With the development of astronomical facilities, large-scale time series data observed by these facilities is being collected. Analyzing anomalies in these astronomical observations is crucial for uncovering potential celestial events and physical phenomena, thus advancing the scientific research process. However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms. To overcome the challenges, we propose AERO, a novel two-stage framework tailored for unsupervised anomaly detection in astronomical observations. In the first stage, we employ a Transformer-based encoder-decoder architecture to learn the normal temporal patterns on each variate (i.e., star) in alignment with the characteristic of variate independence. In the second stage, we enhance the graph neural network with a window-wise graph structure learning to tackle the occurrence of concurrent noise characterized by spatial and temporal randomness. In this way, AERO is not only capable of distinguishing normal temporal patterns from potential anomalies but also effectively differentiating concurrent noise, thus decreasing the number of false alarms. We conducted extensive experiments on three synthetic datasets and three real-world datasets. The results demonstrate that AERO outperforms the compared baselines. Notably, compared to the state-of-the-art model, AERO improves the F1-score by up to 8.76% and 2.63% on synthetic and real-world datasets respectively.

The recent surge in learned index algorithms, along-side traditional indexes, has greatly diversified indexing options to support query processing in databases. Despite the rapid expansion of learned indexes, there remains a significant gap in tools for index algorithm selection. Traditional research on index selection has largely focused on recommending which columns to index, as the choice between algorithms like B+tree or hash index was once straightforward. This was managed through basic rules or experiential judgment, given the historically limited options. However, this approach is inadequate today, due to the growing diversity and complexity of index algorithms. In this paper, we introduce a Learned INDex Algorithm Selector, LINDAS. Taking a learned approach, LINDAS uniquely focuses on automatically selecting the most suitable index algorithm for a specific column, that satisfies diverse performance objectives in a wide range of applications. We explore the design space of LINDAS, employing a carefully designed featurization approach to capture both data-and workload-specific characteristics with attention mechanisms, as well as the meta-features of index algorithms. Two variants of LINDAS are designed to cater to diverse scenarios and adapt readily to new datasets, workloads, and emerging index algorithms. Comprehensive evaluations of LINDAS across various datasets and workloads demonstrate its effectiveness and superiority compared to applicable baselines.

As modern applications generate data at an unprecedented speed and often require the querying/analysis of data spanning a large duration, it is crucial to develop indexing techniques that cater to larger-than-memory databases, where data reside on heterogeneous storage devices (such as memory and disk), and support fast data insertion and query processing. In this paper, we propose FILM, a F ully learned I ndex for L arger-than- M emory databases. FILM is a learned tree structure that uses simple approximation models to index data spanning different storage devices. Compared with existing techniques for larger-than-memory databases, such as anti-caching, FILM allows for more efficient query processing at significantly lower main-memory overhead. FILM is also designed to effectively address one of the bottlenecks in existing methods for indexing larger-than-memory databases that is caused by data swapping between memory and disk. More specifically, updating the LRU (for Least Recently Used) structure employed by existing methods for cold data identification (determining the data to be evicted to disk when the available memory runs out) often incurs significant delay to query processing. FILM takes a drastically different approach by proposing an adaptive LRU structure and piggybacking its update onto query processing with minimal overhead. We thoroughly study the performance of FILM and its components on a variety of datasets and workloads, and the experimental results demonstrate its superiority in improving query processing performance and reducing index storage overhead (by orders of magnitudes) compared with applicable baselines.