A (FM/DRDPE)-based approach to improve federated learning optimizer
Recently, there is a growing need for query optimization algorithm that can effectively deal with federated database systems. Modern optimizers use a cost model to choose the best query execution plan (QEP) which heavily dependent on statistics maintained in the system catalog. Keeping such statisti...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 438 |
|---|---|
| container_issue | |
| container_start_page | 433 |
| container_title | |
| container_volume | |
| creator | Salem, M.M. Ali, H.A. Badawy, M.M. |
| description | Recently, there is a growing need for query optimization algorithm that can effectively deal with federated database systems. Modern optimizers use a cost model to choose the best query execution plan (QEP) which heavily dependent on statistics maintained in the system catalog. Keeping such statistics up to date in the federation is troublesome due to local autonomy. The main objective of this paper is to introduce a general framework for federated database system based on DB2 II to improve federated learning optimizer and enhancing global query optimization. In addition it will suggest two algorithms which may be evolved within the proposed framework to give the federation the full autonomy, precise statistics collection, efficiency in processing federated queries and permitting mid-query execution. |
| doi_str_mv | 10.1109/ICCES.2007.4447082 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4447082</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4447082</ieee_id><sourcerecordid>4447082</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-25c2c85f8eb9369ace84c14f6acbfafd4d4084d1502d07e9511a36452b5e33913</originalsourceid><addsrcrecordid>eNotj11LwzAYhQMiKLN_QG9yqRft8vEmTS5H1-lgovhxPdLkrUbWtaRF0F9vwZ2b88CBBw4h15wVnDO73FZV_VoIxsoCAEpmxBnJbGk4CAAutRIXJBvHLzZHa2OZviTVit5uHpfrl_VzfZc3bsRA3TCk3vlPOvU0djN_I20xYHLTvB7QpWM8ftB-mGIXfzFdkfPWHUbMTr0g75v6rXrId0_322q1yyMv1ZQL5YU3qjXYWKmt82jAc2i1803r2gABmIHAFROBlWgV505qUKJRKKXlckFu_r0REfdDip1LP_vTVfkHyWJITA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A (FM/DRDPE)-based approach to improve federated learning optimizer</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Salem, M.M. ; Ali, H.A. ; Badawy, M.M.</creator><creatorcontrib>Salem, M.M. ; Ali, H.A. ; Badawy, M.M.</creatorcontrib><description>Recently, there is a growing need for query optimization algorithm that can effectively deal with federated database systems. Modern optimizers use a cost model to choose the best query execution plan (QEP) which heavily dependent on statistics maintained in the system catalog. Keeping such statistics up to date in the federation is troublesome due to local autonomy. The main objective of this paper is to introduce a general framework for federated database system based on DB2 II to improve federated learning optimizer and enhancing global query optimization. In addition it will suggest two algorithms which may be evolved within the proposed framework to give the federation the full autonomy, precise statistics collection, efficiency in processing federated queries and permitting mid-query execution.</description><identifier>ISBN: 9781424413652</identifier><identifier>ISBN: 1424413656</identifier><identifier>DOI: 10.1109/ICCES.2007.4447082</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cost function ; Database systems ; Feedback ; Information systems ; Low earth orbit satellites ; Maintenance engineering ; Query processing ; Remote monitoring ; Runtime ; Statistics</subject><ispartof>2007 International Conference on Computer Engineering & Systems, 2007, p.433-438</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4447082$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4447082$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Salem, M.M.</creatorcontrib><creatorcontrib>Ali, H.A.</creatorcontrib><creatorcontrib>Badawy, M.M.</creatorcontrib><title>A (FM/DRDPE)-based approach to improve federated learning optimizer</title><title>2007 International Conference on Computer Engineering & Systems</title><addtitle>ICCES</addtitle><description>Recently, there is a growing need for query optimization algorithm that can effectively deal with federated database systems. Modern optimizers use a cost model to choose the best query execution plan (QEP) which heavily dependent on statistics maintained in the system catalog. Keeping such statistics up to date in the federation is troublesome due to local autonomy. The main objective of this paper is to introduce a general framework for federated database system based on DB2 II to improve federated learning optimizer and enhancing global query optimization. In addition it will suggest two algorithms which may be evolved within the proposed framework to give the federation the full autonomy, precise statistics collection, efficiency in processing federated queries and permitting mid-query execution.