JSON is a very popular data format in many applications in Web and enterprise. Recently, many data analytical systems support the loading and querying JSON data. However, JSON parsing can be costly, which dominates the execution time of querying JSON data. Many previous studies focus on building efficient parsers to reduce this parsing cost, and little work has been done on how to reduce the occurrences of parsing. In this paper, we start with a study with a real production workload in Alibaba, which consists of over 3 million queries on JSON. Our study reveals significant temporal and spatial correlations among those queries, which result in massive redundant parsing operations among queries. Instead of repetitively parsing the JSON data, we propose to develop a cache system named Maxson for caching the JSON query results (the values evaluated from JSONPath) for reuse. Specifically, we develop effective machine learning-based predictor with combining LSTM (long shortterm memory) and CRF (conditional random field) to determine the JSONPaths to cache given the space budget. We have implemented Maxson on top of SparkSQL. We experimentally evaluate Maxson and show that 1) Maxson is able to eliminate the most of duplicate JSON parsing overhead, 2) Maxson improves end-to-end workload performance by 1.5-6.5×.

Object tracking using vision technology is one of the key but complex functions in navigation system of Intelligent Vehicles; it became more difficult in case of there are partial occlusions and significant clutter. A mean shift embedded approach is presents for vehicle tracking under real road scenes. The HSI model and orientation histogram are used to represent the object feature; the mean shift is employed to fast searching the mode of the potential object in a neighborhood of current frame. Experimental results demonstrate that the proposed approach is robust and validate in complicated real scenes.