Abstract: The present disclosure involves systems, software, and computer implemented methods for providing quality-driven processing of out-of-order data streams based on a configurable quality metric. In one example, the method includes identifying a configurable quality metric defined by a user or application for executing continuous queries over a sliding window, the metric specifying a user requirement for the quality of the continuous query result. A quality threshold metric is calculated based on the configurable quality metric, the configurable quality metric associated with a size of an adaptation buffer for incoming tuples of the query. In response to receiving a signal indicating a new query result, a sliding window prior to a current time is identified. A runtime quality metric associated with the incoming tuples for the sliding window is measured, and the adaptation buffer is modified based on a difference between the quality threshold metric and the measured runtime quality metric.

Abstract: The present disclosure involves systems, software, and computer implemented methods for optimizing continuous queries for hybrid execution over a stream processing engine and an in-memory database. In one example, a method may include identifying a continuous query executed at a stream processing engine, the continuous query including a plurality of operators. An optimal plan for execution of the identified continuous query at the stream processing engine is determined. For each of the plurality of operators in the determined optimal plan, an optimized placement decision for executing a particular operator in the stream processing engine or at a database system is determined. An optimized continuous query is generated from the identified continuous query based on the determined optimal placement decisions for each of the plurality of operators in the determined optimal plan. The optimized continuous query is then executed at the stream processing engine and the database system.

Abstract: The present disclosure involves systems, software, and computer implemented methods for providing quality-driven processing of out-of-order data streams based on a configurable quality metric. In one example, the method includes identifying a configurable quality metric defined by a user or application for executing continuous queries over a sliding window, the metric specifying a user requirement for the quality of the continuous query result. A quality threshold metric is calculated based on the configurable quality metric, the configurable quality metric associated with a size of an adaptation buffer for incoming tuples of the query. In response to receiving a signal indicating a new query result, a sliding window prior to a current time is identified. A runtime quality metric associated with the incoming tuples for the sliding window is measured, and the adaptation buffer is modified based on a difference between the quality threshold metric and the measured runtime quality metric.

Abstract: The present disclosure involves systems, software, and computer implemented methods for optimizing continuous queries for hybrid execution over a stream processing engine and an in-memory database. In one example, a method may include identifying a continuous query executed at a stream processing engine, the continuous query including a plurality of operators. An optimal plan for execution of the identified continuous query at the stream processing engine is determined. For each of the plurality of operators in the determined optimal plan, an optimized placement decision for executing a particular operator in the stream processing engine or at a database system is determined. An optimized continuous query is generated from the identified continuous query based on the determined optimal placement decisions for each of the plurality of operators in the determined optimal plan. The optimized continuous query is then executed at the stream processing engine and the database system.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.