Knowledge Discovery in Temporal Databases

* 9318773图智林越来越多的应用领域，从金融市场分析到天气预报，从监控超市采购到监控卫星图像，数据密集度越来越高。因此，数据库中的知识发现领域最近引起了数据库研究人员的极大兴趣。由于许多数据挖掘应用都是时态的，因此研究时态数据库环境下的模式发现问题具有重要意义。本计画的目的是（1）发展一个时间模式的表征与分类的架构;（2）在所提出的架构内，辨识与表征各种类型的模式发现问题;（3）评估现有技术的适用性，包括人工智能、运筹学、信号处理、统计时间序列分析，和其他相关学科的问题，从第（2）部分，并开发新的时间模式发现技术，只要有必要-这些技术也应该更有效，如果可能的话;和（4）并行化的发现算法，以便使它们计算上可行的;这是重要的，因为时间模式发现问题通常处理大量数据。作为该项目的成果，将开发一个系统，帮助用户从大量的时间数据中发现知识。本研究将为时态数据库中模式发现问题提供更好的理论理解，同时也为时态数据中模式发现提供一些实用工具。这项工作的潜在应用包括金融，营销和医疗应用等。 ***

*** 9318773 Tuzhilin More and more application domains, from financial market analysis to weather prediction, from monitoring supermarket purchases to monitoring satellite images, are becoming increasingly data-intensive. For this reason, the area of knowledge discovery in databases has recently attracted much interest of database researchers. Since many data mining applications are temporal in nature, it is important to study the problems of pattern discovery in the temporal database context. The purpose of this project is to (1) develop a framework for the characterization and classification of temporal patterns; (2) identify and characterize various types of pattern discovery problems within the proposed framework; (3) evaluate the applicability of existing techniques from artificial intelligence, operations research, signal processing, statistical time-series analysis, and other related disciplines to the problems from part (2), and to develop new temporal pattern discovery techniques, whenever necessary -- also these techniques should be made more efficient, if possible; and (4) parallelize the discovery algorithms in order to make them computationally feasible; this is important because temporal pattern discovery problems typically deal with large volumes of data. As a result of this project, a system will be developed that helps a user to discover knowledge from a large volume of temporal data. This research will provide a better theoretical understanding of the problems of pattern discovery in temporal databases, as well as provide some practical tools for finding patterns in temporal data. Potential applications of this work include financial, marketing, and medical applications, among others. ***