Abstract: Disclosed are a method, information processing system, and computer readable medium for scanning a storage medium table. The method includes retrieving location information associated with at least one other storage medium table scan. A storage medium table scan is started at a location within a storage medium table based on at least a location of the one other storage medium table scan. A weight is assigned to at least one storage medium block based on at least a current scanning location within the storage medium table relative to the location of the one other table scan. The method determines if a distance between the current scanning location and the location of the one other table scan is greater than a first given threshold. A current scanning operation is delayed, in response to the distance being greater than the given threshold, until the distance is below a second given threshold.

Abstract: A method for accelerating range queries using periodic monotonic properties of non-monotonic functions including mapping a base column x to an existing index on a column y that is correlated with column x through a periodic piecewise monotonic function F(x), rewriting an index construction statement to force the existing index on column y to track a periodic piecewise monotonic property by assigning identical values of F(x) to different periods to different ranges to create an annotated index, and rewriting range queries over the annotated index on F(x) by modifying a derived predicate.

Abstract: A computer implemented method, apparatus, and computer usable program code for detecting monotonicity. A determination is made as to whether a function that is assumed to be monotonic will remain monotonic after performing an operation in response to receiving the operation. The operation is allowed to be performed in response to a determination that the function will remain monotonic after performing the operation.

Abstract: A computer implemented method, apparatus, and computer usable program code for deleting rows in a multidimensional clustering database. A multidimensional clustering processor receives a request to delete a set of multidimensionally clustered data. The multidimensional clustering processor determines whether all multidimensionally clustered data is located in a block. Responsive to all multidimensionally clustered data qualifying for deletion, the multidimensional clustering processor deletes all data in the block to delete the set of multidimensionally clustered data.

Abstract: An embodiment of the present invention provides a computer system comprising a first memory and a second memory, where the first memory is substantially faster than second and that performs a method includes steps of: receiving requests for blocks from scan processes; serializing requests for blocks with a request queue; inspecting requests, with a request handler process, R, for blocks and determining whether the requests can be fulfilled from the first memory, if possible; and using an interface for: getting a next data item not yet processed by a calling process, starting a new one-time table access operation, and ending one-time table access operation.

Abstract: A multi-level locking hierarchy for a relational database includes a locking level applied to a multi-dimensionally clustering table, a locking level applied to blocks within the table, and a locking level applied to rows within the blocks. The hierarchy leverages the multi-dimensional clustering of the table data for efficiency and to reduce lock overhead. Data is normally locked in order of coarser to finer granularity to limit deadlock. When data of finer granularity is locked, data of coarser granularity containing the finer granularity data is also locked. Block lock durations may be employed to ensure that a block remains locked if any contained row remains locked. Block level lock attributes may facilitate detection of at least one of a concurrent scan and a row deletion within a block. Detection of the emptying of a block during a scan of the block may bar scan completion in that block.

Abstract: Disclosed is a data processing system implemented method, a data processing system and an article of manufacture for improving execution efficiency of a database workload to be executed against a database. The database includes database tables, and the database workload identifies at least one of the database tables. The data processing system includes an identification module for identifying candidate database tables being identifiable in the database workload, the identified candidate database tables being eligible for organization under a clustering schema, a selection module for selecting the identified candidate tables according to whether execution of the database workload is improved if the selected identified candidate table is organized according to the clustering scheme, and an organization module for organizing the clustering schema of the selected organized identified candidate tables prior to the database workload being execution against the database.

Abstract: Multidimensional clustered tables are provided for efficient processing and management in a relational database management system. A multidimensional clustered table is one whose data is simultaneously clustered along one or more independent dimensions, or clustering keys, and physically organized into blocks or pages on disk. When such a table is created, one can specify one or more keys as dimensions along which to cluster the table's data. Each of the dimensions can consist of one or more columns. Further provided are efficient query processing and maintenance techniques for use in conjunction with multidimensional clustered tables.

Abstract: A method to process a query for information stored in a relational database using indexes which have different granularity and/or composition is provided. The method involves selecting indexes of various granularity and/or composition based on the query, ordering and combining them (if necessary) in order to subset the data from a relational table to be optimally scanned. The ability to combine indexes is very useful to answer ad hoc queries for which no specific index exists. This requirement may be met by using index ANDing and index ORing techniques.

Abstract: Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

Abstract: An information retrieval system and method are provided for minimizing the number of blocks searched in a cell before recording a new record in the table and determining which block can be assigned if a table has space available to store a new record in the case an additional block should be associated with a cell. Dimensions for a table are identified, and at least one block in the table is associated with a dimension value for each dimension, where each block comprises contiguous storage pages. The block can be further associated with a cell; this associated cell has a unique combination of dimension values comprising an dimension value for each of the dimensions. A unique associated bit list for each dimension value for each dimension has a unique corresponding list entry for each block associated with that dimension value, and a unique associated bit list for each cell has a unique corresponding list entry for each block associated with that cell.

Abstract: A multi-level locking hierarchy for a relational database includes a locking level applied to a multi-dimensionally clustering table, a locking level applied to blocks within the table, and a locking level applied to rows within the blocks. The hierarchy leverages the multi-dimensional clustering of the table data for efficiency and to reduce lock overhead. Data is normally locked in order of coarser to finer granularity to limit deadlock. When data of finer granularity is locked, data of coarser granularity containing the finer granularity data is also locked. Block lock durations may be employed to ensure that a block remains locked if any contained row remains locked. Block level lock attributes may facilitate detection of at least one of a concurrent scan and a row deletion within a block. Detection of the emptying of a block during a scan of the block may bar scan completion in that block.

Abstract: Multidimensional clustered tables are provided for efficient processing and management in a relational database management system. A multidimensional clustered table is one whose data is simultaneously clustered along one or more independent dimensions, or clustering keys, and physically organized into blocks or pages on disk. When such a table is created, one can specify one or more keys as dimensions along which to cluster the table's data. Each of the dimensions can consist of one or more columns. Further provided are efficient query processing and maintenance techniques for use in conjunction with multidimensional clustered tables.

Abstract: A method to process a query for information stored in a relational database using indexes which have different granularity and/or composition is provided. The method involves selecting indexes of various granularity and/or composition based on the query, ordering and combining them (if necessary) in order to subset the data from a relational table to be optimally scanned. The ability to combine indexes is very useful to answer ad hoc queries for which no specific index exists. This requirement may be met by using index ANDing and index ORing techniques.