Abstract: A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop.

Abstract: A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.

Abstract: A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.

Abstract: A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.

Abstract: The formulation of transformations on one or more input event streams to generation one or more output event streams. Accordingly, the transformations may be considered to be a query on the original input event stream(s). The event query includes event stream source representations representing an input event stream available in a particular execution context. The event query also includes a transformation module identifying the transformation set to be performed on the input event streams in the execution context. Once the query is properly formed, an execution module may then cause the transformations to be executed upon the designated input event stream(s) to generate output event streams.

Abstract: Methods of managing distributed analytics on device groups are described. In an embodiment, a management service within a distributed analytics system provides an interface to allow a user to define a group of devices based on a property of the devices. When the property of a device in the system satisfies the criterion specified by the user, the device is added to the group and the device may subsequently be removed from the group if the device no longer satisfies the criterion. Once a group has been defined, the management service enables users to specify management operations, such as creating, starting, stopping or deleting queries or management operations relating to other entities of end devices, which are to be implemented on all the devices in the group and the management service propagates the operation to all devices in the group, irrespective of their current connectivity status.

Abstract: A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop.

Abstract: A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop.

Abstract: Methods of managing distributed analytics on device groups are described. In an embodiment, a management service within a distributed analytics system provides an interface to allow a user to define a group of devices based on a property of the devices. When the property of a device in the system satisfies the criterion specified by the user, the device is added to the group and the device may subsequently be removed from the group if the device no longer satisfies the criterion. Once a group has been defined, the management service enables users to specify management operations, such as creating, starting, stopping or deleting queries or management operations relating to other entities of end devices, which are to be implemented on all the devices in the group and the management service propagates the operation to all devices in the group, irrespective of their current connectivity status.

Abstract: Methods of managing distributed analytics on device groups are described. In an embodiment, a management service within a distributed analytics system provides an interface to allow a user to define a group of devices based on a property of the devices. When the property of a device in the system satisfies the criterion specified by the user, the device is added to the group and the device may subsequently be removed from the group if the device no longer satisfies the criterion. Once a group has been defined, the management service enables users to specify management operations, such as creating, starting, stopping or deleting queries or management operations relating to other entities of end devices, which are to be implemented on all the devices in the group and the management service propagates the operation to all devices in the group, irrespective of their current connectivity status.

Abstract: A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.

Abstract: A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.

Abstract: Examining time series sequences representing performance counters from executing programs can provide significant clues about potential malfunctions, busy periods in terms of traffic on networks, intensive processing cycles and so on. An unsupervised anomaly detector can detect anomalies for any time series. A combination of known techniques from statistics, signal processing and machine learning can be used to identify outliers on unsupervised data, and to capture anomalies like edge detection, spike detection, and pattern error anomalies. Boolean and probabilistic results concerning whether an anomaly was detected can be provided.

Abstract: The formulation of transformations on one or more input event streams to generation one or more output event streams. Accordingly, the transformations may be considered to be a query on the original input event stream(s). The event query includes event stream source representations representing an input event stream available in a particular execution context. The event query also includes a transformation module identifying the transformation set to be performed on the input event streams in the execution context. Once the query is properly formed, an execution module may then cause the transformations to be executed upon the designated input event stream(s) to generate output event streams.

Abstract: Examining time series sequences representing performance counters from executing programs can provide significant clues about potential malfunctions, busy periods in terms of traffic on networks, intensive processing cycles and so on. An unsupervised anomaly detector can detect anomalies for any time series. A combination of known techniques from statistics, signal processing and machine learning can be used to identify outliers on unsupervised data, and to capture anomalies like edge detection, spike detection, and pattern error anomalies. Boolean and probabilistic results concerning whether an anomaly was detected can be provided.

Abstract: A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.

Abstract: A monitor-mine-manage cycle is described, for example, for managing a data center, a manufacturing process, an engineering process or other processes. In various example, the following steps are performed as a continuous automated loop: receiving raw events from an observed system; monitoring the raw events and transforming them into complex events; mining the complex events and reasoning on results; making a set of proposed actions based on the mining; and managing the observed system by applying one or more of the proposed actions to the system. In various examples, the continuous automated loop proceeds while raw events are continuously received from the observed system and monitored. In some examples an application programming interface is described comprising programming statements which allow a user to implement a monitor-mine-manage loop.

Abstract: Methods of managing distributed analytics on device groups are described. In an embodiment, a management service within a distributed analytics system provides an interface to allow a user to define a group of devices based on a property of the devices. When the property of a device in the system satisfies the criterion specified by the user, the device is added to the group and the device may subsequently be removed from the group if the device no longer satisfies the criterion. Once a group has been defined, the management service enables users to specify management operations, such as creating, starting, stopping or deleting queries or management operations relating to other entities of end devices, which are to be implemented on all the devices in the group and the management service propagates the operation to all devices in the group, irrespective of their current connectivity status.

Abstract: Cognitive radio adaptation is employed in WSNs to reduce effects of RF interference. Communication channel quality is assessed locally at each node. Using beacons to propagate channel quality and node related information among the nodes of the network, channel adaptation decision is made either centrally by identifying a channel that is optimum for a majority of nodes through a tree-structure dissemination (Majority Voting Scheme) or in a distributed manner by identifying a channel where maximum interference for any node is less than maximum interference on the other channels (Veto Voting Scheme). If two channels have the same level of maximal interference, the channel with a lesser number is chosen. Channel quality assessment may be optimized based on expected interference type and/or statistical methods.

Abstract: Cognitive radio adaptation is employed in WSNs to reduce effects of RF interference. Communication channel quality is assessed locally at each node. Using beacons to propagate channel quality and node related information among the nodes of the network, channel adaptation decision is made either centrally by identifying a channel that is optimum for a majority of nodes through a tree-structure dissemination (Majority Voting Scheme) or in a distributed manner by identifying a channel where maximum interference for any node is less than maximum interference on the other channels (Veto Voting Scheme). If two channels have the same level of maximal interference, the channel with a lesser number is chosen. Channel quality assessment may be optimized based on expected interference type and/or statistical methods.