公开(公告)号：US09002822B2

公开(公告)日：2015-04-07

申请号：US13529681

申请日：2012-06-21

申请人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

发明人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

IPC分类号： G06F7/00 ,  G06F17/00 ,  G06F17/30

CPC分类号： G06F17/30103 ,  G06F17/30454 ,  G06F17/30516

摘要： A cost monitoring system can monitor a cost of queries executing in a complex event processing system, running on top of a pay-as-you-go cloud infrastructure. Certain embodiments may employ a generic, cloud-platform independent cost model, multi-query optimization, cost calculation, and/or operator placement techniques, in order to monitor and explain query cost down to an operator level. Certain embodiments may monitor costs in near real-time, as they are created. Embodiments may function independent of an underlying complex event processing system and the underlying cloud platform. Embodiments can optimize a work plan of the cloud-based system so as to minimize cost for the end user, matching the cost model of the underlying cloud platform.

摘要翻译： 成本监控系统可以监视在复杂事件处理系统中执行的查询的成本，并在付费即付云基础架构上运行。 某些实施例可以采用通用的，独立于云平台的成本模型，多查询优化，成本计算和/或操作员放置技术，以便监视和解释查询成本降低到操作者级别。 某些实施例可以在创建时近似实时地监视成本。 实施例可以独立于底层复杂事件处理系统和底层云平台。 实施例可以优化基于云的系统的工作计划，以最小化最终用户的成本，匹配底层云平台的成本模型。

公开(公告)号：US09002823B2

公开(公告)日：2015-04-07

申请号：US13536698

申请日：2012-06-28

申请人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

发明人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

IPC分类号： G06F7/00 ,  G06F17/00 ,  G06F17/30

CPC分类号： G06F17/30103 ,  G06F17/30454 ,  G06F17/30516

摘要： Systems and methods according to embodiments provide elasticity for complex event processing (CEP) systems. Embodiments may comprise at least the following three components: (1) incremental query optimization, (2) operator placement, and (3) cost explanation. Incremental query optimization allows avoiding simultaneous computation of identical results by performing operator-level query reuse and subsumption. Using automatic operator placement, a centralized CEP engine can be transformed into a distributed one by dynamically distributing and adjusting the execution according to unpredictable changes in data and query load. Cost explanation functionality can provide end users with near real-time insight into the monetary cost of the whole system, down to operator level granularity. Combination of these components allows a CEP system to be scaled up and down.

摘要翻译： 根据实施例的系统和方法为复杂事件处理（CEP）系统提供弹性。 实施例可以至少包括以下三个部分：（1）增量查询优化，（2）操作者放置，和（3）成本说明。 增量查询优化允许通过执行运营商级查询重用和包含来避免同时计算相同的结果。 使用自动操作员放置，通过根据不可预测的数据和查询负载变化动态分配和调整执行，可以将集中式CEP引擎转换为分布式CEP引擎。 成本解释功能可以为终端用户提供近乎实时的整个系统的货币成本的洞察力，直到操作员级别的粒度。 这些组件的组合允许CEP系统被放大和缩小。

公开(公告)号：US20140006384A1

公开(公告)日：2014-01-02

申请号：US13536698

申请日：2012-06-28

申请人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

发明人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

IPC分类号： G06F17/30

CPC分类号： G06F17/30103 ,  G06F17/30454 ,  G06F17/30516

摘要： Systems and methods according to embodiments provide elasticity for complex event processing (CEP) systems. Embodiments may comprise at least the following three components: (1) incremental query optimization, (2) operator placement, and (3) cost explanation. Incremental query optimization allows avoiding simultaneous computation of identical results by performing operator-level query reuse and subsumption. Using automatic operator placement, a centralized CEP engine can be transformed into a distributed one by dynamically distributing and adjusting the execution according to unpredictable changes in data and query load. Cost explanation functionality can provide end users with near real-time insight into the monetary cost of the whole system, down to operator level granularity. Combination of these components allows a CEP system to be scaled up and down.

摘要翻译： 根据实施例的系统和方法为复杂事件处理（CEP）系统提供弹性。 实施例可以至少包括以下三个部分：（1）增量查询优化，（2）操作者放置，和（3）成本说明。 增量查询优化允许通过执行运营商级查询重用和包含来避免同时计算相同的结果。 使用自动操作员放置，通过根据不可预测的数据和查询负载变化动态分配和调整执行，可以将集中式CEP引擎转换为分布式CEP引擎。 成本解释功能可以为终端用户提供近乎实时的整个系统的货币成本的洞察力，直到操作员级别的粒度。 这些组件的组合允许CEP系统被放大和缩小。

公开(公告)号：US20130346390A1

公开(公告)日：2013-12-26

申请号：US13529681

申请日：2012-06-21

申请人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

发明人： Zbigniew Jerzak ,  Thomas Heinze ,  Yuanzhen Ji

IPC分类号： G06F17/30

CPC分类号： G06F17/30103 ,  G06F17/30454 ,  G06F17/30516

摘要： A cost monitoring system can monitor a cost of queries executing in a complex event processing system, running on top of a pay-as-you-go cloud infrastructure. Certain embodiments may employ a generic, cloud-platform independent cost model, multi-query optimization, cost calculation, and/or operator placement techniques, in order to monitor and explain query cost down to an operator level. Certain embodiments may monitor costs in near real-time, as they are created. Embodiments may function independent of an underlying complex event processing system and the underlying cloud platform. Embodiments can optimize a work plan of the cloud-based system so as to minimize cost for the end user, matching the cost model of the underlying cloud platform.

