Oracle International Corporation

Ashish Mittal

Kenneth Khiaw Hong Eng

Steve Simon Joseph Fernandez

Samar Lotia

Victor Belyaev

Saugata Chowdhury

Alextair Dominico Mascarenhas

Alvin Raj

Gabby Rubin

John Fuller

Ananth Venkata

Srinivas Vemuri

Venugopal Surendran

Nikhil Surve

Roger Bolsius

Paolo Fragapane

James Adam Steadman

Suresh Mali

Kenneth Eng

Vijayakumar Ranganathan

Mike Durran

Praful Hebbar

Geemon Kadambalil Baby

Mahesh Jayashankar

Mahadevan Rajagopalan

Yi Ge

David Wong

In accordance with an embodiment, described herein is a system and method for providing query acceleration with a computing environment such as, for example, a business intelligence environment, database, data warehouse, or other type of environment that supports data analytics. A middle layer is provided as a long-term table data storage format; and one more acceleration formats, or acceleration tables, can be periodically regenerated from the middle layer, wherein a determination can be made as to whether an accelerated table exists for a dataset table, and if so, then the accelerated table is used to process the query.

System and method for query acceleration for use with data analytics environments

A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.

Uploading external files and associating them with existing data models

In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can receive a data set that includes data points having data values and attributes, and a target attribute, and use a machine learning process to automatically determine one or more other attributes as driving factors for the target attribute, based on, for example, the use of a decision tree and a comparison of information gain, Gini, or other indices associated with attributes in the data set. Information describing facts associated with the data set can be graphically displayed at a user interface, as visualizations, and used as a starting point for further analysis of the data set.

System and method for data visualization using machine learning and automatic insight of facts associated with a set of data

In accordance with various embodiments, described herein are systems and methods for use of computer-implemented machine learning to automatically determine insights of facts, segments, outliers, or other information associated with a set of data, for use in generating visualizations of the data. In accordance with an embodiment, the system can use a machine learning process to automatically determine one or more outliers or findings within the data, based on, for example, determining a plurality of combinations representing pairs of attribute dimensions within a data set, from which a general explanation or pattern can be determined for one or more attributes, and then comparing particular values for attributes, with the determined pattern for those attributes. Information describing such outliers or findings can be graphically displayed at a user interface, as text, graphs, charts, or other types of visualizations, and used as a starting point for further analysis of the data set.

System and method for data visualization using machine learning and automatic insight of outliers associated with a set of data

A query optimization system is described that, at runtime, optimizes the execution pipeline generated for a query. Based upon communications between nodes in the execution pipeline, the execution pipeline generated for a query is optimized by modifying the execution pipeline to create a modified execution pipeline. The modified execution pipeline is then executed to execute the query and results obtained for the query. The changes or modifications made to an execution pipeline may include changing the capabilities (e.g., changes to inputs and/or outputs of a node, changing the task(s) or function(s) performed by the node) of one or more nodes within the execution pipeline. The changes may include changing the position of one or more nodes within a directed acyclic graph representing the execution pipeline.

Dynamic self-reconfiguration of nodes in a processing pipeline

Optimized techniques are disclosed for sorting variable-length records using an optimized amount of memory while maintaining good locality of references. The amount of memory required for sorting the variable length records is optimized by reusing some of the memory used for storing the variable length records being sorted. Pairs of input runs storing variable length records may be merged into a merged run that contains the records in a sorted order by incrementally scanning, sorting, and copying the records from the two input runs being merged into memory pages of the merged run. When all the records of a memory page of an input run have been processed or copied to the merged run, that memory page can be emptied and released to a cache of empty memory pages. Memory pages available from the cache of empty memory pages can then be used for generating the merged run.

Optimized sorting of variable-length records

Innovative techniques are disclosed for performing a combined sort and aggregation operation involving eagerly performing aggregation while sorting. The techniques described herein enable an aggregation and a group-by operation to be performed using an amount of system memory that is far less than the system memory required to store all the data records being processed while minimizing the need to spill data to disk. This combined sort and aggregation operation has better performance than conventional techniques since system memory is used more efficiently. In certain embodiments, a combined sort and aggregation operation is disclosed that enables the efficient sorting and aggregation of data records when the desired aggregation function is composable (such as SUM, COUNT, MIN and MAX aggregate operations).

Combined sort and aggregation

Techniques are described that enable the dynamic and automatic life cycle management of database schemas. Temporary database schemas are dynamically and automatically allocated in response to database schema requests. Allocated schemas are also automatically reclaimed upon expiration. A dynamic schema allocation system is provided that is configured to receive database schema requests from one or more clients and dynamically and automatically, substantially free of any manual intervention, serve database schemas in response to the requests. The database schemas may be allocated from multiple database instances available to the dynamic schema allocation system. Attributes specified in a database schema request may be used to find a matching database instance and a schema is allocated for the request from the matching database instance.

Dynamic lifecycle management of temporary storage schemas

Techniques for dynamically changing the amount of memory that is allocated to and used by processes (e.g., sorter processes) executed concurrently by a data processing system in a manner that is adaptive to the overall memory usage of the system. The ability to dynamically change the amount of memory that can be allocated to and used by individual processes increases and optimizes the overall memory utilization of the data processing system, which in turn results in the operations (e.g., sorting) performed by the processes to be performed in a more efficient manner. A memory manager is provided that is responsible for periodically collecting memory related information for the data processing system and for the processes (e.g., sorter processes) executed by the data processing system, and based on the collected information, for periodically and dynamically causing the memory used by the processes to be changed during the lifetime of the processes.

Dynamic memory management techniques

Growing community of inventors

Foster City, CA, United States of America

Ashish Mittal