RecovDB: Accurate and Efficient Missing Blocks Recovery for Large Time Series

With the emergence of the Internet of Things (IoT), time series data has become ubiquitous in our daily life. Making sense of time series is a topic of great interest in many domains. Existing time series analysis applications generally assume or even require perfect time series (i.e. regular time intervals without unknown values), but real-world time series are rarely so neat. They often contain "holes" of different sizes (i.e., single missing values, or blocks of consecutive missing values) due to some failures or irregular time intervals. Hence, missing value recovery is a prerequisite for many time series analysis applications. In this demo, we present RecovDB, a relational database system enhanced with advanced matrix decomposition technology for missing blocks recovery. This demo will show the main features of RecovDB that are important for today's time series analysis but are lacking in state-of-the-art technologies: i) recovering large missing blocks in multiple time series at once; ii) achieving high recovery accuracy by benefiting from different correlations across time series; iii) maintaining recovery accuracy under increasing size of missing blocks; iv) maintaining recovery efficiency with increasing time series' lengths and the number of time series; and iv) supporting all these features while being parameter-free. In this paper, we also compare the efficiency and accuracy of RecovDB against state-of-the-art recovery systems.