Abstract: Systems and methods may include receiving performance data of components in a system. The performance data may include data for parameters for each of the components. The systems and methods may include determining aggregate data for each group of similar components of the components. The aggregate data for each group of similar components may include a group characteristic for each of the parameters. The systems and methods may include, for each group of similar components, determining whether the data for each of the parameters for each component is consistent with the group characteristic for the respective parameter. The systems and methods may include, for each component of the respective group determining that the component is anomalous in response to determining that the data for a parameter for the component is not consistent with the group characteristic for the parameter.

Abstract: Systems and methods may include receiving real-time data about a real component operating in a real-world environment. The systems and methods may further include generating a virtual representation of the real component based on the real-time data about the real component and historical data associated with the real component. In addition, the systems and methods may include receiving injected data from a lab. The injected data may provide data about a lab component operating in the lab. The systems and methods may also include simulating, in a virtual environment, a real-time interaction in the real-world environment between the real component and the lab component using the virtual representation of the real component and the injected data. Moreover, the systems and methods may include determining a real-time performance characteristic of at least one of the lab component and the real component based on the simulated real-time interaction in the real-world environment.

Abstract: Various implementations of the invention relate to converting SQL databases into improved databases that are optimized for querying. Such improved databases may include NoSQL databases that may have been optimized for querying.

Abstract: The system and method of the present disclosure relates to technology for reducing the amount of data stored in a storage system by processing subsets of data stored in data sources using advanced analytics. The process generally includes extracting data from data sources for analysis by ranking the data, marking the data, identifying pattern changes in the data, comparing pattern changes in the data and purging and/or masking the data for storage. The system also includes databases for storing and defining rules, patterns, policies and classification data to be applied to the data from the data sources and analytics to apply the rules, patterns, policies and classification information on the data. As a result, the data stored in the data sources is reduced, and processing efficiency is increased.

Abstract: A computer implemented method includes monitoring blocks of data on a storage device that are changing as the computer operates. On detecting a computer shut down event, a copy of changes to the monitored blocks are saved. Upon startup of the computer, a backup of the changed blocks of data is performed.

Abstract: Systems and methods may include receiving data of a physical system. The systems and methods may include determining whether an anomalous event has occurred based on the data. The systems and methods may include, in response to determining that the anomalous event has occurred, capturing data of the physical system and of an environment. The systems and methods may include generating a virtual representation of the physical system and the environment based on the captured data. The systems and methods may include repeatedly simulating data of the virtual representation by varying parameters of the captured data. The systems and methods may include determining which parameters are a root cause of the anomalous event based on the simulated performance data. The systems and methods may include determining particular values of the parameters that are likely the root cause of the anomalous event to use as a trigger for a proactive process.

Abstract: A system and method obtain a set of data and identify successive subsets of data within the set of data. A boundary identifying hash is calculated on a subset of data and compared with a boundary indicating value. If the calculated boundary identifying hash matches the boundary indicating value, a natural boundary is identified in the set of data.

Abstract: Data from applications is published using a web feed document such as according to the Really Simple Syndication (RSS) protocol. The applications can be information technology (IT) services, such as an Application Performance Management program and a Service Desk program, which are used by an organization. The data is received at an Application Programming Interface (API) and stored in a database in a format which is usable by the web feed document. For example, the data can provide a parameter name, parameter label, severity and user type which are associated with placeholder elements in the web feed document. The user type may be used to restrict the publishing of certain parameters to certain users based on their business roles in an organization. The feed server may provide additional details in response to the user selecting a hyperlink in the web feed document.

Abstract: Systems and methods may include receiving first data of components, which may represent performance characteristics of the components at a first time. The systems and methods may include performing a first cluster analysis of the first data to identify clusters of the components with similar characteristics. The systems and methods may include receiving second data of the components, which may represent performance characteristics of the components at a second time. The systems and methods may include performing a second cluster analysis of the second data to identify clusters of the components with similar characteristics. The systems and methods may include determining whether a component transitioned from a cluster identified in the first cluster analysis to a different cluster identified in the second cluster analysis. The systems and methods may include determining that an anomaly occurred in response to determining that the component transitioned from the cluster to the different cluster.

Abstract: A system and method obtain a set of data and identify successive subsets of data within the set of data. A boundary identifying hash is calculated on a subset of data and compared with a boundary indicating value. If the calculated boundary identifying hash matches the boundary indicating value, a natural boundary is identified in the set of data.

Abstract: A computer implemented method and system obtains current signatures of data chunks and performs a proximity search of a library of previous signatures as a function of the likely location of corresponding data chunks. A full search of the library of previous signatures for those current signatures not found in the proximity search is also performed.

