公开(公告)号：US20090006365A1

公开(公告)日：2009-01-01

申请号：US11770505

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F7/00

CPC分类号： G06F17/30864 ,  G06F17/3064

摘要： Techniques for identifying similar queries based on their overall similarity and partial similarity of time series of frequencies of the queries are provided. To identify queries that are similar to a target query, the query analysis system generates, for each query, an overall similarity score for that query and the target query based on the time series of the query and the target query. The query analysis system also generates, for each query, partial similarity scores for the query and the target query based on various time sub-series of the overall time series of the queries. The query analysis system then identifies queries as being similar to the target query based on the overall similarity scores and the partial similarity scores of the queries.

摘要翻译： 提供了基于其查询的时间序列的总体相似性和部分相似性来识别类似查询的技术。 为了识别类似于目标查询的查询，查询分析系统根据查询和目标查询的时间序列为每个查询生成该查询和目标查询的总体相似性得分。 查询分析系统还根据查询的整个时间序列的各种时间子序列，为每个查询生成查询和目标查询的部分相似度分数。 然后，查询分析系统基于查询的总体相似性得分和部分相似性得分将查询识别为与目标查询相似。

公开(公告)号：US20090006326A1

公开(公告)日：2009-01-01

申请号：US11770307

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/30

CPC分类号： G06Q30/02

摘要： Representing queries and determining similarity of queries based on an autoregressive integrated moving average (“ARIMA”) model is provided. A query analysis system represents each query by its ARIMA coefficients. The query analysis system may estimate the frequency information for a desired past or future interval based on frequency information for some initial intervals. The query analysis system may also determine the similarity of a pair of queries based on the similarity of their ARIMA coefficients. The query analysis system may use various metrics, such as a correlation metric, to determine the similarity of the ARIMA coefficients.

摘要翻译： 提供了基于自回归综合移动平均（“ARIMA”）模型的查询和确定查询的相似性。 查询分析系统通过其ARIMA系数表示每个查询。 查询分析系统可以基于一些初始间隔的频率信息来估计期望的过去或将来间隔的频率信息。 查询分析系统还可以基于它们的ARIMA系数的相似度来确定一对查询的相似性。 查询分析系统可以使用诸如相关度量的各种度量来确定ARIMA系数的相似性。

公开(公告)号：US20090006313A1

公开(公告)日：2009-01-01

申请号：US11770462

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/40

CPC分类号： G06Q30/02

摘要： Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.

摘要翻译： 用于分析和建模查询频率的技术由查询分析系统提供。 查询分析系统分析查询的频率，以确定查询是时间依赖还是时间无关。 查询分析系统根据其周期性预测与时间相关的查询的频率。 查询分析系统根据与其他查询的因果关系预测与时间无关的查询的频率。 为了预测与时间无关的查询的频率，查询分析系统会随着时间的推移分析查询的频率，以确定频率的显着增加，这被称为“查询事件”或“事件”。 查询分析系统基于具有事件倾向于在要预测的查询的事件之前的查询来预测与时间无关的查询的频率。

公开(公告)号：US20090006312A1

公开(公告)日：2009-01-01

申请号：US11770358

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F7/00

CPC分类号： G06F17/30864 ,  G06Q30/02

摘要： Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.

摘要翻译： 用于分析和建模查询频率的技术由查询分析系统提供。 查询分析系统分析查询的频率，以确定查询是时间依赖还是时间无关。 查询分析系统根据其周期性预测与时间相关的查询的频率。 查询分析系统根据与其他查询的因果关系预测与时间无关的查询的频率。 为了预测与时间无关的查询的频率，查询分析系统会随着时间的推移分析查询的频率，以确定频率的显着增加，这被称为“查询事件”或“事件”。 查询分析系统基于具有事件倾向于在要预测的查询的事件之前的查询来预测与时间无关的查询的频率。

公开(公告)号：US07693908B2

公开(公告)日：2010-04-06

申请号：US11770358

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/30

CPC分类号： G06F17/30864 ,  G06Q30/02

摘要： Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.

摘要翻译： 用于分析和建模查询频率的技术由查询分析系统提供。 查询分析系统分析查询的频率，以确定查询是时间依赖还是时间无关。 查询分析系统根据其周期性预测与时间相关的查询的频率。 查询分析系统根据与其他查询的因果关系预测与时间无关的查询的频率。 为了预测时间无关查询的频率，查询分析系统随时间分析查询的频率，以识别频率的显着增加，这被称为“查询事件”或“事件”。查询分析系统预测频率 基于具有事件倾向于在要预测的查询的事件之前的查询的与时间无关的查询。

公开(公告)号：US07685099B2

公开(公告)日：2010-03-23

申请号：US11770445

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/30

CPC分类号： G06F17/30864 ,  G06Q30/02

摘要： Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.

