Sanjay Agrawal - Redmond WA Surajit Chaudhuri - Redmond WA Vivek R. Narasayya - Redmond WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 1730
US Classification:
707 2, 707 3, 707 5
Abstract:
An index and materialized view selection wizard produces a fast and reasonable recommendation for a configuration of indexes, materialized views, and indexes on materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes indexes, materialized views and indexes on materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.
Identifying Indexes On Materialized Views For Database Workload
Sanjay Agrawal - Redmond WA Surajit Chaudhuri - Redmond WA Vivek R. Narasayya - Redmond WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 1730
US Classification:
707 2, 707 3, 707 5
Abstract:
An index and materialized view selection wizard produces a fast and reasonable recommendation for a configuration of indexes, materialized views, and indexes on materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes indexes, materialized views and indexes on materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.
Index And Materialized View Selection For A Given Workload
Sanjay Agrawal - Redmond WA Surajit Chaudhuri - Redmond WA Vivek R. Narasayya - Redmond WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 1730
US Classification:
707 2, 707 4, 707 5
Abstract:
An index and materialized view selection wizard produces a fast and reasonable recommendation of indexes and materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes both indexes and materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.
Interesting Table-Subset Selection For Database Workload Materialized View Selection
Sanjay Agrawal - Redmond WA Surajit Chaudhuri - Redmond WA Vivek R. Narasayya - Redmond WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 1730
US Classification:
707 2, 707 5
Abstract:
An index and materialized view selection wizard produces a fast and reasonable recommendation for a configuration of indexes, materialized views, and indexes on materialized views which are beneficial given a specified workload for a given database and database server. Candidate materialized views and indexes are obtained, and a joint enumeration of the combined materialized views and indexes is performed to obtain a recommended configuration. The configuration includes indexes, materialized views and indexes on materialized views. Candidate materialized views are obtained by first determining subsets of tables are referenced in queries in the workload and then finding interesting table subsets. Next, interesting subsets are considered on a per query basis to determine which are syntactically relevant for a query. Materialized views which are likely to be used for the workload are then generated along with a set of merged materialized views.
Generalized Keyword Matching For Keyword Based Searching Over Relational Databases
Surajit Chaudhuri - Redmond WA Sanjay Agrawal - Redmond WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 1730
US Classification:
707 2, 707 3
Abstract:
Searching by keywords and providing generalized matching capabilities on a relational database is enabled by performing preprocessing operations to construct inverted list lookup tables based on data record components at an interim level of granularity, such as column location. Prefix information is in the inverted list stored for each keyword, keyword sub-string, or stemmed version of the keyword. A keyword search is performed on the lookup tables rather than the database tables to determine database column locations of the keyword. The lookup tables is scanned to identify each prefix associated with the search term. Schema information about the database is used to link the column locations to form database subgraphs that span the keywords. Join tables are to generated based on the subgraphs consisting of columns containing the keywords. A query on the database is generated to join the tables and retrieve database rows that contain the keyword and the prefixes associated with the keyword.
System For Keyword Based Searching Over Relational Databases
Surajit Chaudhuri - Redmond WA Sanjay Agrawal - Redmond WA Guatam Das - Redmond WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 1730
US Classification:
707 2, 707 3
Abstract:
Searching by keywords on a relational database is enabled by performing preprocessing operations to construct lookup tables at an interim level of granularity, such as column location. A keyword search is performed on the lookup tables rather than the database tables to determine database column locations of the keyword. Schema information about the database is used to link the column locations to form database subgraphs that span the keywords. Join tables are to generated based on the subgraphs consisting of columns containing the keywords. A query on the database is generated to join the tables and retrieve database rows that contain the keywords. The retrieved rows are ranked in order of relevance before being output. By preprocessing a relational database to form lookup tables, and initially searching the lookup tables to obtain a targeted subset of the database upon which SQL queries can be performed to collect data records, keyword searching on relational database is made efficient.
