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.

In the evolving realm of natural language processing (NLP), generative AI models like ChatGPT are increasingly utilized across various applications. Among the possible purposes, many people are considering asking ChatGPT for relationship advice. However, the lack of in-depth examination of ChatGPT's response quality could be concerning when it is used for personal topics like mental health issues and intimate relationship problems. In these topics, a piece of misleading advice could cause harmful repercussions. In response to people's growing interest in using ChatGPT as a relationship advisor, our research evaluates ChatGPT's proficiency in discerning relationship advice. Specifically, we investigate its alignment with human judgements. We conducted our analysis with 13,138 Reddit posts about intimate relationship problems to examine the overall alignment. Furthermore, we investigate ChatGPT's consistency in judging intimate relationship advice by re-prompting identical queries. Our results indicate a significant disparity between ChatGPT and human judgments, with the model displaying inconsistency in its own decisions. Our findings emphasize the need for comprehensive insights into ChatGPT's mechanisms for intimacy problems and future improvements in its proficiency in helping people's relationship struggles.

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.