Patents by Inventor Seema Sundara
Seema Sundara has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11907250Abstract: Techniques are described for executing machine learning models trained for specific operators with feature values that are based on the actual execution of a workload set. The machine learning models generate an estimate of benefit gain/cost for executing operations on data portions in the alternative encoding format. Such data potions may be sorted based on the estimated benefit, in an embodiment. Using cost estimation machine learning models for memory space, the data portions with the most benefits that comply with the existing memory space constraints are recommended and/or are automatically encoded into the alternative encoding format.Type: GrantFiled: July 22, 2022Date of Patent: February 20, 2024Assignee: Oracle International CorporationInventors: Urvashi Oswal, Marc Jolles, Onur Kocberber, Seema Sundara, Nipun Agarwal
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Publication number: 20240028605Abstract: Techniques are described for executing machine learning models trained for specific operators with feature values that are based on the actual execution of a workload set. The machine learning models generate an estimate of benefit gain/cost for executing operations on data portions in the alternative encoding format. Such data potions may be sorted based on the estimated benefit, in an embodiment. Using cost estimation machine learning models for memory space, the data portions with the most benefits that comply with the existing memory space constraints are recommended and/or are automatically encoded into the alternative encoding format.Type: ApplicationFiled: July 22, 2022Publication date: January 25, 2024Inventors: URVASHI OSWAL, MARC JOLLES, ONUR KOCBERBER, SEEMA SUNDARA, NIPUN AGARWAL
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Patent number: 11868261Abstract: Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.Type: GrantFiled: July 20, 2021Date of Patent: January 9, 2024Assignee: Oracle International CorporationInventors: Peyman Faizian, Mayur Bency, Onur Kocberber, Seema Sundara, Nipun Agarwal
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Patent number: 11782926Abstract: Embodiments utilize trained query performance machine learning (QP-ML) models to predict an optimal compute node cluster size for a given in-memory workload. The QP-ML models include models that predict query task runtimes at various compute node cardinalities, and models that predict network communication time between nodes of the cluster. Embodiments also utilize an analytical model to predict overlap between predicted task runtimes and predicted network communication times. Based on this data, an optimal cluster size is selected for the workload. Embodiments further utilize trained data capacity machine learning (DC-ML) models to predict a minimum number of compute nodes needed to run a workload. The DC-ML models include models that predict the size of the workload dataset in a target data encoding, models that predict the amount of memory needed to run the queries in the workload, and models that predict the memory needed to accommodate changes to the dataset.Type: GrantFiled: January 12, 2022Date of Patent: October 10, 2023Assignee: Oracle International CorporationInventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Publication number: 20230297573Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automatic data placement recommendations for partitioning data across multiple nodes in a database system. The system is configured to extract workload-specific features of a database workload running at a database system and dataset-specific features of a database running on the database system. The workload-specific features characterize utilization of the database workload. The dataset-specific features characterize how data is organized within the database. The system identifies a plurality of candidate keys for determining how to partition data stored in the database across nodes. Based at least in part on the workload-specific features, the dataset specific features, and the plurality of candidate keys, a set of candidate key combinations for partitioning data is generated.Type: ApplicationFiled: March 21, 2022Publication date: September 21, 2023Inventors: Urvashi Oswal, Jian Wen, Farhan Tauheed, Onur Kocberber, Seema Sundara, Nipun Agarwal
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Patent number: 11567937Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automate database configuration parameter tuning for a database workload. This approach uses machine learning (ML) models to test performance metrics resulting from application of particular database parameters to a database workload, and does not require live trials on the DBMS managing the workload. Specifically, automatic configuration (AC) ML models are trained, using a training corpus that includes information from workloads being run by DBMSs, to predict performance metrics based on workload features and configuration parameter values. The trained AC-ML models predict performance metrics resulting from applying particular configuration parameter values to a given database workload being automatically tuned. Based on correlating changes to configuration parameter values with changes in predicted performance metrics, an optimization algorithm is used to converge to an optimal set of configuration parameters.Type: GrantFiled: May 12, 2021Date of Patent: January 31, 2023Assignee: Oracle International CorporationInventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Publication number: 20230022884Abstract: Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.Type: ApplicationFiled: July 20, 2021Publication date: January 26, 2023Inventors: Peyman Faizian, Mayur Bency, Onur Kocberber, Seema Sundara, Nipun Agarwal
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Patent number: 11423022Abstract: Techniques are described herein for building a framework for declarative query compilation using both rule-based and cost-based approaches for database management. The framework involves constructing and using: a set of rule-based properties tables that contain optimization parameters for both logical and physical optimization, a recursive algorithm to form candidate physical query plans that is based on the rule based tables, and a cost model for estimating the cost of a generated physical query plan that is used with the rule based properties tables to prune inferior query plans.Type: GrantFiled: June 25, 2018Date of Patent: August 23, 2022Assignee: Oracle International CorporationInventors: Jian Wen, Sam Idicula, Nitin Kunal, Farhan Tauheed, Seema Sundara, Nipun Agarwal, Indu Bhagat