Surajit Chaudhuri - Redmond WA, US Ashish Kumar Gupta - Seattle WA, US Vivek Narasayya - Redmond WA, US Sanjay Agrawal - Kirkland WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F017/30
US Classification:
7071041, 706917, 709224, 703 21
Abstract:
Relational database applications such as index selection, histogram tuning, approximate query processing, and statistics selection have recognized the importance of leveraging workloads. Often these applications are presented with large workloads, i. e. , a set of SQL DML statements, as input. A key factor affecting the scalability of such applications is the size of the workload. The invention concerns workload compression which helps improve the scalability of such applications. The exemplary embodiment is broadly applicable to a variety of workload-driven applications, while allowing for incorporation of application specific knowledge. The process is described in detail in the context of two workload-driven applications: index selection and approximate query processing.
Aggregate Fair Queuing Technique In A Communications System Using A Class Based Queuing Architecture
Sanjay K. Agrawal - San Jose CA, US Neil N. Mammen - San Jose CA, US Ajit Ninan - San Jose CA, US Jason C. Fan - Mountain View CA, US
Assignee:
Luminous Networks, Inc. - Cupertino CA
International Classification:
H04L 12/26
US Classification:
370235
Abstract:
A communications network is described having a class-based queuing architecture. Shared class queues receive packet flows from different customers. In one embodiment, there are eight classes and thus eight shared queues, one for each class. A scheduler schedules the output of packets by the various queues based on priority. Each customer (or other aggregate of packet flows) is allocated a certain space in a class queue based on the customers' Service Level Agreement (SLA) with the service provider. A queue input circuit detects bits in the packet header identifying the customer (or other criteria) and makes selections to drop or pass packets destined for a shared queue based on the customers' (or other aggregates') allocated space in the queue. In another embodiment, the relative positions of the nodes in the network are taken into account by each node when dropping packets forwarded by other nodes by detecting a node label (or other ID code) so that packets from the various nodes are dropped in a more fair way when there is congestion in the network, irrespective of the “passing ” node's position relative to the other nodes.
Benign Polyps of the Colon Constipation Disorders of Lipoid Metabolism Diverticulosis Esophagitis
Languages:
English German Spanish
Description:
Dr. Agrawal graduated from the Sawai Man Singh Med Coll, Rajasthan Univ, Jaipur, Rajasthan, India in 1992. He works in Gig Harbor, WA and 1 other location and specializes in Gastroenterology. Dr. Agrawal is affiliated with MultiCare Allenmore Hospital, St Joseph Medical Center and Tacoma General Hospital.
Principal Engineer and Management Leader, SDN Architecture Strategy at Cisco Systems
Location:
San Francisco Bay Area
Industry:
Computer Networking
Work:
Cisco Systems - San Jose, CA since Dec 2005
Principal Engineer and Management Leader, SDN Architecture Strategy
Telsima (now part of Aviat) Jan 2003 - Sep 2005
Founder and Chief of Architecture & Strategy
Propulsion Networks - camden, ca Feb 2001 - Dec 2002
Founder & Director of Architecture Strategy
Luminous Networks 2000 - 2001
Principle Engineer
Education:
Stanford University 1992 - 1997
Ph. D., Electrical Engineering and Computer Science
University of Illinois at Urbana-Champaign 1989 - 1992
BS, Computer Engineering
Saroj Securities (Member - National Stock Exchange) - CEO (2009) Saroj & Co - Proprietor (1992-2009)
Education:
Pandit Deendayal Upadhyay Sanatan Dharm Vidhyalay - High school, Pandit Deendayal Upadhyay Sanatan Dharm Vidhyalay - Intermediate, University of Poona - Electronics Engineering
Sanjay Agrawal
Work:
Hotel qutub Itdc
Education:
Pt. rsu, raipur
Bragging Rights:
S.s. kalibadi school, raipur
Sanjay Agrawal
Education:
University of Bombay / Mumbai - Master of Surgery, MS,, Calcutta University - Bachelor of Medicine and Bachelor of Surgery, M.B.B.S
About:
Mr Sanjay Agrawal is a Consultant General Surgeon specialising in Obesity and advanced Laparoscopic Surgery at Homerton University Hospital in London. Mr Agrawal specialises in laparoscopic weight los...
Tagline:
General Surgeon specialising in Obesity/Weight Loss Surgery & advanced Laparoscopic Surgery