Oracle International Corporation

Oracel International Corporation

Oracle Lnternational Corporation

Oracle Corporation

Amit Ganesh

Vineet Marwah

Jesse Kamp

Bipul Sinha

Vivekanandhan Raja

Shasank Kisan Chavan

Jonathan D Klein

Roger MacNicol

Niloy Mukherjee

Allison Holloway

Gary C Ngai

Sanket Hase

Sujatha Muthulingam

Michael J Gleeson

Vikram Kapoor

Dustin R Garvey

Manosiz Bhattacharyya

Uri Shaft

Anindya Chandra Patthak

Kam Shergill

Sampanna Shahaji Salunke

Sumathi Gopalakrishnan

Dheeraj Pandey

Srinivas Vemuri

Teck Hua Lee

Atrayee Mullick

Krishna Kunchithapadam

Juan R Loaiza

Timothy Mark Frazier

Sachin Kulkarni

Ajit Mylavarapu

Sukhada Pendse

Hariharan Lakshmanan

Shriram Krishnan

Prasad Ravuri

Wei Zhang

Tirthankar Lahiri

Aurosish Mishra

Prashant Gaharwar

Wanli Yang

Shuang Su

Roger J Bamford

Vasudha Krishnaswamy

Lawrence J Ellison

Kenneth R Jacobs

Archna Kalra Johnson

Liang Guo

Lik Wong

Lakshminarayanan Luxi Chidambaran

Dieter Gawlick

Dmitry Mikhailovich Potapov

Joy Forsythe

Joshua Gould

Bharath Aleti

Long Yang

Zhen Hua Liu

Neil MacNaughton

Ramesh Kumar

Fulu Li

Victor Chen

Chinmayi Krishnappa

Roger Dermot Macnicol

Benoit Dageville

Daniel ManHung Wong

Andrew Witkowski

Namit Jain

Boris Klots

Kothanda Umamageswaran

Debashis Saha

Ananth Raghavan

Nimar Singh Arora

Michael Zoll

Krishnan Meiyyappan

William Bridge

Vikram Joshi

Ashish Thusoo

J William Lee

Shrikanth Shankar

Hui Joe Chang

Alok Pareek

Wei Ming Hu

Arvind Nithrakashyap

Ekrem Soylemez

Brent Arthur Enck

Thuvan Hoang

Viral Shah

Michael John De Groot

Steven Wertheimer

Xia Hua

Richard Yu Gu

Chi Ching Chui

Adrian Tsz Him Ng

Gaurav Bhalotia

Adrian Ng

Phumpong Watanaprakornkul

Vishwanath Karra

Francisco Sanchez

Rosanne Park Toohey

Neil Goodman

Alexander Hwang

A method, apparatus, and system for tracking row and object database activity into block level heatmaps is provided. Database activity including reads, writes, and creates can be tracked by a database management system at the finest possible level of granularity, or the row and object level. To efficiently record the tracked database activity, a two-part structure is described for writing the activity into heatmaps. A hierarchical in-memory component may use a dynamically allocated sparse pool of bitmap blocks. Periodically, the in-memory component is persisted to a stored representation component, sharable with multiple database instances, which may include consolidated last access times and/or a history of heatmap snapshots to reflect access over time. The heatmaps may then be externalized to database users and applications to provide and support a variety of features.

Tracking row and object database activity into block level heatmaps

Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Artificial intelligence driven configuration management

A method, apparatus, and system for policy driven data placement and information lifecycle management in a database management system are provided. A user or database application can specify declarative policies that define the movement and transformation of stored database objects. The policies are associated with a database object and may also be inherited. A policy defines, for a database object, an archiving action to be taken, a scope, and a condition before the archiving action is triggered. Archiving actions may include compression, data movement, table clustering, and other actions to place the database object into an appropriate storage tier for a lifecycle phase of the database object. Conditions based on access statistics can be specified at the row level and may use segment or block level heatmaps. Policy evaluation occurs periodically in the background, with actions queued as tasks for a task scheduler.

