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1. 发明授权

US08499001B1 Systems and methods for index selection in collections of data 有权
标题翻译：数据收集中的索引选择的系统和方法
公开(公告)号：US08499001B1
公开(公告)日：2013-07-30
申请号：US12939637
申请日：2010-11-04
申请人： Wai-Yip To
发明人： Wai-Yip To
IPC分类号： G06F7/00
CPC分类号： G06F19/28 , G06N3/126
摘要： Systems and methods are disclosed that utilize a genetic algorithm to search for an index configuration for a collection of data such as, e.g., a database. Genetic algorithms can include stochastic search heuristics that mimic processes of natural evolution including inheritance, mutation, crossover, and selection. A population of chromosomes representing candidate index configurations can evolve to increase or optimize the fitness of the population and to identify the best (e.g., most fit) index configuration. Fitness of a chromosome may be measured based at least in part on the cost of computer resources used for executing Structured Query Language (SQL) statements in the indexed database. In various implementations, virtual indexing may be used to simulate building an index, chromosomes may be encoded using non-bitmapped representations of index configurations, chromosomes may include genes representing a column in a table in a database, dropping an index from a table in a database, or a composite index for a database, and/or a participation pool may be used to select fitter genes for an initial population of chromosomes.
摘要翻译：公开了利用遗传算法来搜索例如数据库的数据集合的索引配置的系统和方法。遗传算法可以包括模拟自然进化过程的随机搜索启发式，包括遗传，突变，交叉和选择。可以演化表示候选索引配置的染色体群体，以增加或优化群体的适应度并识别最佳（例如，最适合的）索引配置。可以至少部分地基于用于在索引数据库中执行结构化查询语言（SQL）语句的计算机资源的成本来测量染色体的适应度。在各种实现中，可以使用虚拟索引来模拟构建索引，可以使用索引配置的非位图表示来编码染色体，染色体可以包括表示数据库中的表中的列的基因，从表中的索引丢弃数据库或数据库的复合索引，和/或参与池可以用于选择初始染色体群体的拟合基因。

2. 发明授权

US08001074B2 Fuzzy-learning-based extraction of time-series behavior 有权
标题翻译：基于模糊学习的时间序列行为提取
公开(公告)号：US08001074B2
公开(公告)日：2011-08-16
申请号：US12023920
申请日：2008-01-31
申请人： Wai Yip To
发明人： Wai Yip To
IPC分类号： G06F9/44 , G06F15/18 , G06N7/02 , G06N7/06
CPC分类号： G06K9/6226 , G06N99/005
摘要： Systems and methods for extracting or analyzing time-series behavior are described. Some embodiments of computer-implemented methods include generating fuzzy rules from time series data. Certain embodiments also include resolving conflicts between fuzzy rules according to how the data is clustered. Some embodiments further include extracting a model of the time-series behavior via defuzzification and making that model accessible. Advantageously, to resolve conflicts between fuzzy rules, some embodiments define Gaussian functions for each conflicting data point, sum the Gaussian functions according to how the conflicting data points are clustered, and resolve the conflict based on the results of summing the Gaussian functions. Some embodiments use both crisp and non-trivially fuzzy regions and/or both crisp and non-trivially fuzzy membership functions.
摘要翻译：描述了提取或分析时间序列行为的系统和方法。计算机实现的方法的一些实施例包括从时间序列数据生成模糊规则。某些实施例还包括根据数据如何聚类来解决模糊规则之间的冲突。一些实施例还包括通过去模糊化提取时间序列行为的模型并使该模型可访问。有利地，为了解决模糊规则之间的冲突，一些实施例为每个冲突数据点定义高斯函数，根据冲突数据点如何聚类来对高斯函数求和，并且基于对高斯函数求和的结果来解决冲突。一些实施例使用清晰和非平凡的模糊区域和/或清晰和非平凡的模糊隶属函数。

3. 发明授权

US08332346B1 Fuzzy-learning-based extraction of time-series behavior 有权
标题翻译：基于模糊学习的时间序列行为提取
公开(公告)号：US08332346B1
公开(公告)日：2012-12-11
申请号：US13209018
申请日：2011-08-12
申请人： Wai Yip To
发明人： Wai Yip To
IPC分类号： G06F9/44 , G06N7/02 , G06N7/06
CPC分类号： G06K9/6226 , G06N99/005
摘要： Systems and methods for extracting or analyzing time-series behavior are described. Some embodiments of computer-implemented methods include generating fuzzy rules from time series data. Certain embodiments also include resolving conflicts between fuzzy rules according to how the data is clustered. Some embodiments further include extracting a model of the time-series behavior via defuzzification and making that model accessible. Advantageously, to resolve conflicts between fuzzy rules, some embodiments define Gaussian functions for each conflicting data point, sum the Gaussian functions according to how the conflicting data points are clustered, and resolve the conflict based on the results of summing the Gaussian functions. Some embodiments use both crisp and non-trivially fuzzy regions and/or both crisp and non-trivially fuzzy membership functions.
摘要翻译：描述了提取或分析时间序列行为的系统和方法。计算机实现的方法的一些实施例包括从时间序列数据生成模糊规则。某些实施例还包括根据数据如何聚类来解决模糊规则之间的冲突。一些实施例还包括通过去模糊化提取时间序列行为的模型并使该模型可访问。有利地，为了解决模糊规则之间的冲突，一些实施例为每个冲突数据点定义高斯函数，根据冲突数据点如何聚类来对高斯函数求和，并且基于对高斯函数求和的结果来解决冲突。一些实施例使用清晰和非平凡的模糊区域和/或清晰和非平凡的模糊隶属函数。

4. 发明申请

US20090198640A1 FUZZY-LEARNING-BASED EXTRACTION OF TIME-SERIES BEHAVIOR 有权
标题翻译：基于FUZZY-LEARNING的时间序列提取时间序列行为
公开(公告)号：US20090198640A1
公开(公告)日：2009-08-06
申请号：US12023920
申请日：2008-01-31
申请人： Wai Yip To
发明人： Wai Yip To
IPC分类号： G06N7/02
CPC分类号： G06K9/6226 , G06N99/005
摘要： Systems and methods for extracting or analyzing time-series behavior are described. Some embodiments of computer-implemented methods include generating fuzzy rules from time series data. Certain embodiments also include resolving conflicts between fuzzy rules according to how the data is clustered. Some embodiments further include extracting a model of the time-series behavior via defuzzification and making that model accessible. Advantageously, to resolve conflicts between fuzzy rules, some embodiments define Gaussian functions for each conflicting data point, sum the Gaussian functions according to how the conflicting data points are clustered, and resolve the conflict based on the results of summing the Gaussian functions. Some embodiments use both crisp and non-trivially fuzzy regions and/or both crisp and non-trivially fuzzy membership functions.
摘要翻译：描述了提取或分析时间序列行为的系统和方法。计算机实现的方法的一些实施例包括从时间序列数据生成模糊规则。某些实施例还包括根据数据如何聚类来解决模糊规则之间的冲突。一些实施例还包括通过去模糊化提取时间序列行为的模型并使该模型可访问。有利地，为了解决模糊规则之间的冲突，一些实施例为每个冲突数据点定义高斯函数，根据冲突数据点的聚类方法对高斯函数求和，并且基于对高斯函数求和的结果来解决冲突。一些实施例使用清晰和非平凡的模糊区域和/或清晰和非平凡的模糊隶属函数。

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