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The technique of searching for similar patterns among time series data is very useful in many applications. The problem becomes difficult when shifting and scaling are considered. We find that we can treat the problem geometrically and the major contribution of this paper is that a uniform geometrical model that can analyze the existing related methods is proposed. Based on the analysis, we conclude that the angle between two vectors after the Shift-Eliminated Transformation is a more intrinsical similarity measure invariant to shifting and scaling. We then enhance the original conical index to adapt to the geometrical properties of the problem and compare its performance with that of sequential search and R*-tree. Experimental results show that the enhanced conical index achieves larger improvement on R*-tree and sequential search in high dimension. It can also keep a steady performance as the selectivity increases. Part of the result related to the geometrical model has been published in the Proceedings of the 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp 237–248. Mi Zhou was born in China. He received his BS and MS degrees in computer science from the Northeastern University, China, in 1999 and 2002, respectively. He is currently pursuing the Ph D degree in the Computer Science and Engineering Department, The Chinese University of Hong Kong. His research interests include indexing of time series data, high-dimensional index, and sensor network. Man-Hon Wong received his BSc and MPhil degrees from The Chinese University of Hong Kong in 1987 and 1989 respectively. He then went to University of California at Santa Barbara where he got the PhD degree in 1993. Dr. Wong joined The Chinese University of Hong Kong in August 1993 as an assistant professor. He was promoted to associate professor in 1998. His research interests include transaction management, mobile databases, data replication, distributed systems, and computer and network security. Kam-Wing Chu was born in Hong Kong. He received his BS and MPhil degrees in computer science and engineering from The Chinese University of Hong Kong. When he was in Hong Kong, his research interests included database indexing of high dimensional data, and data mining. He later went to United States and received his MS degree in computer science from University of Maryland at College Park. While he was in Maryland, he focused on high performance implementation and algorithm design of advanced database systems. He is currently a senior software engineer in Server Performance group at Actuate Corporation. His expertise is in enterprise software development and software performance optimization.  相似文献   
In this paper, an algorithm is proposed for subsequence matching that supports normalization transform in time-series databases. Normalization transform enables finding sequences with similar fluctuation patterns even though they are not close to each other before the normalization transform. Simple application of existing subsequence matching algorithms to support normalization transform is not feasible since the algorithms do not have information for normalization transform of subsequences of arbitrary lengths. Application of the existing whole matching algorithm supporting normalization transform to the subsequence matching is feasible, but requires an index for every possible length of the query sequence causing serious overhead on both storage space and update time. The proposed algorithm generates indexes only for a small number of different lengths of query sequences. For subsequence matching it selects the most appropriate