Patents by Inventor Orestis KOSTAKIS
Orestis KOSTAKIS 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: 11620289Abstract: Embodiments of the present disclosure may provide a database optimization system that can generate computational values through a database compiler and assignment data for execution of a query by a plurality of nodes of a database system. The computational values and assignment data can be generated by one or more machine learning schemes. The machine learning schemes can be trained on previous computational values and previous assignment data.Type: GrantFiled: September 7, 2022Date of Patent: April 4, 2023Assignee: Snowflake Inc.Inventors: Qiming Jiang, Orestis Kostakis, John Reumann
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Patent number: 11568320Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.Type: GrantFiled: January 21, 2021Date of Patent: January 31, 2023Assignee: Snowflake Inc.Inventors: Orestis Kostakis, Qiming Jiang, Boxin Jiang
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Publication number: 20220245135Abstract: Disclosed herein are embodiments of systems and methods for analyzing query comments for identifying potential software bugs. In an example, a data platform obtains query comments associated with a query. Based on determining that the query comments include a reference to a software bug of the data platform, the data platform generates a software-bug alert based on the query comments, and transmits the software-bug alert to an endpoint.Type: ApplicationFiled: March 9, 2022Publication date: August 4, 2022Inventor: Orestis Kostakis
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Publication number: 20220237192Abstract: The subject technology receives a query directed to a set of source tables, each source table organized into a set of micro-partitions. The subject technology determines a set of metadata, the set of metadata comprising table metadata, query metadata, and historical data related to the query. The subject technology predicts, using a machine learning model, an indicator of an amount of computing resources for executing the query based at least in part on the set of metadata. The subject technology generates a query plan for executing the query based at least in part on the predicted indicator of the amount of computing resources. The subject technology executes the query based at least in part on the query plan.Type: ApplicationFiled: January 25, 2021Publication date: July 28, 2022Inventors: Qiming Jiang, Orestis Kostakis
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Publication number: 20220230093Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.Type: ApplicationFiled: January 21, 2021Publication date: July 21, 2022Inventors: Orestis Kostakis, Qiming Jiang, Boxin Jiang
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Patent number: 11372679Abstract: The subject technology requests information related to usage history metadata from a metadata database. The subject technology receives the requested information from the metadata database, the requested information comprising information related to user demand. The subject technology predicts a size value indicating an amount of computing resources to request for executing a set of queries based on the usage history metadata. The subject technology determines, during a prefetch window of time within a first period of time, a current size of freepool of computing resources. The subject technology, in response to the current size of the freepool of computing resources being smaller than the predicted size value, sends a request for additional computing resources to include in the freepool of computing resources.Type: GrantFiled: January 11, 2022Date of Patent: June 28, 2022Assignee: Snowflake Inc.Inventors: Qiming Jiang, Orestis Kostakis, Abdul Munir, Prayag Chandran Nirmala, Jeffrey Rosen
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Patent number: 11294895Abstract: Disclosed herein are systems and methods for generating anonymized software-bug alerts from query comments. In an embodiment, a data platform obtains query comments associated with a query, and determines that the query comments include a reference to a software bug of the data platform. In response to making that determination, the data platform generates an anonymized software-bug alert that includes at least part of the query comments, and transmits the anonymized software-bug alert to an endpoint such as a queue of software-bug tickets.Type: GrantFiled: November 23, 2021Date of Patent: April 5, 2022Assignee: Snowflake Inc.Inventor: Orestis Kostakis
