Abstract: Methods and systems for optimizing the configuration and parameters of a workflow using an evolutionary approach augmented with intelligent learning capabilities using a Big Data infrastructure. In an embodiment, a Big Data infrastructure receives workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria from a client computer, and then runs multiple instances of a workflow based on the pool of initial configuration parameters resulting in corresponding output results. The process includes storing the workflow input parameters and the corresponding output results, modeling the relationship between changes in the workflow input parameters and the corresponding output results, determining that optimal output results have been achieved, and then transmitting the optimal output and the input-output variable relationships results to the client computer.

Abstract: A system for storing time series data includes an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. The time series database distributes storage of the data blocks among multiple data nodes. A query layer receives queries and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result.

Abstract: Time series data is received from a time series data repository and the time series data includes a plurality of sub-portions. The sub-portions of data are first sorted in chronological order to appear as if the data is being generated in real time and are then sent for analysis. The received sorted time series data is then analyzed to detect one or more predefined events or patterns in the data. When the predefined events or patterns are detected in the time series data by the analysis, a user or downstream analysis component is informed that the one or more predefined events or patterns have been found.

Abstract: A system for predicting events by associating time series data with other types of non-time series data can include a processor configured to receive a data stream including time series data transmitted from a sensor configured to measure an operating parameter of a component being monitored. The processor identifies sequences of interest in the time series data having predictive value. The processor compares the real-time data stream to a set of known historical patterns that act as effective leading indicators of different alarms and events. The processor extracts any identified sequences of interest from the time series data as an extracted event. The processor quantifies the relationship between the data of the extracted event and the known historical pattern by calculating a confidence level to denote a probability of occurrence of the event by comparing how closely the new time series data matches the data patterns associated with known events.

Abstract: A system for predicting events by associating time series data with other types of non-time series data can include a processor configured to receive a data stream including time series data transmitted from a sensor configured to measure an operating parameter of a component being monitored. The processor identifies sequences of interest in the time series data having predictive value. The processor compares the real-time data stream to a set of pre-existing event data that act as effective leading indicators of different alarms and events. The processor extracts any identified sequences of interest from the time series data as an extracted event. The processor quantifies the relationship between the data of the extracted event and the known historical pattern by calculating a confidence level to denote a probability of occurrence of the event by comparing how closely the new time series data matches the data patterns associated with known events.

Abstract: A system and method for searching for and finding data across industrial time-series data is disclosed. A computer system receives a search query from a client system. The computer system accesses a database including a plurality of stored time-series data sets. For each stored time-series data set, the computer system determines whether the stored time-series data set includes one or more sections that match the received search query. In accordance with a determination that two or more of stored time-series data sets include at least one section that matches the received search query, the computer system determines whether the matching sections in each stored time-series data set have overlapping time periods. In accordance with a determination that the matching sections in each time-series data set have overlapping time periods, the computer system identifies a particular event that occurred during the overlapping time periods.

Abstract: A method, system, and non-transitory computer-readable medium, the method including receiving notifications from a plurality of agents, the notifications being associated with the plurality of agents sensing aspects of an environment; determining, based at least in part on the received notifications from the plurality of agents, a situational model of the environment from the notifications; determining a status of the environment based on the situational model; and reporting the status of the environment to at least one of the plurality of agents.

Abstract: Embodiments allow blocking and featurization of time-series data gathered from at least one sensor. The input time-series data is divided into blocks with common attributes (features) according to feature models that describe patterns in the data. The blocks may be overlapping or non-overlapping. The resultant feature blocks are annotated with feature information and semantic meaning. The feature blocks can be indexed to facilitate semantic search of the data. Feature blocks may be further analyzed to create semantic blocks that incorporate semantic meaning and features for multiple feature blocks, sensors and/or related time-series data. The semantic blocks can also be indexed to facilitate semantic search of the data.