index among them. Better search performance can be obtained by using more indexes. In this paper, the approach is called index interpolation. It is formally proved that the proposed algorithm does not cause false dismissal. The search performance can be traded off with storage space by adjusting the number of indexes. For performance evaluation, a series of experiments is conducted using the indexes for only five different lengths out of lengths 256512 of the query sequence. The results show that the proposed algorithm outperforms the sequential scan by up to 2.4 times on the average when the selectivity of the query is 10–2 and up to 14.6 times when it is 10–5. Since the proposed algorithm performs better with smaller selectivities, it is suitable for practical situations, where the queries with smaller selectivities are much more frequent.  相似文献   
Time series are often generated by continuous sampling or measurement of natural or social phenomena. In many cases, events cannot be represented by individual records, but instead must be represented by time series segments (temporal intervals). A consequence of this segment-based approach is that the analysis of events is reduced to analysis of occurrences of time series patterns that match segments representing the events.A major obstacle on the path toward event analysis is the lack of query languages for expressing interesting time series patterns. We have introduced SQL/LPP (Perng and Parker, 1999). Which provides fairly strong expressive power for time series pattern queries, and are now able to attack the problem of specifying queries that analyze temporal coupling, i.e., temporal relationships obeyed by occurrences of two or more patterns.In this paper, we propose SQL/LPP+, a temporal coupling verification language for time series databases. Based on the pattern definition language of SQL/LPP (Perng and Parker, 1999), SQL/LPP+ enables users to specify a query that looks for occurrences of a cascade of multiple patterns using one or more of Allen's temporal relationships (Allen, 1983) and obtain desired aggregates or meta-aggregates of the composition. Issues of pattern composition control are also discussed.  相似文献   
时序数据库中的数据挖掘研究   总被引:3,自引:0,他引:3  
时序数据库中的某个字段的值是随着时间而不断变化的,例如股票价格每天的涨跌、浏览网页的次序等。文章运用数据挖掘的方法来对这些数据库进行趋势分析、时序分析、序列模式挖掘以及周期分析。  相似文献   
随着电网规模扩大和集约化运行管理体系的建设,地区电网调度控制系统监控范围急剧增加,传统以商用数据库为基础的历史数据管理技术和早期使用时间序列数据库的历史数据管理技术已不能满足大型地区电网调度运行和各类应用深化拓展的需要.结合商用数据库、文件系统,以时间序列数据库为重要存储手段的海量历史数据处理技术能很好地解决上述问题.文中首先介绍了以时间序列数据库为核心的历史数据管理体系架构,然后阐述了解决大容量、高效率、高可靠问题的关键技术,紧接着介绍了部分依赖历史时序数据的深化应用.该技术已成功应用于苏州电网调度控制系统,它很好地适应了“大运行”的要求,提升了地区电网调度运行水平,为地区智能电网建设提供了有力的技术支撑.  相似文献   
时间序列数据在许多领域广泛存在,有海量和复杂的特点,直接查询出所有的原始数据并对其进行分析十分耗时,且对计算机的内存消耗极大。为此,提出一种基于分段极值的时间序列数据查询显示方法,对需要查询分析数据的时间范围进行分段,根据各个时间段数据的极值及总取点个数来确定该时间段的取点个数,通过数据库本身的查询机制实现均匀取点,并结合多线程机制实现各时间段数据的并行查询及曲线绘制。实验结果表明,与传统查询及可视化方法相比,该方法能够指定取点数量,并在取点数量确定的情况下,绘制曲线能较好地逼近原始曲线,且极大地缩短曲线的查询绘制时间,具有较好的工程实用性。  相似文献   
为了能够掌握动态目标的飞行特性,提出了一种模拟目标动态雷达散射截面(RCS)的方法。该方法根据雷达目标航迹相关数据,结合测量坐标系和目标坐标系转换原理,采用多层快速多极子算法,仿真建立了目标静态下的RCS数据库。同时,通过编程实现了目标动态RCS模拟程序,并通过分析得到动态目标RCS曲线。该结果表明,雷达目标的动态RCS模拟对掌握目标的飞行特性起到了一定的作用。该方法可以代替RCS实际测量过程,具有实现简单、操作性好等优点。  相似文献   
数据备份是任何自动化系统在设计之初就必须考虑的项目。能量管理系统(EMS)对备份的数据量、备份的版本、备份及恢复的时间有其特殊的需求,而常用的商用备份工具不能满足EMS的运行需求,同时,目前的商用备份软件不支持时间序列数据库。文中对电力系统EMS数据备份方案进行研究,制定出一套符合电力系统安全生产要求的备份及恢复策略,并已在南方电网网调EMS中投入实际运行。  相似文献   
In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms.  相似文献   
转移规则挖掘算法的提出对于关联挖掘算法等原有数据挖掘算法做了重要补充.然而,目前的转移规则挖掘算法由于选取挖掘对象的不当,往往使转移规则缺乏代表性,导致规则无参考价值.在分析原有转移规则挖掘方法不足的基础上,提出了两种改进的方法:基于关联挖掘的转移规则发现和基于概率关系数据模式的转移规则挖掘,并把这两种方法和现有的转移规则挖掘算法融合到一起,构造一个更为有效和可行的新的基于时序数据库的转移规则挖掘算法.  相似文献   
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