OPTIMIZING DEVICE HAVING NEURAL NETWORK EVALUATING DEVICE

PROBLEM TO BE SOLVED: To estimate selectively and one call cost data by dynamically applying a neural network to a database management system based on generation selectivity value in order to determine an optimal query search sequence. SOLUTION: This device includes a feature vector extraction eleme...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: ZHOU SHAOYU, LAKSHMI SEETHA M
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator ZHOU SHAOYU
LAKSHMI SEETHA M
description PROBLEM TO BE SOLVED: To estimate selectively and one call cost data by dynamically applying a neural network to a database management system based on generation selectivity value in order to determine an optimal query search sequence. SOLUTION: This device includes a feature vector extraction element which is connected to data base tables 120 and 122 which convert an input-output parameters of a related user defined routines(UDRs) into base representation that has standard data type. A neural network receives a feature vector output and produces an estimation optimization value that is used by an optimizing device 102. The neural network regularly uses a query that is randomly produced to conduct training or dynamically trainined by taking in data that are generated in the query. Under a prediction mode, the device 102 dynamically calls the neural network in order to produce estimation that is used to determine optimum DBMS database management system 100 query search sequence to the DBMS.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_JPH11175566A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>JPH11175566A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_JPH11175566A3</originalsourceid><addsrcrecordid>eNrjZLD0Dwjx9PWM8vRzV3BxDfN0dlXwcAwD8fxcQ4McfYBUSLh_kLeCa5ijT6hjCEIdDwNrWmJOcSovlOZmUHRzDXH20E0tyI9PLS5ITE7NSy2J9wrwMDQ0NDc1NTNzNCZGDQCqeykq</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>OPTIMIZING DEVICE HAVING NEURAL NETWORK EVALUATING DEVICE</title><source>esp@cenet</source><creator>ZHOU SHAOYU ; LAKSHMI SEETHA M</creator><creatorcontrib>ZHOU SHAOYU ; LAKSHMI SEETHA M</creatorcontrib><description>PROBLEM TO BE SOLVED: To estimate selectively and one call cost data by dynamically applying a neural network to a database management system based on generation selectivity value in order to determine an optimal query search sequence. SOLUTION: This device includes a feature vector extraction element which is connected to data base tables 120 and 122 which convert an input-output parameters of a related user defined routines(UDRs) into base representation that has standard data type. A neural network receives a feature vector output and produces an estimation optimization value that is used by an optimizing device 102. The neural network regularly uses a query that is randomly produced to conduct training or dynamically trainined by taking in data that are generated in the query. Under a prediction mode, the device 102 dynamically calls the neural network in order to produce estimation that is used to determine optimum DBMS database management system 100 query search sequence to the DBMS.</description><edition>6</edition><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>1999</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=19990702&amp;DB=EPODOC&amp;CC=JP&amp;NR=H11175566A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=19990702&amp;DB=EPODOC&amp;CC=JP&amp;NR=H11175566A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHOU SHAOYU</creatorcontrib><creatorcontrib>LAKSHMI SEETHA M</creatorcontrib><title>OPTIMIZING DEVICE HAVING NEURAL NETWORK EVALUATING DEVICE</title><description>PROBLEM TO BE SOLVED: To estimate selectively and one call cost data by dynamically applying a neural network to a database management system based on generation selectivity value in order to determine an optimal query search sequence. SOLUTION: This device includes a feature vector extraction element which is connected to data base tables 120 and 122 which convert an input-output parameters of a related user defined routines(UDRs) into base representation that has standard data type. A neural network receives a feature vector output and produces an estimation optimization value that is used by an optimizing device 102. The neural network regularly uses a query that is randomly produced to conduct training or dynamically trainined by taking in data that are generated in the query. Under a prediction mode, the device 102 dynamically calls the neural network in order to produce estimation that is used to determine optimum DBMS database management system 100 query search sequence to the DBMS.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>1999</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLD0Dwjx9PWM8vRzV3BxDfN0dlXwcAwD8fxcQ4McfYBUSLh_kLeCa5ijT6hjCEIdDwNrWmJOcSovlOZmUHRzDXH20E0tyI9PLS5ITE7NSy2J9wrwMDQ0NDc1NTNzNCZGDQCqeykq</recordid><startdate>19990702</startdate><enddate>19990702</enddate><creator>ZHOU SHAOYU</creator><creator>LAKSHMI SEETHA M</creator><scope>EVB</scope></search><sort><creationdate>19990702</creationdate><title>OPTIMIZING DEVICE HAVING NEURAL NETWORK EVALUATING DEVICE</title><author>ZHOU SHAOYU ; LAKSHMI SEETHA M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_JPH11175566A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>1999</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHOU SHAOYU</creatorcontrib><creatorcontrib>LAKSHMI SEETHA M</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHOU SHAOYU</au><au>LAKSHMI SEETHA M</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>OPTIMIZING DEVICE HAVING NEURAL NETWORK EVALUATING DEVICE</title><date>1999-07-02</date><risdate>1999</risdate><abstract>PROBLEM TO BE SOLVED: To estimate selectively and one call cost data by dynamically applying a neural network to a database management system based on generation selectivity value in order to determine an optimal query search sequence. SOLUTION: This device includes a feature vector extraction element which is connected to data base tables 120 and 122 which convert an input-output parameters of a related user defined routines(UDRs) into base representation that has standard data type. A neural network receives a feature vector output and produces an estimation optimization value that is used by an optimizing device 102. The neural network regularly uses a query that is randomly produced to conduct training or dynamically trainined by taking in data that are generated in the query. Under a prediction mode, the device 102 dynamically calls the neural network in order to produce estimation that is used to determine optimum DBMS database management system 100 query search sequence to the DBMS.</abstract><edition>6</edition><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_JPH11175566A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title OPTIMIZING DEVICE HAVING NEURAL NETWORK EVALUATING DEVICE
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T21%3A17%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ZHOU%20SHAOYU&rft.date=1999-07-02&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EJPH11175566A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true