Abstract: A first attribute is associated with a first data storage device and a second attribute is associated with a second data storage device. The first data storage device stores first time series data and the second data storage device stores second time series data. In parallel, the first attribute is applied to the first time series data and the second attribute is applied to the second time series data. The application is effective to cause an alteration of one or more of the first time series data or the second time series data. The alteration may be a thinning or reduction of the time series data.

Abstract: A data storage policy is determined. Time series data is received and a score for the time series data is determined. The score prioritizes the time series data according to a likelihood the time series data will be needed for future use. Based upon the data storage policy and the score, the time series data is stored at one or more data storage devices. The score is updated over time to reflect changing priorities regarding the use of the data.

Abstract: Time series data is identified that is related to a predetermined characteristic and the predetermined characteristic being at least one of an identity of a sensor or a time range. Based upon the identified time series data, the time series data is moved to selected ones of the plurality of separate data storage devices, and the movement is temporary for processing purposes. In parallel, queries are performed on the time series data on each of the selected ones of the plurality of separate data storage devices to obtain a plurality of results. The plurality of results are aggregated.

Abstract: Characterization information related to time series data is obtained. A data storage rule is automatically determined based upon the characterization information. The rule defines at least one of a location for the storage of the time series data and a format for storage of the time series data. The rule is applied to the time series data and the time series data is stored according to the rule.

Abstract: A user query or analytic user is received. The user query or analytic requires one or more portions of time series data from a plurality of transient memories. The time series data is linked together across the transient memories devices. A location of one or more portions of the time series data is identified at the plurality of transient memories. One or more portions of the time series data is automatically retrieved from the transient memories.

Abstract: A system includes a processor and a non-transitory computer-readable medium. The non-transitory computer-readable medium comprises instructions executable by the processor to cause the system to perform a method. The method comprises receiving a first job to execute and executing the first job. A plurality of data associated with the first job is determined The plurality of data comprises data associated with (i) a second job executed immediately prior to the first job, (ii) a third job executed immediately after the first job, (iii) a determination of whether the first job failed or executed successfully and (iv) a type of data associated with the first job. The determined plurality of data is stored.

Abstract: Methods and systems for optimizing the configuration and parameters of a workflow using an evolutionary approach augmented with intelligent learning capabilities using a Big Data infrastructure. In an embodiment, a Big Data infrastructure receives workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria from a client computer, and then runs multiple instances of a workflow based on the pool of initial configuration parameters resulting in corresponding output results. The process includes storing the workflow input parameters and the corresponding output results, modeling the relationship between changes in the workflow input parameters and the corresponding output results, determining that optimal output results have been achieved, and then transmitting the optimal output and the input-output variable relationships results to the client computer.

Abstract: A system for storing time series data includes an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. The time series database distributes storage of the data blocks among multiple data nodes. A query layer receives queries and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result.

Abstract: A method for performing queries on a distributed time series data storage system is presented. The time series data storage system has a time series database that stores data blocks containing time stamped data across a plurality of computing devices. The system also includes an index database that stores an index associated with the time stamped data in each data block. The method includes the steps of sending a query, requesting indices, returning the indices, preparing a sub-query, forwarding the sub-query to an evaluator, evaluating the sub-query, performing a logical operation on each sub-query's result, receiving the sub-results at an output handler, and combining the sub-results.

Abstract: A system for storing time series data includes an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. The time series database distributes storage of the data blocks among multiple data nodes. A query layer receives queries and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result.

Abstract: A service for storing time series data provides a data pipe for receiving time series data, a query pipe for making requests to the service, and a result pipe for receiving output from the service. Data sent to the query pipe is processed by an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. A query layer receives queries from the query pipe and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result which is sent out by the results pipe.

Abstract: A plurality of analytics in a cloud-based environment is accessed. Each of the plurality of analytics performs an operation on time series data. Within the cloud-based environment, a selected one or more of the plurality of analytics is chosen. A set of time series data is uploaded to the cloud-based environment and the selected one of the plurality of analytics is optimized on that set of time series data.