Policy driven data placement and information lifecycle management

A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a 'compression unit' using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Storing compression units in relational tables

A query result set is an example of a type of body of data whose content is defined by a query. Modifications to a database can change the result of a query and therefore what the content of such a body of data, such as a result set, should be. Active Queries is an approach that enables a database server to detect database changes that alter the result of a query registered for a client and to notify a client of those changes. The notifications may be sent to, for example, a cache manager responsible for maintaining coherency of a result set cache generated by the query. The notification notifies the cache manager of when and/or what changes are needed to maintain the coherency of the cache.

Active queries filter extraction

Row locking is performed at the row level of granularity for database data stored in columnar form. Row level locking entails use of a lock vector that is stored in a compression unit in a data block, the compression unit storing rows in columnar-major format. On an as needed basis, the lock vector is expanded to identify more transactions affecting the rows in the compression unit.

Row level locking for columnar data

Techniques are described for materializing computations in memory. In an embodiment, responsive to a database server instance receiving a query, the database server instance identifies a set of computations for evaluation during execution of the query. Responsive to identifying the set of computations, the database server instance evaluates at least one computation in the set of computations to obtain a first set of computation results for a first computation in the set of computations. After evaluating the at least one computation, the database server instance stores, within an in-memory unit, the first set of computation results. The database server also stores mapping data that maps a set of metadata values associated with the first computation to the first set of computation results.

Materializing internal computations in-memory to improve query performance

Techniques are described for characterizing and summarizing seasonal patterns detected within a time series. According to an embodiment, a set of time series data is analyzed to identify a plurality of instances of a season, where each instance corresponds to a respective sub-period within the season. A first set of instances from the plurality of instances are associated with a particular class of seasonal pattern. After classifying the first set of instances, a second set of instances may remain unclassified or otherwise may not be associated with the particular class of seasonal pattern. Based on the first and second set of instances, a summary may be generated that identifies one or more stretches of time that are associated with the particular class of seasonal pattern. The one or more stretches of time may span at least one sub-period corresponding to at least one instance in the second set of instances.

System for detecting and characterizing seasons

A method, apparatus, and system for automated information lifecycle management using low access patterns in a database management system are provided. A user or the database can store policy data that defines an archiving action when meeting an activity-level condition on one or more database objects. The archiving actions may include compression, data movement, and other actions to place the database object in an appropriate storage tier for a lifecycle phase of the database object. The activity-level condition may specify the database object meeting a low access pattern, optionally for a minimum time period. Various criteria including access statistics for the database object and cost characteristics of current and target compression levels or storage tiers may be considered to determine the meeting of the activity-level condition. The policies may be evaluated on an adjustable periodic basis and may utilize a task scheduler for minimal performance impact.

Automated information lifecycle management using low access patterns

Techniques are provided for automatic verification of query results obtained from data formatted different from row-major format. Techniques are also provided for automatic triage of query results obtained from data formatted different from row-major format. In an embodiment, the query results may be obtained from querying data formatted in column-major format. In an embodiment, when the query results are determined to be incorrect, a cause of the error in the query results can be automatically identified.

Automatic verification and triage of query results

Techniques for activity tracking, data classification, and in-database archiving are described. Activity tracking refers to techniques that collect statistics related to user access patterns, such as the frequency or recency with which users access particular database elements. The statistics gathered through activity tracking can be supplied to data classification techniques to automatically classify the database elements or to assist users with manually classifying the database elements. Then, once the database elements have been classified, in-database archiving techniques can be employed to move database elements to different storage tiers based on the classifications. However, although the techniques related to activity tracking, data classification, and in-database archiving may be used together as described above; each technique may also be practiced separately.

Techniques for activity tracking, data classification, and in database archiving

Techniques are described for generating predictive alerts. In one or more embodiments, a seasonal model is generated, the seasonal model representing one or more seasonal patterns within a first set of time-series data, the first set of time-series data comprising data points from a first range of time. A trend-based model is also generated to represent trending patterns within a second set of time-series data comprising data points from a second range of time that is different than the first range of time. A set of forecasted values is generated based on the seasonal model and the trend-based model. Responsive to determining that a set of alerting thresholds has been satisfied based on the set of forecasted values, an alert is generated.

Multiscale method for predictive alerting

Growing community of inventors

San Jose, CA, United States of America

Amit Ganesh