Abstract: The disclosed embodiments relate to a system for analyzing telemetry data. During operation, the system obtains telemetry data gathered from sensors during operation of a monitored system. Next, the system applies a univariate model to the telemetry data to identify an operational phase for the monitored system, wherein the univariate model analyzes an individual signal in the telemetry data without reference to other signals in the telemetry data. The system then selects a phase-specific multivariate model based on the identified operational phase, wherein the phase-specific multivariate model was previously trained based on telemetry data gathered while the system was operating in the identified operational phase. Finally, the system uses the phase-specific multivariate model to monitor the telemetry data to detect incipient anomalies associated with the operation of the monitored system.

Abstract: The disclosed embodiments relate to a system that caches time-series data in a time-series database system. During operation, the system receives the time-series data, wherein the time-series data comprises a series of observations obtained from sensor readings for each signal in a set of signals. Next, the system performs a multivariate memory vectorization (MMV) operation on the time-series data, which selects a subset of observations in the time-series data that represents an underlying structure of the time-series data for individual and multivariate signals that comprise the time-series data. The system then performs a geometric compression aging (GAC) operation on the selected subset of time-series data. While subsequently processing a query involving the time-series data, the system: caches the selected subset of the time-series data in an in-memory database cache in the time-series database system; and accesses the selected subset of the time-series data from the in-memory database cache.

Abstract: The disclosed embodiments provide a system that proactively resilvers a disk array when a disk drive in the array is determined to have an elevated risk of failure. The system receives time-series signals associated with the disk array during operation of the disk array. Next, the system analyzes the time-series signals to identify at-risk disk drives that have an elevated risk of failure. If one or more disk drives are identified as being at-risk, the system performs a proactive resilvering operation on the disk array using a background process while the disk array continues to operate using the at-risk disk drives.

Abstract: The disclosed embodiments relate to a system that certifies provenance of time-series data in a time-series database. During operation, the system retrieves time-series data from the time-series database, wherein the time-series data comprises a sequence of observations comprising sensor readings for each signal in a set of signals. The system also retrieves multivariate state estimation technique (MSET) estimates, which were computed for the time-series data, from the time-series database. Next, the system performs a reverse MSET computation to produce reconstituted time-series data from the MSET estimates. The system then compares the reconstituted time-series data with the time-series data. If the reconstituted time-series data matches the original time-series data, the system certifies provenance for the time-series data.

Abstract: First, the system obtains time-series sensor data. Next, the system identifies missing values in the time-series sensor data, and fills in the missing values through interpolation. The system then divides the time-series sensor data into a training set and an estimation set. Next, the system trains an inferential model on the training set, and uses the inferential model to replace interpolated values in the estimation set with inferential estimates. If there exist interpolated values in the training set, the system switches the training and estimation sets. The system trains a new inferential model on the new training set, and uses the new inferential model to replace interpolated values in the new estimation set with inferential estimates. The system then switches back the training and estimation sets. Finally, the system combines the training and estimation sets to produce preprocessed time-series sensor data, wherein missing values are filled in with imputed values.

Abstract: Techniques related to an in-memory key-value store for a multi-model database are disclosed. In an embodiment, a relational database may be maintained on persistent storage. The relational database may be managed by a database server and may include a database table. The database table may be stored in a persistent format. Key-value records may be generated within volatile memory accessible to the database server by converting data in the database table to a key-value format. The key-value format may be different from and independent of the persistent format. A database statement referencing the database table may be executed based on determining whether to access one or more key-value records in the volatile memory or to access the data in the database table. In response to determining to access the one or more key-value records, the database server may access the one or more key-value records in the volatile memory.

Abstract: The disclosed embodiments provide a system that proactively resilvers a disk array when a disk drive in the array is determined to have an elevated risk of failure. The system receives time-series signals associated with the disk array during operation of the disk array. Next, the system analyzes the time-series signals to identify at-risk disk drives that have an elevated risk of failure. If one or more disk drives are identified as being at-risk, the system performs a proactive resilvering operation on the disk array using a background process while the disk array continues to operate using the at-risk disk drives.

Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.

Abstract: The disclosed embodiments relate to a system that caches time-series data in a time-series database system. During operation, the system receives the time-series data, wherein the time-series data comprises a series of observations obtained from sensor readings for each signal in a set of signals. Next, the system performs a multivariate memory vectorization (MMV) operation on the time-series data, which selects a subset of observations in the time-series data that represents an underlying structure of the time-series data for individual and multivariate signals that comprise the time-series data. The system then performs a geometric compression aging (GAC) operation on the selected subset of time-series data. While subsequently processing a query involving the time-series data, the system: caches the selected subset of the time-series data in an in-memory database cache in the time-series database system; and accesses the selected subset of the time-series data from the in-memory database cache.

