge-to-cloud time-series data management application is chosen to demonstrate how IoTDB handles time-series data in real-time and supports advanced analytics by integrating with Hadoop and Spark. An end-to-end IoT data management solution is shown by integrating IoTDB with PLC4x, Calcite, and Grafana.