关键词: 交互式数据探索, 查询推荐, 查询结果优化, 用户反馈, 机器学习

Abstract:

Large data sets have exceeded the scale of terabytes and petabytes, and existing techniques can collect and store massive information. While database management systems have been constantly improved to offer a variety of complex data management capabilities, but the query tools cannot satisfy the needs of large data, so how to precisely understand and explore the massive data set remains a huge challenge. The focus of interactive data exploration (IDE) is to emphasize interaction, exploration and discovery. Users will accurately find the information they need with the minimum cost in the vast amounts of data. Firstly, this paper introduces the IDE and its application background, summarizes the general model and features of IDE, and analyzes the present situation of the query technology and the optimization techniques for query results. Furthermore, this paper analyzes and compares IDE prototype systems respectively. Finally, this paper summarizes and forecasts the techniques of IDE.

Key words: interactive data exploration, query recommendation, optimization for query results, user?feedback, machine learning