Abstract: Techniques related to an in-memory key-value store for a multi-model database are disclosed. In an embodiment, a relational database may be maintained on persistent storage. The relational database may be managed by a database server and may include a database table. The database table may be stored in a persistent format. Key-value records may be generated within volatile memory accessible to the database server by converting data in the database table to a key-value format. The key-value format may be different from and independent of the persistent format. A database statement referencing the database table may be executed based on determining whether to access one or more key-value records in the volatile memory or to access the data in the database table. In response to determining to access the one or more key-value records, the database server may access the one or more key-value records in the volatile memory.

Abstract: The disclosed embodiments relate to a system that certifies provenance of time-series data in a time-series database. During operation, the system retrieves time-series data from the time-series database, wherein the time-series data comprises a sequence of observations comprising sensor readings for each signal in a set of signals. The system also retrieves multivariate state estimation technique (MSET) estimates, which were computed for the time-series data, from the time-series database. Next, the system performs a reverse MSET computation to produce reconstituted time-series data from the MSET estimates. The system then compares the reconstituted time-series data with the time-series data. If the reconstituted time-series data matches the original time-series data, the system certifies provenance for the time-series data.

Abstract: The disclosed embodiments relate to a system for analyzing telemetry data. During operation, the system obtains telemetry data gathered from sensors during operation of a monitored system. Next, the system applies a univariate model to the telemetry data to identify an operational phase for the monitored system, wherein the univariate model analyzes an individual signal in the telemetry data without reference to other signals in the telemetry data. The system then selects a phase-specific multivariate model based on the identified operational phase, wherein the phase-specific multivariate model was previously trained based on telemetry data gathered while the system was operating in the identified operational phase. Finally, the system uses the phase-specific multivariate model to monitor the telemetry data to detect incipient anomalies associated with the operation of the monitored system.

Abstract: Techniques related to an in-memory key-value store for a multi-model database are disclosed. In an embodiment, a relational database may be maintained on persistent storage. The relational database may be managed by a database server and may include a database table. The database table may be stored in a persistent format. Key-value records may be generated within volatile memory accessible to the database server by converting data in the database table to a key-value format. The key-value format may be different from and independent of the persistent format. A database statement referencing the database table may be executed based on determining whether to access one or more key-value records in the volatile memory or to access the data in the database table. In response to determining to access the one or more key-value records, the database server may access the one or more key-value records in the volatile memory.

Abstract: A method and apparatus queries a table in a database where the table includes at least one column declared to be sparse. A binary large object may be used to store the sparse column data. The object includes a column-id and column-value pair for each non-null value. To answer a query with a constraint on a sparse column, the object is searched for one or more column ids to obtain the column values. Rows whose column values match a constraint are returned. In another embodiment, an internal table is used. Each tuple in the internal table has a column id and a value array indexed by an ordinal row number. To answer a query with a constraint on a sparse column, the column value in the internal table is found and matched against the constraint. If the match is successful, the index of the column value in the internal table is returned.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.

Abstract: Systems, methods, and other embodiments associated with event processing are described. In one embodiment, a method includes detecting an event. The example method may also include analyzing the event to extract information about the user and processing a subsequent event in accordance with the extracted information about the user.

Abstract: A method and apparatus queries a table in a database where the table includes at least one column declared to be sparse. A binary large object may be used to store the sparse column data. The object includes a column-id and column-value pair for each non-null value. To answer a query with a constraint on a sparse column, the object is searched for one or more column ids to obtain the column values. Rows whose column values match a constraint are returned. In another embodiment, an internal table is used. Each tuple in the internal table has a column id and a value array indexed by an ordinal row number. To answer a query with a constraint on a sparse column, the column value in the internal table is found and matched against the constraint. If the match is successful, the index of the column value in the internal table is returned.

Abstract: Systems, methods, and other embodiments associated with bi-temporal user profiling are described. An event is detected that occurs at a valid event time. In response to the event, a repository is accessed that stores data describing one or more user profiles that include a profile record valid time period specifying a time at which the given profile record is valid. A prior user profile record is retrieved that has a profile record valid time period that overlaps with the valid event time. An updated user profile record is created based, at least in part, on the event. The updated user profile record is saved with the valid event time demarcating the start of a profile valid time period. The prior user profile with the valid event time demarcating the end of the profile record valid time period is also saved for subsequent processing.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.