摘要翻译： 成本监控系统可以监视在复杂事件处理系统中执行的查询的成本，并在付费即付云基础架构上运行。 某些实施例可以采用通用的，独立于云平台的成本模型，多查询优化，成本计算和/或操作员放置技术，以便监视和解释查询成本降低到操作者级别。 某些实施例可以在创建时近似实时地监视成本。 实施例可以独立于底层复杂事件处理系统和底层云平台。 实施例可以优化基于云的系统的工作计划，以最小化最终用户的成本，匹配底层云平台的成本模型。

公开(公告)号：US09007208B2

公开(公告)日：2015-04-14

申请号：US13915787

申请日：2013-06-12

申请人： Zbigniew Jerzak ,  Yuanzhen Ji

发明人： Zbigniew Jerzak ,  Yuanzhen Ji

IPC分类号： G08B21/00 ,  G08B21/18

CPC分类号： G08B21/182 ,  G05B19/048 ,  G05B23/0221 ,  G05B2219/21062

摘要： Data is received that comprises at least one data stream derived from each of a plurality of sensors that each characterize one or more attributes of equipment components. Thereafter, using the received data and a density-based clustering algorithm that produces micro-clusters for each pair of sensors, correlated sensors having component correlations above a pre-defined threshold are identified. It can then be determined that data from two or more correlated sensors triggers at least one alert event. Subsequently, data is provided that characterizes the at least one alert event. Related apparatus, systems, techniques and articles are also described.

摘要翻译： 收到的数据包括从多个传感器中的每一个导出的每个表征设备部件的一个或多个属性的至少一个数据流。 此后，使用接收的数据和基于密度的聚类算法，为每对传感器产生微簇，识别具有高于预定义阈值的分量相关性的相关传感器。 然后可以确定来自两个或更多个相关传感器的数据触发至少一个警报事件。 随后，提供表征至少一个警报事件的数据。 还描述了相关设备，系统，技术和物品。

公开(公告)号：US20140368340A1

公开(公告)日：2014-12-18

申请号：US13915787

申请日：2013-06-12

申请人： Zbigniew Jerzak ,  Yuanzhen Ji

发明人： Zbigniew Jerzak ,  Yuanzhen Ji

IPC分类号： G08B21/18

CPC分类号： G08B21/182 ,  G05B19/048 ,  G05B23/0221 ,  G05B2219/21062

摘要： Data is received that comprises at least one data stream derived from each of a plurality of sensors that each characterize one or more attributes of equipment components. Thereafter, using the received data and a density-based clustering algorithm that produces micro-clusters for each pair of sensors, correlated sensors having component correlations above a pre-defined threshold are identified. It can then be determined that data from two or more correlated sensors triggers at least one alert event. Subsequently, data is provided that characterizes the at least one alert event. Related apparatus, systems, techniques and articles are also described.

摘要翻译： 收到的数据包括从多个传感器中的每一个导出的每个表征设备部件的一个或多个属性的至少一个数据流。 此后，使用接收的数据和基于密度的聚类算法，为每对传感器产生微簇，识别具有高于预定义阈值的分量相关性的相关传感器。 然后可以确定来自两个或更多个相关传感器的数据触发至少一个警报事件。 随后，提供表征至少一个警报事件的数据。 还描述了相关设备，系统，技术和物品。

公开(公告)号：US09424150B2

公开(公告)日：2016-08-23

申请号：US13311596

申请日：2011-12-06

申请人： Zbigniew Jerzak ,  Thomas Heinze

发明人： Zbigniew Jerzak ,  Thomas Heinze

IPC分类号： G06F11/20 ,  G06F17/30

CPC分类号： G06F11/2035 ,  G06F17/30477

摘要： Various embodiments of systems and methods for a fault tolerance based query execution are described herein. Queries are received from users, the queries including operators. A multi-query optimization is performed on the operators included in the queries to obtain a query plan. A fault tolerance degree is determined for the operators included in the query plan. Based on the fault tolerance degree of the operators, nodes are assigned to the operators included in the query plan. The assigned nodes execute the operators included in the query plan to execute the queries. In one aspect, the nodes simultaneously execute the operators included in the query plan.

