View source for 17808216. QUERY SET OPTIMIZATION IN A DATA ANALYTICS PIPELINE simplified abstract (Microsoft Technology Licensing, LLC)

=QUERY SET OPTIMIZATION IN A DATA ANALYTICS PIPELINE=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Jyoti Leeka of Sunnyvale CA (US)|Jyoti Leeka of Sunnyvale CA (US)]][[Category:Jyoti Leeka of Sunnyvale CA (US)]]

[[:Category:Sunny Gakhar of Seattle WA (US)|Sunny Gakhar of Seattle WA (US)]][[Category:Sunny Gakhar of Seattle WA (US)]]

[[:Category:Hiren S. Patel of Bothell WA (US)|Hiren S. Patel of Bothell WA (US)]][[Category:Hiren S. Patel of Bothell WA (US)]]

[[:Category:Marc Todd Friedman of Seattle WA (US)|Marc Todd Friedman of Seattle WA (US)]][[Category:Marc Todd Friedman of Seattle WA (US)]]

[[:Category:Brandon Haynes of Seattle WA (US)|Brandon Haynes of Seattle WA (US)]][[Category:Brandon Haynes of Seattle WA (US)]]

[[:Category:Shi Qiao of Redmond WA (US)|Shi Qiao of Redmond WA (US)]][[Category:Shi Qiao of Redmond WA (US)]]

[[:Category:Alekh Jindal of Seattle WA (US)|Alekh Jindal of Seattle WA (US)]][[Category:Alekh Jindal of Seattle WA (US)]]

==QUERY SET OPTIMIZATION IN A DATA ANALYTICS PIPELINE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17808216 titled 'QUERY SET OPTIMIZATION IN A DATA ANALYTICS PIPELINE

==Simplified Explanation==

The patent application describes a method for optimizing data analytics queries that involve multiple operators and dependencies between queries. 

* The method identifies the relationships between queries, where each query can either produce data for another query or consume data from another query.
* Based on these relationships, the method identifies optimizations that can be applied to improve the performance of the queries.
* The identified optimizations are then applied to the queries to enhance their efficiency and effectiveness.

== Potential Applications ==
* This technology can be applied in various data analytics scenarios where multiple queries are involved, such as business intelligence, data mining, and machine learning.
* It can be used in industries like finance, healthcare, e-commerce, and telecommunications to optimize data analysis processes.

== Problems Solved ==
* Traditional data analytics queries often lack optimization techniques, leading to inefficient and time-consuming processes.
* The method addresses the problem of optimizing queries with multiple operators and dependencies, improving the overall performance of data analytics tasks.

== Benefits ==
* The method improves the efficiency and effectiveness of data analytics queries by identifying and applying optimizations.
* It reduces the time and resources required for data analysis, leading to faster insights and decision-making.
* By optimizing the queries, it enhances the overall performance of data analytics systems, enabling organizations to handle larger datasets and complex analysis tasks.


==Original Abstract Submitted==

In a set of data analytics queries, at least a one of the queries comprising more than one operator, and each query being at least one of i) a producer of data for an other query in the set, and ii) a consumer of data from an other query in the set. In such examples, one or more computing devices identify each producer/consumer relationship between the queries. The one or more computing devices identify one or more optimizations among the queries based on the identified relationships. The one or more computing devices then apply at least one identified optimization to at least one of the queries.



[[Category:G06F16/2453]]
[[Category:G06F16/2458]]
[[Category:G06F16/27]]