Abstract: A query having a Union All view is identified. A logical join between Union AH view/derived table and other tables is broken down into multiple physical joins. The physical joins are pushed to the branches. Cost-based processing statistics are obtained for the branches. An optimal plan for the joins is selected based on the statistics; representing an optimal query execution for the query. The optimal query execution plan is provided to a database engine for executing the optimal query execution plan against a data warehouse.

Abstract: A system may include at least one processor. The at least one processor may receive data from a plurality of independent data sources. The data from each respective data source is received at a rate determined by the respective data source. The at least one processor may further write the received data to at least one data store at a rate independent of the respective rates at which data from the plurality of independent data sources is received. A method and computer-readable medium are also disclosed.

Abstract: A system may include a server and a data store system. The server may include at least one storage device and at least one processor. The server may execute an application and may store an encrypted password. The data store system may include at least one persistent storage device configured to store a data store. The data store system may further include a plurality of processing nodes configured to operate on the data store. The data store system may receive the encrypted password from the application with one of the plurality of processing nodes and may decrypt the encrypted password with the one of the plurality of processing nodes. The data store system may authenticate the decrypted password with the one of the processing nodes and provide the decrypted password to other processing nodes. Each processing node that has the decrypted password may be accessible to the application to operate on the data store. A method and computer-readable medium may also be implemented.

Abstract: A data store system includes an array of persistent storage devices configured to store a plurality of data store tables. The data store system includes a processor in communication with the storage device. The processor may receive a query comprising an aggregate function and identify structure of an argument of the aggregate function. The subset of data store tables may be associated with the argument. The processor may partially-execute the aggregate function on each data store table in the subset involved in the argument of the aggregate function to create partially-executed results for each data store table of the subset of data store tables. The processor may join the partially-executed results based on join conditions contained in the aggregate function. The processor may complete execution of the aggregate function on the partially-executed results to generate a final result of the aggregate function. A method and computer-readable medium are also disclosed.

Abstract: A data-changing request is received on a first data source system. Metadata for a data source object associated with the request is obtained. The request is executed on a second data source system and results are obtained. A selection between multiple replication processes is made based on the metadata and the results. The selected process is performed on each of the remaining data source systems to synchronize/replicate data changes on the remaining data source systems. In an embodiment, bidirectional and concurrent data connections are permitted to a source object for one data source system and a replicated version of the source object on a different source system. In an embodiment, each data source system includes an agent that establishes an out-of-band connection to a synchronization/replication server, such that synchronization/replication is processed by the server without an affirmative connection being made from the data source systems to the server.

Abstract: A single storage allocation unit of a distributed storage is mapped to multiple different data temperature metric values (metric value), and each metric value calculated and produced by a data temperature generator. Each generator is designed to provide a metric value used to optimize performance of a specific data management feature for the storage subsystem. At any given point in time, the storage allocation unit has multiple up-to-date metric values available for managing the underlying data of the storage allocation unit by storage subsystems and storage management processes. Based on changing performance characteristics associated with the underlying data, a current metric value being used to manage the storage allocation unit can be dynamically switched to a different up-to-date metric value. This automatically causes the underlying storage subsystems/storage management processes to optimize arrangement and organization of the underlying data for a desired data management feature.

Abstract: A method may include receiving a stored procedure associated with data stored in a plurality of data stores. The stored procedure may include a plurality of executable statements. The method may further include identifying a first executable statement of the plurality of executable statements to be executed by the processor and a second executable statement of the plurality of executable statements that is executable by at least one of a plurality of other processors. The other processors each may have access to only a respective one of the plurality of copies of the data. The method may further include executing the first executable statement. A system and computer-readable medium may also be implemented.

Abstract: Techniques for processing relational data with a user-defined function (UDF) are provided. Relational input data being requested by the UDF, from within a relational database system, is intercepted and normalized. The UDF is called with the normalized input data and as the UDF produces output data in response to the normalized input data that output data is captured and normalized. In an embodiment, the normalized output data is used to dynamically update a data model within the relational database for the input data.

Abstract: Improved techniques for reducing the size of data of the multidimensional data are disclosed. The improved techniques are highly suitable for processing large-scale data and various large-scale data applications (e.g., large-scale Data Mining (DM), large-scale data analysis (LSDA) in computing systems (e.g., Data Information Systems, Database Systems). Redundancy-reduced data can be provided (RRDS) thereby providing data that can be used more efficiently by various applications, especially, large-scale data applications. At least one assumption about the distribution of a multi-dimensional data set (MDDS) and its corresponding set of responses (Y) can be made in order to reduce the multi-dimensional data set (MDDS). It should be noted that the assumption can be made after effectively combining multi-dimensional data set (MDDS) and its corresponding set of responses (Y) so that the set of responses (Y) can also be in considered in effectively reducing the size of the multi-dimensional data set (MDDS).

