Nga M Tran1842 Oakdale St, Philadelphia, PA 19125

8086598, Dec 27, 2011

Feb 6, 2009

12/367415

Andrew Lamb - Arlington MA, US
Mitch Cherniack - Chelsea MA, US
Shilpa Lawande - Littleton MA, US
Nga Tran - Framingham MA, US

Hewlett-Packard Development Company, L.P. - Houston TX

G06F 17/30

707714, 707606, 707797

Methods, program products and systems for determining, for a database query that does not represent a snowflake schema, a graph comprising vertices each representing a table joined in the query, a directed edge between each pair of vertices of which a first vertex represents a first table and a second vertex represents a second table that is joined in the query with the first table, each of the edges representing one of an outer join and an inner join. Further determining, for the graph, a directed spanning tree that represents an ordering of joins in the query and includes all outer join edges in the graph.

8214352, Jul 3, 2012

Nov 24, 2009

12/625482

Andrew Lamb - Arlington MA, US
Lakshmikant Shrinivas - Billerica MA, US
Shilpa Lawande - Littleton MA, US
Mitch Cherniack - Chelsea MA, US
Nga Tran - Framingham MA, US

Hewlett-Packard Development Company - Houston TX

G06F 17/30

707713, 707714, 707718

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modular query optimizer. In one aspect, a method includes selecting one or more projections from a set of projections for each table in a database query wherein each of the selected projections for the table has leads to an estimated lower execution cost for the query as compared to non-selected projections; generating join orders for the query based on data distribution of one or more of the selected projections among sites in a computer network wherein the join orders reflect different combinations of data distribution operations applied to the output of one or more of the query's joins; and selecting a join order from the join orders based on evaluation of the join orders using a cost model.

8312027, Nov 13, 2012

Jun 5, 2012

13/489094

Andrew Lamb - Arlington MA, US
Lakshmikant Shrinivas - Billerica MA, US
Shilpa Lawande - Littleton MA, US
Mitch Cherniack - Chelsea MA, US
Nga Tran - Framingham MA, US

Hewlett-Packard Development Company, L.P. - Houston TX

G06F 17/30

707752, 707753, 707713, 707714, 707718

Computer programs encoded on a computer storage medium provide for a modular query optimizer. In one aspect, a computer program product includes selecting one or more projections from a set of projections for each table in a database query wherein each of the selected projections for the table has leads to an estimated lower execution cost for the query as compared to non-selected projections; generating join orders for the query based on data distribution of one or more of the selected projections among sites in a computer network wherein the join orders reflect different combinations of data distribution operations applied to the output of one or more of the query's joins; and selecting a join order from the join orders based on evaluation of the join orders using a cost model.

2008003, Feb 7, 2008

Aug 2, 2006

11/461937

Mitch Cherniack - Chelsea MA, US
Shilpa Lawande - Nashua NH, US
Nga Tran - Framingham MA, US

G06F 7/00
G06F 17/30

707 3, 707 7

For a database query that defines a plurality of separate snowflake schemas, a query optimizer computes separately for each of the snowflake schemas a logical access plan for obtaining from that schema's tables a respective record set that includes the data requested from those tables by that query. The query optimizer also computes a logical access plan for obtaining the query's results from the record sets in which execution of the logical access plans thus computed will result.

2020008, Mar 19, 2020

Aug 7, 2015

15/750654

- Sunnyvale CA, US
Thao Nguyen PHAM - Cambridge MA, US
Nga TRAN - Cambridge MA, US
Chuck Edward BEAR - Cambridge MA, US

G06F 16/2453
G06F 16/22

Examples herein involve providing a first query to an optimizer; identifying a first execution plan selected by the optimizer based on the first query; generating an annotated query based on the selected first execution plan, the annotated query comprising hints for executing the selected first execution plan; and storing the annotated query in a database, the annotated query to cause the optimizer to generate a second execution plan corresponding to the first execution plan based on the hints.

2019006, Feb 28, 2019

Aug 28, 2017

15/688144

- Sunnyvale CA, US
Yuanzhe Bei - Cambridge MA, US
Charles Edward Bear - Cambridge MA, US
Nga Tran - Andover MA, US

EntlT Software LLC - Sunnyvale CA

G06F 17/30

According to an example, a system may receive a refresh column command for a column-oriented database. The refresh column command may identify a table in the column-oriented database and a denormalized column in the table. The system may determine column definitions for the column, generate a query to populate values from a data source associated with the column into the column based on the column definitions, and may execute the query to replace entire set of existing column values with the data source values.

2018023, Aug 16, 2018

Aug 11, 2015

15/751265

- Sunnyvale CA, US
Charles Edward BEAR - Cambridge MA, US
Nga TRAN - Cambridge MA, US

G06F 17/30

A user-defined function (UDF) is received. Then, execution of the UDF is distributed into a plurality of phases. Lastly, each of the phases is executed separately on a relational database including an aggregate projection that stores an aggregate value calculated from a table column of a table using an aggregate function.

2016027, Sep 22, 2016

Dec 2, 2013

15/033537

- Houston TX, US
Nga TRAN - Cambridge MA, US
Sreenath BODAGALA - Cambridge MA, US

G06F 17/30

In an example, data from a source location is merged into a target location, in which both the source location and the target location contain tuples of data. A matched tuple in the target table that matches a The deleting of the tuple in the target table includes executing a semi inner join operation between the source table and the target table to identify the matched tuple. Each tuple contained in the source table may be inserted into the target table as a new tuple.