摘要翻译： 用于分析和建模查询频率的技术由查询分析系统提供。 查询分析系统分析查询的频率，以确定查询是时间依赖还是时间无关。 查询分析系统根据其周期性预测与时间相关的查询的频率。 查询分析系统根据与其他查询的因果关系预测与时间无关的查询的频率。 为了预测时间无关查询的频率，查询分析系统随时间分析查询的频率，以识别频率的显着增加，这被称为“查询事件”或“事件”。查询分析系统预测频率 基于具有事件倾向于在要预测的查询的事件之前的查询的与时间无关的查询。

公开(公告)号：US08290921B2

公开(公告)日：2012-10-16

申请号：US11770505

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06F17/30864 ,  G06F17/3064

摘要： Techniques for identifying similar queries based on their overall similarity and partial similarity of time series of frequencies of the queries are provided. To identify queries that are similar to a target query, the query analysis system generates, for each query, an overall similarity score for that query and the target query based on the time series of the query and the target query. The query analysis system also generates, for each query, partial similarity scores for the query and the target query based on various time sub-series of the overall time series of the queries. The query analysis system then identifies queries as being similar to the target query based on the overall similarity scores and the partial similarity scores of the queries.

摘要翻译： 提供了基于其查询的时间序列的总体相似性和部分相似性来识别类似查询的技术。 为了识别类似于目标查询的查询，查询分析系统根据查询和目标查询的时间序列为每个查询生成该查询和目标查询的总体相似性得分。 查询分析系统还根据查询的整个时间序列的各种时间子序列，为每个查询生成查询和目标查询的部分相似度分数。 然后，查询分析系统基于查询的总体相似性得分和部分相似性得分将查询识别为与目标查询相似。

公开(公告)号：US08090709B2

公开(公告)日：2012-01-03

申请号：US11770307

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/30

CPC分类号： G06Q30/02

摘要： Representing queries and determining similarity of queries based on an autoregressive integrated moving average (“ARIMA”) model is provided. A query analysis system represents each query by its ARIMA coefficients. The query analysis system may estimate the frequency information for a desired past or future interval based on frequency information for some initial intervals. The query analysis system may also determine the similarity of a pair of queries based on the similarity of their ARIMA coefficients. The query analysis system may use various metrics, such as a correlation metric, to determine the similarity of the ARIMA coefficients.

摘要翻译： 提供了基于自回归综合移动平均（“ARIMA”）模型的查询和确定查询的相似性。 查询分析系统通过其ARIMA系数表示每个查询。 查询分析系统可以基于一些初始间隔的频率信息来估计期望的过去或将来间隔的频率信息。 查询分析系统还可以基于它们的ARIMA系数的相似度来确定一对查询的相似性。 查询分析系统可以使用诸如相关度量的各种度量来确定ARIMA系数的相似性。

公开(公告)号：US07685100B2

公开(公告)日：2010-03-23

申请号：US11770462

申请日：2007-06-28

申请人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Ning Liu ,  Jun Yan ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/30

CPC分类号： G06Q30/02

摘要： Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.

摘要翻译： 用于分析和建模查询频率的技术由查询分析系统提供。 查询分析系统分析查询的频率，以确定查询是时间依赖还是时间无关。 查询分析系统根据其周期性预测与时间相关的查询的频率。 查询分析系统根据与其他查询的因果关系预测与时间无关的查询的频率。 为了预测时间无关查询的频率，查询分析系统随时间分析查询的频率，以识别频率的显着增加，这被称为“查询事件”或“事件”。查询分析系统预测频率 基于具有事件倾向于在要预测的查询的事件之前的查询的与时间无关的查询。

公开(公告)号：US07529719B2

公开(公告)日：2009-05-05

申请号：US11378095

申请日：2006-03-17

申请人： Ning Liu ,  Benyu Zhang ,  Jun Yan ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

发明人： Ning Liu ,  Benyu Zhang ,  Jun Yan ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

IPC分类号： G06N5/00

CPC分类号： G06N5/02 ,  G06F17/30705

摘要： Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

摘要翻译： 具有计算机可执行指令和设备的计算机可读介质将文档或语料库分类。 张量空间模型（TSM），其通过高阶张量对文本进行建模，表示文档或文档语料库。 由多线代数技术支持，TSM为多因素结构分析提供了框架。 TSM还受到操作和提出的工具的支持，例如用于降低高阶张量尺寸的高阶奇异值分解（HOSVD）。 将尺寸减小的张量与表示可能类别的张量进行比较。 因此，文档或文档的语料库选择一个类别。 20个新闻组的数据集的实验结果表明，TSM对于文本分类的向量空间模型（VSM）是有利的。