Abstract: A database includes a Value List Compression (VLC) predicate evaluator. A table identified in a query that is being processed is identified as having compressed data values. The predicate evaluator compares a query predicate of the query against actual decompressed values noted in a dictionary for the table and the predicate evaluator maintains a bitmap for selective ones of the actual values that satisfy the query predicate. The matched bitmap positions are processed against an index maintained in the table for the actual values to provide selective decompressed table entries as results for the query.

Abstract: A User-Defined Function (UDF) provided by a user and accessed through a user query is identified. A wrapper for the UDF is generated. The wrapper represents a customized instance of the UDF. The wrapper, during execution, selectively calls the UDF provided by the user. Any query that references the UDF is rewritten to process the wrapper instead of the UDF.

Abstract: A system and method for determining optimal query plans within distributed database system employing table operators for performing analytic operations for storing and processing multi-structured data. The optimization of a query plan proceeds through a collaborative exchange between a database system optimizer, or planner, and a table operator, wherein multiple communications between said optimizer and said table operator are conducted to exchange input and output information relevant to optimizing execution of the query and table operator.

Abstract: Data can be stored based on one or more indications and one or more other storage criteria. The indications can effectively indicate or identify a storage level for storing data in a multi-storage system. The indications, however, need not be the only basis for storing the data object in a multi-storage system as one or more other storage criteria can also be considered. As a result, the indication can be used to effectively influence data storage but other storage criteria can be used as well to prevent adverse effects caused by undue influence and to ensure the overall efficiency of the system. Also, the one or more other storage criteria can be evaluated or revaluated on a continual basis.

Abstract: One or a soft correlation of a database can be adjusted (e.g., modified, replaced, overwritten) for use with respect to one or more record(s) of the database associated with the soft correlation, by considering at least one or more violations of the soft correlations in the one or more of records database records associated with the soft correlation. In addition, an adjusted soft correlation can be stored and used for optimizations of database queries pertaining to one or more records associated with the adjusted soft correlation. Typically, the adjusted soft correlation is adjusted by at least considering the violations of an original soft correlation in the one or more records relating to the database queries.

Abstract: A method for performing row qualification in database table retrieval and join operations. This method, referred to as bulk qualification, evaluates conditions on multiple rows in a database table at the same time, providing more efficient utilization of memory bandwidth and CPU throughput.

Abstract: A relational database management system (RDBMS) accepts a workload comprised of one or more queries against a relational database. The RDBMS evolves a default cost profile into a plurality of cost profiles using fixed or dynamic evolution, wherein each of the cost profiles captures one or more cost parameters for the workload. The cost profiles are represented by a multi-dimensional matrix that has one or more dimensions, and each of the dimensions represents one of the cost parameters. The RDBMS dynamically determines which of the cost profiles is an optimal cost profile for the workload by mapping the cost profiles to the workload using a random walk scoring algorithm or a biased walk scoring algorithm that searches the multi-dimensional matrix to identify the optimal cost profile. The RDBMS selects and performs one or more query execution plans for the workload based on the optimal cost profile for the workload.

Abstract: A data store system may include an array of persistent storage devices configured to store a plurality of data store tables. The data store system may further include a processor in communication with the storage device. The processor may receive a query containing a non-equality join condition on a first column from a first data store table and a second column on a second data store table. The processor may generate a bitmap based on the join condition. The bitmap indicate respective matches between the first column and second column in accordance with the non-equality join condition. The bitmap may also be used each time the non-equality join condition is present in another received query. A method and computer-readable medium may also be implemented.

Abstract: A system and method for determining optimal query plans within a distributed processing system. The system includes a query optimizer for receiving a query and generating a query plan for execution by the distributed processing system; a repository including statistics related to prior executed query plans; a plan execution engine for receiving and executing the query plan, collecting statistics corresponding to the query plan, and storing the statistics and optimizer usage metadata corresponding to the query plan and prior executed query plans in the repository. The system further includes a statistics advisory tool for mining the metadata to generate a collection of histogram statistics and storing said histogram statistics in the repository. When generating a query plan, the query optimizer searches the statistics repository for statistics, including histogram statistics, useful to the generation of the current query plan and uses selected statistics from the repository to generate the query plan.

Abstract: A method of operating a data store system may include identifying a non-responsive processing node from a plurality of processing nodes. The method may further include generating a new registration key in response to identifying the non-responsive processing node. The method may further include providing the new registration key to the other processing nodes of the plurality of processing nodes excluding the identified non-responsive node. Each processing node provided the new registration key may be authorized to access a plurality of storage devices of a storage array in communication with the plurality of processing nodes. A system and computer-readable medium may also be implemented.

Abstract: A computer running a database system receives one or more queries, each query comprised of parallel threads of execution working towards the common goal of completing a user request. These threads are grouped into a schedulable object called a task group. The task groups are placed within a specific multiple tier hierarchy, and database system resources and service level goals (SLGs) allocated to the task groups according to their placement within the hierarchy. The execution of requests/tasks is monitored, and resource allocations temporarily increased to critical requests that are unlikely to meet execution goals (SLGs).