Abstract: New data points are added to a streaming window of data points and existing data points are removed from the window over time. Each data point has a value for each of one or more dimensions. Each time a given new data point is added to the window or a given existing data point is removed from the window, one or more outlier detection data structures are updated. Each outlier detection data structure encompasses the data points within the streaming window for a corresponding dimension. The outlier detection data structures are used to detect outlier data points within the window over selected one or more dimensions.

Abstract: In a method for estimating a loaded execution runtime of database query, a database query for a database is received for a loaded database. Load information for the database is received. A prediction of query runtime tree is accessed. The prediction of query runtime tree is built from historical query information regarding historical database queries previously executed on the database. The prediction of query runtime tree includes a classifier function adjusted for an historical impact of loading of the database. The database query is processed through the prediction of query runtime tree, such that a loaded execution runtime of the database query is estimated based upon information pertaining to historical database queries similar to the database query.

Abstract: Sensor data is received from one or more sensors. The sensor data is organized within a hierarchy. The sensor data is organized within a hierarchy that is non-dyadic. A processor of a computing device generates a discrete wavelet transform, based on the sensor data and based on the hierarchy of the sensor data, to compress the sensor data. The sensor data, as has been compressed via generation of the discrete wavelet transform, is processed.

Abstract: In a method for estimating a static execution time of a database query, a prediction of query runtime tree is built from historical query information. A database query is received. The prediction of query runtime tree is used to estimate the static execution time of the database query.

Abstract: One embodiment is a method that builds a model of multi-dimensional sequence data in real-time with cuboids that aggregate the multi-dimensional sequence data over both patterns and dimensions. The model provides search results for a query.

Abstract: A method of processing a stream of raw data from a plurality of distributed data producing devices includes reducing the raw data to a plurality of representative synopsis coefficients, organizing the synopsis coefficients into a data structure with at least three dimensions, including a time window dimension and an accuracy dimension. Responsive to a detected anomaly in the data structure, at least one of a predetermined autonomous action and an action directed by a user is performed.

Abstract: A system and method for creating a CD Tree for data having unknown classes are provided. Such a method can include dividing training data into a plurality of subsets of node training data at a plurality of nodes arranged in a hierarchical arrangement, wherein the node training data has a range. Furthermore, dividing node training data at each node can include, ordering the node training data, generating a plurality of separation points and a plurality of pairs of bins from the node training data, wherein each pair of bins includes a first bin and a second bin with a separation point being located between the first bin and the second bin, and classifying the node training data into either the first bin or the second bin for each of the separation points, wherein the classifying is based on a data classifier.

Abstract: A mixed workload scheduler and operating method efficiently handle diverse queries ranging from short less-intensive queries to long resource-intensive queries. A scheduler is configured for scheduling mixed workloads and comprises an analyzer and a schedule controller. The analyzer detects execution time and wait time of a plurality of queries and balances average stretch and maximum stretch of scheduled queries wherein query stretch is defined as a ratio of a sum of wait time and execution time to execution time of a query. The schedule controller modifies scheduling of queries according to service level differentiation.

Abstract: A query scheduler orders queries in a queue. Each query is executed based on its position in the queue. When a new query is received, the new query is inserted in the queue. A position in the queue for inserting the new query is determined based on a stretch metric for each query in the queue.

Abstract: A mixed workload management system and associated operating method modify a shortest job first (SJF) by service levels. The workload management system comprises a scheduler configured for scheduling mixed workloads. The scheduler comprises an analyzer that determines query execution time, assigns scheduling priority to a query in order inverse to the query execution time, weights the assigned scheduling priority by service level of the query, and sorts a list of queries in order of weighted scheduling priority. A schedule controller selects a query for execution from head of the sorted list of queries.

Abstract: A workload management system and operating method are configured for query stream execution using priority gradient programming. The workload management system comprises a database system that executes queries at a priority gradient wherein no more than a predetermined number of queries execute at a particular priority, and a scheduler that schedules queries for execution on the database system and restricts the queries to a number that consumes less than total system memory.

Abstract: Processing batch database workload while avoiding overload. A method for efficiently processing a database workload in a computer system comprises receiving the workload, which comprises a batch of queries directed toward the database. Each query within the batch of queries is assigned a priority. Resources of the computer system are assigned in accordance with the priority. The batch of queries is executed in unison within the computer system in accordance with the priority of each query thereby resolving a conflict within the batch of queries for the resources of the computer system, hence efficiently processing the database workload and avoiding overload of the computer system.

Abstract: Described herein is a workload manager for managing a workload in a database that includes: an admission controller operating to divide the workload into a plurality of batches, with each batch having at least one workload process to be performed in the database, and each batch having a memory requirement based on the available memory for processing workloads in the database; a scheduler operating to assign a unique priority to each of the at least one workload process in each of the plurality of batches, the unique priority provides an order in which each workload process is executed in the database; and an execution manager operating to execute the at least one workload process in each of the plurality of batches in accordance with the unique priority assigned to each workload process.

Abstract: In a method for estimating a loaded execution runtime of database query, a database query for a database is received for a loaded database. Load information for the database is received. A prediction of query runtime tree is accessed. The prediction of query runtime tree is built from historical query information regarding historical database queries previously executed on the database. The prediction of query runtime tree includes a classifier function adjusted for an historical impact of loading of the database. The database query is processed through the prediction of query runtime tree, such that a loaded execution runtime of the database query is estimated based upon information pertaining to historical database queries similar to the database query.

Abstract: In a method for estimating a static execution time of a database query, a prediction of query runtime tree is built from historical query information. A database query is received. The prediction of query runtime tree is used to estimate the static execution time of the database query.

Abstract: A hardware/software system for analyzing memory dumps. The system collects data structures in a memory dump into logical tables, one logical table per selected type of data structure. The logical tables are generated by use of extraction logic for extracting data in data structures in the memory dump. The extraction logic is used in conjunction with a template library that contains data structure definitions for various types of data structures. The extraction logic, together with the template library, make possible populating logical tables with the contents of data structures found in the memory dump. Each row in a logical table is dedicated to one data structure of the selected type. Collecting data structures into logical tables makes available the power of a standard database management system for operating on the logical tables to determine the cause of a crash of a hardware/software system for which the memory dump was taken.