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Patent number: 11243811Abstract: The subject technology requests information related to usage history metadata from a metadata database. The subject technology receives the requested information from the metadata database, the requested information comprising information related to user demand. The subject technology predicts a size value indicating an amount of computing resources to request for executing a set of queries based on the usage history metadata. The subject technology determines, during a prefetch window of time within a first period of time, a current size of freepool of computing resources. The subject technology, in response to the current size of the freepool of computing resources being smaller than the predicted size value, sends a request for additional computing resources to include in the freepool of computing resources.Type: GrantFiled: July 30, 2021Date of Patent: February 8, 2022Assignee: Snowflake Inc.Inventors: Qiming Jiang, Orestis Kostakis, Abdul Munir, Prayag Chandran Nirmala, Jeffrey Rosen
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Patent number: 11188528Abstract: Disclosed herein are systems and methods for rapid detection of software bugs in data platforms. One embodiment takes the form of a method that includes a comment-analysis system of a data platform receiving query comments associated with a query that was submitted to the data platform. The data platform determines that the query comments include a reference to a software bug of the data platform, and responsively causes one or more software-bug alerts pertaining to the software bug to be transmitted to one or more endpoints.Type: GrantFiled: April 27, 2021Date of Patent: November 30, 2021Assignee: Snowflake Inc.Inventor: Orestis Kostakis
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Patent number: 11138038Abstract: The subject technology determines usage history metadata. The subject technology predicts a size value indicating an amount of computing resources to request for executing a set of queries based at least in part on the usage history metadata. The subject technology determines, during a prefetch window of time within a first period of time, a current size of a freepool of computing resources. The subject technology, in response to the current size of the freepool of computing resources being smaller than the predicted size value, sends a request for additional computing resources to include in the freepool of computing resources. The subject technology receives an indication that the request for additional computing resources was granted. The subject technology performs an operation to include the additional computing resources in the freepool of computing resources.Type: GrantFiled: February 11, 2021Date of Patent: October 5, 2021Assignee: Snowflake Inc.Inventors: Qiming Jiang, Orestis Kostakis, Abdul Munir, Prayag Chandran Nirmala, Jeffrey Rosen
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Patent number: 10970270Abstract: Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.Type: GrantFiled: May 29, 2018Date of Patent: April 6, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Karthik Raman, Momin Mahmoud Al-Ghosien, Samer Boshra, Brandon Chong, Madhan Gajendran, Mikhail Mikhailovich Koltachev, Orestis Kostakis, Aravind Ramachandran Krishna, Liang Li, Jayanta Mondal, Balachandar Perumalswamy, Karan Vishwanath Popali, Adrian Ilcu Predescu, Vivek Ravindran, Ankur Savailal Shah, Pankaj Sharma, Dharma Shukla, Ashwini Singh, Vinod Sridharan, Hari Sudan Sundar, Krishnan Sundaram, Shireesh Kumar Thota, Oliver Drew Leonard Towers, Siddhesh Dilip Vethe
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Publication number: 20190372939Abstract: There are provided measures for malicious network activity mitigation. Such measures exemplarily comprise determining a boundary enclosing a first group of target virtual network functions including at least one target virtual network function, identifying, on the basis of said boundary, a first group of communication paths between said first group of target virtual network functions and respective network entities outside said boundary, said first group of communication paths including a first communication path, and initiating setup of a first wrapper virtual network function corresponding to said first communication path, said first wrapper virtual network function monitoring network traffic on said first communication path.Type: ApplicationFiled: September 16, 2016Publication date: December 5, 2019Inventors: Aapo KALLIOLA, Ian Justin OLIVER, Yoan Jean Claude MICHE, Orestis KOSTAKIS
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Publication number: 20190340291Abstract: Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.Type: ApplicationFiled: May 29, 2018Publication date: November 7, 2019Inventors: Karthik RAMAN, Momin Mahmoud AL-GHOSIEN, Samer BOSHRA, Brandon CHONG, Madhan GAJENDRAN, Mikhail Mikhailovich KOLTACHEV, Orestis KOSTAKIS, Aravind Ramachandran KRISHNA, Liang LI, Jayanta MONDAL, Balachandar PERUMALSWAMY, Karan Vishwanath POPALI, Adrian Ilcu PREDESCU, Vivek RAVINDRAN, Ankur Savailal SHAH, Pankaj SHARMA, Dharma SHUKLA, Ashwini SINGH, Vinod SRIDHARAN, Hari Sudan SUNDAR, Krishnan SUNDARAM, Shireesh Kumar THOTA, Oliver Drew Leonard TOWERS, Siddhesh Dilip VETHE