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Patent number: 11379456Abstract: Systems and methods for adjusting parameters for a spin-lock implementation of concurrency control are described herein. In an embodiment, a system continuously retrieves, from a resource management system, one or more state values defining a state of the resource management system. Based on the one or more state values, the system determines that the resource management system has reached a steady state and, in response adjusts a plurality of parameters for spin-locking performed by said resource management system to identify optimal values for the plurality of parameters. After adjusting the plurality of parameters, the system detects, based on one or more current state values, a workload change in the resource management system and, in response, readjusts the plurality of parameters for spin-locking performed by said resource management system to identify new optimal values for the parameters.Type: GrantFiled: October 1, 2020Date of Patent: July 5, 2022Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Onur Kocberber, Mayur Bency, Marc Jolles, Seema Sundara, Nipun Agarwal
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Publication number: 20220138199Abstract: Embodiments utilize trained query performance machine learning (QP-ML) models to predict an optimal compute node cluster size for a given in-memory workload. The QP-ML models include models that predict query task runtimes at various compute node cardinalities, and models that predict network communication time between nodes of the cluster. Embodiments also utilize an analytical model to predict overlap between predicted task runtimes and predicted network communication times. Based on this data, an optimal cluster size is selected for the workload. Embodiments further utilize trained data capacity machine learning (DC-ML) models to predict a minimum number of compute nodes needed to run a workload. The DC-ML models include models that predict the size of the workload dataset in a target data encoding, models that predict the amount of memory needed to run the queries in the workload, and models that predict the memory needed to accommodate changes to the dataset.Type: ApplicationFiled: January 12, 2022Publication date: May 5, 2022Inventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Publication number: 20220107933Abstract: Systems and methods for adjusting parameters for a spin-lock implementation of concurrency control are described herein. In an embodiment, a system continuously retrieves, from a resource management system, one or more state values defining a state of the resource management system. Based on the one or more state values, the system determines that the resource management system has reached a steady state and, in response adjusts a plurality of parameters for spin-locking performed by said resource management system to identify optimal values for the plurality of parameters. After adjusting the plurality of parameters, the system detects, based on one or more current state values, a workload change in the resource management system and, in response, readjusts the plurality of parameters for spin-locking performed by said resource management system to identify new optimal values for the parameters.Type: ApplicationFiled: October 1, 2020Publication date: April 7, 2022Inventors: Onur Kocberber, Mayur Bency, Marc Jolles, Seema Sundara, Nipun Agarwal
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Patent number: 11256698Abstract: Embodiments utilize trained query performance machine learning (QP-ML) models to predict an optimal compute node cluster size for a given in-memory workload. The QP-ML models include models that predict query task runtimes at various compute node cardinalities, and models that predict network communication time between nodes of the cluster. Embodiments also utilize an analytical model to predict overlap between predicted task runtimes and predicted network communication times. Based on this data, an optimal cluster size is selected for the workload. Embodiments further utilize trained data capacity machine learning (DC-ML) models to predict a minimum number of compute nodes needed to run a workload. The DC-ML models include models that predict the size of the workload dataset in a target data encoding, models that predict the amount of memory needed to run the queries in the workload, and models that predict the memory needed to accommodate changes to the dataset.Type: GrantFiled: April 11, 2019Date of Patent: February 22, 2022Assignee: Oracle International CorporationInventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Patent number: 11169995Abstract: Techniques related to relational dictionaries are disclosed. In some embodiments, one or more non-transitory storage media store a sequence of instructions which, when executed by one or more computing devices, cause performance of a method. The method involves storing a code dictionary comprising a set of tuples. The code dictionary is a database table defined by a database dictionary and comprises columns that are each defined by the database dictionary. The set of tuples maps a set of codes to a set of tokens. The set of tokens are stored in a column of unencoded database data. The method further involves generating encoded database data based on joining the unencoded database data with the set of tuples. Furthermore, the method involves generating decoding database data based on joining the encoded database data with the set of tuples.Type: GrantFiled: November 21, 2017Date of Patent: November 9, 2021Assignee: Oracle International CorporationInventors: Pit Fender, Seema Sundara, Benjamin Schlegel, Nipun Agarwal
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Publication number: 20210263934Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automate database configuration parameter tuning for a database workload. This approach uses machine learning (ML) models to test performance metrics resulting from application of particular database parameters to a database workload, and does not require live trials on the DBMS managing the workload. Specifically, automatic configuration (AC) ML models are trained, using a training corpus that includes information from workloads being run by DBMSs, to predict performance metrics based on workload features and configuration parameter values. The trained AC-ML models predict performance metrics resulting from applying particular configuration parameter values to a given database workload being automatically tuned. Based on correlating changes to configuration parameter values with changes in predicted performance metrics, an optimization algorithm is used to converge to an optimal set of configuration parameters.Type: ApplicationFiled: May 12, 2021Publication