摘要翻译： 本文描述了用于基于容错的查询执行的系统和方法的各种实施例。 来自用户的查询，包括运算符在内的查询。 对查询中包含的运算符执行多查询优化，以获取查询计划。 确定查询计划中包含的运营商的容错度。 根据运营商的容错度，将节点分配给查询计划中包含的运营商。 分配的节点执行查询计划中包含的运算符以执行查询。 在一个方面，节点同时执行查询计划中包括的运算符。

公开(公告)号：US09405854B2

公开(公告)日：2016-08-02

申请号：US14203248

申请日：2014-03-10

申请人： Zbigniew Jerzak ,  Ibrahim Ahmed ,  Palaniappan Gandhi

发明人： Zbigniew Jerzak ,  Ibrahim Ahmed ,  Palaniappan Gandhi

IPC分类号： G06F17/30

CPC分类号： G06F17/30958 ,  G06F17/30433 ,  G06F17/30516

摘要： In an example embodiment, event stream processing is performed by first parsing an input query into a directed acyclic graph (DAG) including a plurality of operator nodes. Then a grouping of one or more of the operator nodes is created. One or more partitions are created, either by the user or automatically, in the DAG by forming one or more duplicates of the grouping. A splitter node is created in the DAG, the splitter node splits data from one or more event streams and distributes it among the grouping and the duplicates of the grouping. Then, the input query is resolved by processing data from one or more event streams using the DAG.

摘要翻译： 在示例实施例中，通过首先将输入查询解析成包括多个运算符节点的有向非循环图（DAG）来执行事件流处理。 然后创建一个或多个运算符节点的分组。 一个或多个分区由用户或自动地在DAG中通过形成分组的一个或多个重复来创建。 在DAG中创建分离器节点，分离器节点从一个或多个事件流分割数据，并将其分配到分组和分组的重复之间。 然后，通过使用DAG处理来自一个或多个事件流的数据来解析输入查询。

公开(公告)号：US20150347528A1

公开(公告)日：2015-12-03

申请号：US14289421

申请日：2014-05-28

申请人： Uwe Jugel ,  Zbigniew Jerzak ,  Eric Peukert

发明人： Uwe Jugel ,  Zbigniew Jerzak ,  Eric Peukert

IPC分类号： G06F17/30

CPC分类号： G06F17/30554 ,  G06F17/30448 ,  G06F17/30451

摘要： A query rewriter associated with a database management system or visualization client rewrites a database query based on properties, characteristics, etc. of the visualization to be rendered by the visualization client. For example, the query rewriter receives an initial query and one or more visualization parameters (such as width, height and/or type of visualization) for the visualization client. The query rewriter rewrites the initial query based on the visualization parameter(s), so as to produce a data-reducing query, and then outputs the data-reducing query for execution. The query rewriter can selectively rewrite the initial query depending on size of query results of the initial query. In some example implementations, the query rewriting models a process of rasterization of geometric primitives by the visualization client, so as to facilitate error-free visualization. In many cases, the query rewriter significantly reduces the volume of query results while facilitating fast interaction with the visualization.

摘要翻译： 与数据库管理系统或可视化客户端相关联的查询重写器基于由可视化客户端呈现的可视化的属性，特性等来重写数据库查询。 例如，查询重写器接收可视化客户端的初始查询和一个或多个可视化参数（例如，可视化的宽度，高度和/或类型）。 查询重写器基于可视化参数重写初始查询，以产生数据减少查询，然后输出数据减少查询以执行。 查询重写器可以根据初始查询的查询结果的大小有选择地重写初始查询。 在一些示例实现中，查询重写通过可视化客户端模拟几何图元的光栅化过程，以便于无错误的可视化。 在许多情况下，查询重写器显着减少查询结果的数量，同时有助于与可视化的快速交互。

公开(公告)号：US20150169786A1

公开(公告)日：2015-06-18

申请号：US14203248

申请日：2014-03-10

申请人： Zbigniew Jerzak ,  IBRAHIM AHMED ,  PALANIAPPAN GANDHI

发明人： Zbigniew Jerzak ,  IBRAHIM AHMED ,  PALANIAPPAN GANDHI

IPC分类号： G06F17/30

CPC分类号： G06F17/30958 ,  G06F17/30433 ,  G06F17/30516

摘要： In an example embodiment, event stream processing is performed by first parsing an input query into a directed acyclic graph (DAG) including a plurality of operator nodes. Then a grouping of one or more of the operator nodes is created. One or more partitions are created, either by the user or automatically, in the DAG by forming one or more duplicates of the grouping. A splitter node is created in the DAG, the splitter node splits data from one or more event streams and distributes it among the grouping and the duplicates of the grouping. Then, the input query is resolved by processing data from one or more event streams using the DAG.

摘要翻译： 在示例实施例中，通过首先将输入查询解析成包括多个运算符节点的有向非循环图（DAG）来执行事件流处理。 然后创建一个或多个运算符节点的分组。 一个或多个分区由用户或自动地在DAG中通过形成分组的一个或多个重复来创建。 在DAG中创建分离器节点，分离器节点从一个或多个事件流分割数据，并将其分配到分组和分组的重复之间。 然后，通过使用DAG处理来自一个或多个事件流的数据来解析输入查询。