Abstract: A device or system includes a plurality of storage resources each associated with a respective performance class, each being associated with selected performance characteristics such as IOPS, bandwidth, etc. The device or system includes a compute instance having access to allocated storage resources, the allocated storage devices including one or more storage resources. The device or system also includes an optimization component adapted to obtain information relating to utilization by the compute instance component of the allocated storage resources, determine that a change to the allocated storage resources is necessary, based on the information, cause data to be migrated from a first storage resource associated with a first performance class to a second storage resource associated with a second storage class, and cause a removal from the allocated storage resources of the first storage resource and an addition to the allocated storage resources of the second storage resource.

Abstract: A system and method obtain a set of data and identify successive subsets of data within the set of data. A boundary identifying hash is calculated on a subset of data and compared with a boundary indicating value. If the calculated boundary identifying hash matches the boundary indicating value, a natural boundary is identified in the set of data.

Abstract: Systems and methods may include receiving real-time data from real components operating in a real-world environment. The systems and methods may further include generating a virtual representation of the real components based on the real-time data received from the real components and historical data associated with the real components. In addition, the systems and methods may include determining whether the real-time data from a component of the real components indicates an anomaly. The systems and methods also may include, in response to determining that the real-time data from the component indicates the anomaly, determining whether the anomaly indicated by the real-time data from the component is consistent with the virtual representation of the real components.

Abstract: Systems and methods may include receiving first and second motion data about first and second objects, respectively. The systems and methods may include applying an asymmetric correlation rule (ACR) to the first and second motion data. The ACR may define a baseline state of motion for the first object and a correlation relationship between the first and second objects. The systems and methods may include determining whether the first object is in the baseline state and, in response to determining that the first object is not in the baseline state, determining whether a first motion vector of the first object is sufficiently correlated with a second motion vector of the second object. The systems and methods may include, in response to determining both that the first object is not in the baseline state and that the first and second motion vectors are not sufficiently correlated, generating an anomalous behavior notification.

Abstract: A device or system includes a plurality of storage resources each associated with a respective performance class, each being associated with selected performance characteristics such as IOPS, bandwidth, etc. The device or system includes a compute instance having access to allocated storage resources, the allocated storage devices including one or more storage resources. The device or system also includes an optimization component adapted to obtain information relating to utilization by the compute instance component of the allocated storage resources, determine that a change to the allocated storage resources is necessary, based on the information, cause data to be migrated from a first storage resource associated with a first performance class to a second storage resource associated with a second storage class, and cause a removal from the allocated storage resources of the first storage resource and an addition to the allocated storage resources of the second storage resource.

Abstract: A computer implemented method includes creating a child disk for a machine. A configuration of a backed up disk drive is derived from a storage container that includes a block level backup copy of the disk drive. The child disk is populated with selected data from blocks of data corresponding to selected files desired to be used on the machine. Drivers may be injected and registries modified to start with booting of the virtual.

Abstract: Systems and methods may include receiving first and second motion data about first and second objects, respectively. The systems and methods may include applying an asymmetric correlation rule (ACR) to the first and second motion data. The ACR may define a baseline state of motion for the first object and a correlation relationship between the first and second objects. The systems and methods may include determining whether the first object is in the baseline state and, in response to determining that the first object is not in the baseline state, determining whether a first motion vector of the first object is sufficiently correlated with a second motion vector of the second object. The systems and methods may include, in response to determining both that the first object is not in the baseline state and that the first and second motion vectors are not sufficiently correlated, generating an anomalous behavior notification.

Abstract: Systems and methods may include receiving performance data of components in a system. The performance data may include data for parameters for each of the components. The systems and methods may include determining aggregate data for each group of similar components of the components. The aggregate data for each group of similar components may include a group characteristic for each of the parameters. The systems and methods may include, for each group of similar components, determining whether the data for each of the parameters for each component is consistent with the group characteristic for the respective parameter. The systems and methods may include, for each component of the respective group determining that the component is anomalous in response to determining that the data for a parameter for the component is not consistent with the group characteristic for the parameter.

Abstract: Systems and methods may include receiving data of a physical system. The systems and methods may include determining whether an anomalous event has occurred based on the data. The systems and methods may include, in response to determining that the anomalous event has occurred, capturing data of the physical system and of an environment. The systems and methods may include generating a virtual representation of the physical system and the environment based on the captured data. The systems and methods may include repeatedly simulating data of the virtual representation by varying parameters of the captured data. The systems and methods may include determining which parameters are a root cause of the anomalous event based on the simulated performance data. The systems and methods may include determining particular values of the parameters that are likely the root cause of the anomalous event to use as a trigger for a proactive process.