date: August 26, 2021Inventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Patent number: 11061902Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automate database configuration parameter tuning for a database workload. This approach uses machine learning (ML) models to test performance metrics resulting from application of particular database parameters to a database workload, and does not require live trials on the DBMS managing the workload. Specifically, automatic configuration (AC) ML models are trained, using a training corpus that includes information from workloads being run by DBMSs, to predict performance metrics based on workload features and configuration parameter values. The trained AC-ML models predict performance metrics resulting from applying particular configuration parameter values to a given database workload being automatically tuned. Based on correlating changes to configuration parameter values with changes in predicted performance metrics, an optimization algorithm is used to converge to an optimal set of configuration parameters.Type: GrantFiled: March 11, 2019Date of Patent: July 13, 2021Assignee: Oracle International CorporationInventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Patent number: 11023430Abstract: Techniques related to a sparse dictionary tree are disclosed. In some embodiments, computing device(s) execute instructions, which are stored on non-transitory storage media, for performing a method. The method comprises storing an encoding dictionary as a token-ordered tree comprising a first node and a second node, which are adjacent nodes. The token-ordered tree maps ordered tokens to ordered codes. The ordered tokens include a first token and a second token. The ordered codes include a first code and a second code, which are non-consecutive codes. The first node maps the first token to the first code. The second node maps the second token to the second code. The encoding dictionary is updated based on inserting a third node between the first node and the second node. The third node maps a third token to a third code that is greater than the first code and less than the second code.Type: GrantFiled: November 21, 2017Date of Patent: June 1, 2021Assignee: Oracle International CorporationInventors: Georgios Giannikis, Seema Sundara, Sabina Petride, Nipun Agarwal
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Patent number: 10810195Abstract: Techniques related to distributed relational dictionaries are disclosed. In some embodiments, one or more non-transitory storage media store a sequence of instructions which, when executed by one or more computing devices, cause performance of a method. The method involves generating, by a query optimizer at a distributed database system (DDS), a query execution plan (QEP) for generating a code dictionary and a column of encoded database data. The QEP specifies a sequence of operations for generating the code dictionary. The code dictionary is a database table. The method further involves receiving, at the DDS, a column of unencoded database data from a data source that is external to the DDS. The DDS generates the code dictionary according to the QEP. Furthermore, based on joining the column of unencoded database data with the code dictionary, the DDS generates the column of encoded database data according to the QEP.Type: GrantFiled: January 3, 2018Date of Patent: October 20, 2020Assignee: Oracle International CorporationInventors: Anantha Kiran Kandukuri, Seema Sundara, Sam Idicula, Pit Fender, Nitin Kunal, Sabina Petride, Georgios Giannikis, Nipun Agarwal
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Publication number: 20200125568Abstract: Embodiments utilize trained query performance machine learning (QP-ML) models to predict an optimal compute node cluster size for a given in-memory workload. The QP-ML models include models that predict query task runtimes at various compute node cardinalities, and models that predict network communication time between nodes of the cluster. Embodiments also utilize an analytical model to predict overlap between predicted task runtimes and predicted network communication times. Based on this data, an optimal cluster size is selected for the workload. Embodiments further utilize trained data capacity machine learning (DC-ML) models to predict a minimum number of compute nodes needed to run a workload. The DC-ML models include models that predict the size of the workload dataset in a target data encoding, models that predict the amount of memory needed to run the queries in the workload, and models that predict the memory needed to accommodate changes to the dataset.Type: ApplicationFiled: April 11, 2019Publication date: April 23, 2020Inventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Publication number: 20200125545Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automate database configuration parameter tuning for a database workload. This approach uses machine learning (ML) models to test performance metrics resulting from application of particular database parameters to a database workload, and does not require live trials on the DBMS managing the workload. Specifically, automatic configuration (AC) ML models are trained, using a training corpus that includes information from workloads being run by DBMSs, to predict performance metrics based on workload features and configuration parameter values. The trained AC-ML models predict performance metrics resulting from applying particular configuration parameter values to a given database workload being automatically tuned. Based on correlating changes to configuration parameter values with changes in predicted performance metrics, an optimization algorithm is used to converge to an optimal set of configuration parameters.Type: ApplicationFiled: March 11, 2019Publication date: April 23, 2020Inventors: Sam Idicula, Tomas Karnagel, Jian Wen, Seema Sundara, Nipun Agarwal, Mayur Bency
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Publication number: 20190392068Abstract: Techniques are described herein for building a framework for declarative query compilation using both rule-based and cost-based approaches for database management. The framework involves constructing and using: a set of rule-based properties tables that contain optimization parameters for both logical and physical optimization, a recursive algorithm to form candidate physical query plans that is based on the rule based tables, and a cost model for estimating the cost of a generated physical query plan that is used with the rule based properties tables to prune inferior query plans.Type: ApplicationFiled: June 25, 2018Publication date: December 26, 2019Inventors: JIAN WEN, SAM IDICULA, NITIN KUNAL, FARHAN TAUHEED, SEEMA SUNDARA, NIPUN AGARWAL, INDU BHAGAT