</description><subject>Cost function</subject><subject>Database systems</subject><subject>Feedback</subject><subject>Information systems</subject><subject>Low earth orbit satellites</subject><subject>Maintenance engineering</subject><subject>Query processing</subject><subject>Remote monitoring</subject><subject>Runtime</subject><subject>Statistics</subject><isbn>9781424413652</isbn><isbn>1424413656</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj11LwzAYhQMiKLN_QG9yqRft8vEmTS5H1-lgovhxPdLkrUbWtaRF0F9vwZ2b88CBBw4h15wVnDO73FZV_VoIxsoCAEpmxBnJbGk4CAAutRIXJBvHLzZHa2OZviTVit5uHpfrl_VzfZc3bsRA3TCk3vlPOvU0djN_I20xYHLTvB7QpWM8ftB-mGIXfzFdkfPWHUbMTr0g75v6rXrId0_322q1yyMv1ZQL5YU3qjXYWKmt82jAc2i1803r2gABmIHAFROBlWgV505qUKJRKKXlckFu_r0REfdDip1LP_vTVfkHyWJITA</recordid><startdate>200711</startdate><enddate>200711</enddate><creator>Salem, M.M.</creator><creator>Ali, H.A.</creator><creator>Badawy, M.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200711</creationdate><title>A (FM/DRDPE)-based approach to improve federated learning optimizer</title><author>Salem, M.M. ; Ali, H.A. ; Badawy, M.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-25c2c85f8eb9369ace84c14f6acbfafd4d4084d1502d07e9511a36452b5e33913</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Cost function</topic><topic>Database systems</topic><topic>Feedback</topic><topic>Information systems</topic><topic>Low earth orbit satellites</topic><topic>Maintenance engineering</topic><topic>Query processing</topic><topic>Remote monitoring</topic><topic>Runtime</topic><topic>Statistics</topic><toplevel>online_resources</toplevel><creatorcontrib>Salem, M.M.</creatorcontrib><creatorcontrib>Ali, H.A.</creatorcontrib><creatorcontrib>Badawy, M.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Salem, M.M.</au><au>Ali, H.A.</au><au>Badawy, M.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A (FM/DRDPE)-based approach to improve federated learning optimizer</atitle><btitle>2007 International Conference on Computer Engineering & Systems</btitle><stitle>ICCES</stitle><date>2007-11</date><risdate>2007</risdate><spage>433</spage><epage>438</epage><pages>433-438</pages><isbn>9781424413652</isbn><isbn>1424413656</isbn><abstract>Recently, there is a growing need for query optimization algorithm that can effectively deal with federated database systems. Modern optimizers use a cost model to choose the best query execution plan (QEP) which heavily dependent on statistics maintained in the system catalog. Keeping such statistics up to date in the federation is troublesome due to local autonomy. The main objective of this paper is to introduce a general framework for federated database system based on DB2 II to improve federated learning optimizer and enhancing global query optimization. In addition it will suggest two algorithms which may be evolved within the proposed framework to give the federation the full autonomy, precise statistics collection, efficiency in processing federated queries and permitting mid-query execution.</abstract><pub>IEEE</pub><doi>10.1109/ICCES.2007.4447082</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781424413652 |
| ispartof | 2007 International Conference on Computer Engineering & Systems, 2007, p.433-438 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_4447082 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cost function Database systems Feedback Information systems Low earth orbit satellites Maintenance engineering Query processing Remote monitoring Runtime Statistics |
| title | A (FM/DRDPE)-based approach to improve federated learning optimizer |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T02%3A26%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20(FM/DRDPE)-based%20approach%20to%20improve%20federated%20learning%20optimizer&rft.btitle=2007%20International%20Conference%20on%20Computer%20Engineering%20&%20Systems&rft.au=Salem,%20M.M.&rft.date=2007-11&rft.spage=433&rft.epage=438&rft.pages=433-438&rft.isbn=9781424413652&rft.isbn_list=1424413656&rft_id=info:doi/10.1109/ICCES.2007.4447082&rft_dat=%3Cieee_6IE%3E4447082%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4447082&rfr_iscdi=true |