We propose the concept of Intra-Query Runtime Elasticity (IQRE) for cloud-native data analysis. IQRE enables a cloud-native OLAP engine to dynamically adjust a query's Degree of Parallelism (DOP) during execution. This capability allows users to utilize cloud computing resources more cost-effectively. We present Accordion, the first IQRE query engine. Accordion can adjust the parallelism of a query at any point during query execution without pausing data processing. It features a user-friendly interface and an auto-tuner backed by a "what-if" service to allow users to adjust the DOP according to their query latency constraints. The design of Accordion follows the execution model in Presto, an open-source distributed SQL query engine developed at Meta. We present the implementation of Accordion and demonstrate its ease of use, showcasing how it enables users to minimize compute resource consumption while meeting their query time constraints.

Cloud databases empower users to leverage vast computing resources for efficient data analysis. However, achieving cost-effective utilization of these resources remains a challenge. Users often struggle to balance computing resource allocation with their temporal and financial constraints. To address this, we propose the concept of Intra-Query Runtime Elasticity (IQRE), which allows a cloud-native OLAP engine to dynamically adjust the query Degree of Parallelism (DOP) during query execution. We introduce Accordion, the first IQRE engine. Accordion features a friendly user interface for parallelism adjustments. It includes an auto-tuner that supports both manual and automatic DOP tuning during query execution. In this demonstration, we present Accordion's architecture and provide attendees with hands-on experience, allowing them to execute queries and adjust query parallelism during query execution based on their time or cost budgets.

Abstract To enhance the accuracy of fine‐grained image classification and address challenges such as excessive interference factors within the dataset, inadequate extraction of local key features, and insufficient channel semantic association, a dual‐branch information interaction model that integrates convolutional neural networks (CNN) with Vision Transformers is proposed. This model leverages the Vision Transformer branch to extract global features, which are subsequently combined with the CNN branch to further augment the model's capability for local information extraction. In order to enhance the ability of the CNN branch to extract global information and reduce the loss of feature information, a feature enhancement module is added to the CNN branch. Since the Vision Transformer branch directly convolves with the convolution kernel will result in the inability to learn the underlying features of the image, a shallow feature extraction module is proposed, and the CNN and Vision Transformer branches interact with the information of the dual branches through the down‐sampling Down module and the up‐sampling UP module. The accuracy of the improved method on CUB‐200‐2011, Stanford Cars and FGVC‐Aircraft fine‐grained image classification datasets are 95.2%, 97.1% and 96.9%, respectively. The experimental results show that the method has good generalization on different datasets.

Detecting anomalies with high accuracy and real time from large amounts of streaming data is a challenge for many real-world applications, such as smart city, astronomical observations, and remote sensing. This article focuses on a special kind of stream, catalog stream, whose high-level catalog structure can be used to analyze the stream effectively. We first formulate the anomaly detection in catalog streams as a constrained optimization problem based on a catalog stream matrix. Then, a novel filtering-identifying based anomaly detection algorithm (FIAD) is proposed, which includes two complementary strategies, true event identifying and false alarm filtering, data-oriented general method and domain-oriented specific method together, to detect truly valuable anomalies. Furthermore, different kinds of attention windows are developed to provide corresponding data for various algorithm components. A scalable and lightweight catalog stream processing frameworkCSPFis designed to support and implement the proposed method efficiently. A prototype system is developed to evaluate the proposed algorithm. Extensive experiments are conducted on the catalog stream data sets from an operational super large field-of-view high-cadence astronomy observation. The experimental results show that the proposed method can achieve a false-positive rate as low as 0.04%, reduces the false alarms by 98.6% compared with the existing methods, and the latency to handle each catalog is 2.1 seconds (much less than the required 15 seconds). Furthermore, a total of 36 transient candidates, including seven microlensing events, 27 superflares, and two dual-superflares, are detected from 21.67 million stars (involving 1.09 million catalogs) from one observation season.

We present the design of and a demonstration for SciDetector, a system of scientific research for online analysis. SciDetector can efficiently online analyze scientific events on large-scale newly arriving data. In modern scientific research, especially astronomy survey, with the development of the observation infrastructure, Short-Timescale and Large Field-of-view (STLF) sky survey has been the major topic. However, existing scientific system focus on offline analysis of long-term historical data, not real-time analysis of large-scale scientific data. Using real data, we demonstrate how SciDetector processes the data and generate the scientific product.