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With the explosive growth of unstructured data (such as images, videos, and audios), unstructured data analytics is widespread in a rich vein of real-world applications. Many database systems start to incorporate unstructured data analysis to meet such demands. However, queries over unstructured and structured data are often treated as disjoint tasks in most systems, where hybrid queries ( i.e. , involving both data types) are not yet fully supported. In this paper, we present a hybrid analytic engine developed at Alibaba, named AnalyticDB-V (ADBV), to fulfill such emerging demands. ADBV offers an interface that enables users to express hybrid queries using SQL semantics by converting unstructured data to high dimensional vectors. ADBV adopts the lambda framework and leverages the merits of approximate nearest neighbor search (ANNS) techniques to support hybrid data analytics. Moreover, a novel ANNS algorithm is proposed to improve the accuracy on large-scale vectors representing massive unstructured data. All ANNS algorithms are implemented as physical operators in ADBV, meanwhile, accuracy-aware cost-based optimization techniques are proposed to identify effective execution plans. Experimental results on both public and in-house datasets show the superior performance achieved by ADBV and its effectiveness. ADBV has been successfully deployed on Alibaba Cloud to provide hybrid query processing services for various real-world applications.

With data explosion in scale and variety, OLAP databases play an increasingly important role in serving real-time analysis with low latency (e.g., hundreds of milliseconds), especially when incoming queries are complex and ad hoc in nature. Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts). In this paper, we introduce AnalyticDB, a real-time OLAP database system developed at Alibaba. AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries. Its storage engine extends hybrid row-column layout for fast retrieval of both structured data and data of complex types. To handle large-scale data with high query concurrency and write throughput, AnalyticDB decouples read and write access paths. To further reduce query latency, novel storage-aware SQL optimizer and execution engine are developed to fully utilize the advantages of the underlying storage and indexes. AnalyticDB has been successfully deployed on Alibaba Cloud to serve numerous customers (both large and small). It is capable of holding 100 trillion rows of records, i.e., 10PB+ in size. At the same time, it is able to serve 10m+ writes and 100k+ queries per second, while completing complex queries within hundreds of milliseconds.

An appropriate subset of features is needed for a classification-based pedestrian detection system since its performance is greatly affected by the features adopted. Moreover, the combination of different types of features (eg, grey-scale, colour) could improve the detection accuracy, so it is helpful to obtain a feature subset and the proportion of each type simultaneously for the classifier. However, because a larger number and various types of features are generally extracted to represent pedestrians better, it is difficult to achieve this. This paper proposed a co-evolutionary method to solve this problem. In the feature subset selection method, each sub-population mapped to one type of pedestrian feature, and then all sub-populations evolved co-operatively to obtain an optimal feature subset. Moreover, a strategy was specially designed to adjust the sub-population size adaptively in order to improve the optimizing performance. The proposed method has been tested on pedestrian detection applications and the experimental results illustrate its better performance compared with other methods such as genetic algorithm and AdaBoost.

At present, moving vehicle detection on airborne platform has been an important technology for urban traffic surveillance. In such a situation, most commonly used methods (e.g. image subtraction) could hardly work well because of some additional difficulties such as slow movement of vehicles and jam. This paper proposed a new moving vehicle detection method named MVD-RD for airborne urban traffic surveillance. First, the non-road regions are extracted using road detection technique. Secondly, the non-road regions with no vehicles are removed according to their size. As a result of this two-stage regions shrinkage, the detection area reduces a lot. Finally, to the reduced area, image subtraction is used to get all moving regions and then moving vehicles can be accurately filtered in a simple way. The experimental results show that, compared with traditional image subtraction methods used in airborne moving vehicle detection, the proposed MVD-RD method achieves much better performance in detection rate, false alarm rate, and detection speed.