XLDB (eXtremely Large DataBases) is a yearly conference about databases, data management and analytics. The definition of extremely large refers to data sets that are too big in terms of volume (too much), and/or velocity (too fast), and/or variety (too many places, too many formats) to be handled using conventional solutions. This conference deals with the high-end of very large databases (VLDB). It was conceived and it is chaired by Jacek Becla.
In October 2007, data experts gathered at SLAC National Accelerator Lab for the . As a result, the XLDB research community was formed to meet the rapidly growing demands of the largest data systems. In addition to the original invitational workshop, an open conference, tutorials, and annual satellite events on different continents were added. The main event, held annually at Stanford University gathers over 300 attendees. XLDB is one of the data systems events catering to both academic and industry communities. For 2009, the workshop was co-located with VLDB 2009 in France to reach out to non-US research communities.[1] XLDB 2019 followed Stanford's Conference on Systems and Machine Learning (SysML).[2]
Identify trends, commonalities and major roadblocks related to building extremely large databases
Bridge the gap between users trying to build extremely large databases and database solution providers worldwide
Facilitate development and growth of practical technologies for extremely large data stores
XLDB Communityedit
As of 2013, the community consisted of above one thousand members including:
Scientists who develop, use, or plan to develop or use XLDB for their research, from laboratories.
Commercial users of XLDB.
Providers of database products, including commercial vendors and representatives from open source database communities.
Academic database researchers.
XLDB Conferences, Workshops and Tutorialsedit
The community meets annually at Stanford University where the main event is held each Spring. Those who live too far from California to attend have the opportunity to attend occasional satellite events either in Asia or Europe.
A detailed report or videos are produced after each workshop.
XLDB events led to initiating an effort to build a new open source, science database called .[4]
The XLDB organizers started defining a science benchmark for scientific data management systems called SS-DB.
At XLDB 2012 the XLDB organizers announced that two major databases that support arrays as first-class objects (MonetDB SciQL and SciDB) have formed a working group in conjunction with XLDB. This working group is proposing a common syntax (provisionally named “ArrayQL”) for manipulating arrays, including array creation and query.
^"Building the biggest scientific databases". symmetry magazine. Retrieved 2019-04-15.
^"XLDB Extremely Large Databases 2019". XLDB Extremely Large Databases 2019. Retrieved 2019-04-15.
^Becla, Jacek (2009). "XLDB 3 Welcome". Retrieved 2009-08-29.
^Becla, Jacek (2008). "Report from the SciDB Workshop". Retrieved 2008-09-29.[permanent dead link]
Further readingedit
Pavlo A., Paulson E., Rasin A., Abadi D. J., Dewitt D. J., Madden S., and Stonebraker M., A Comparison of Approaches to Large-Scale Data Analysis," Proceedings of the 2009 ACM SIGMOD,
Becla, Jacek; Hanushevsky, Andrew; Nikolaev, Sergei; Abdulla, Ghaleb; Szalay, Alex; Nieto-Santisteban, Maria; Thakar, Ani; Gray, Jim (2006). "Designing a multi-petabyte database for LSST". In Silva, David R; Doxsey, Rodger E (eds.). Observatory Operations: Strategies, Processes, and Systems. Vol. 6270. pp. 62700R. arXiv:cs/0604112. doi:10.1117/12.671721. S2CID 3204824.
Becla, J., & Wang, D. L. 2005, Lessons Learned from Managing a Petabyte, downloaded from on 2007-11-25.
Duellmann, D. 1999, Petabyte Databases, ACM SIGMOD Record, vol. 28, p. 506, .
Hanushevsky, A., & Nowak, M. 1999, Pursuit of a Scalable High Performance Multi-Petabyte Database, 16th IEEE Symposium on Mass Storage Systems, pp. 169–175, http://citeseer.ist.psu.edu/217883.html.
Shiers, J., Building Very Large, Distributed Object Databases, downloaded from on 2007-11-25.
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XLDB eXtremely Large DataBases is a yearly conference about databases data management and analytics The definition of extremely large refers to data sets that are too big in terms of volume too much and or velocity too fast and or variety too many places too many formats to be handled using conventional solutions This conference deals with the high end of very large databases VLDB It was conceived and it is chaired by Jacek Becla Contents 1 History 2 Goals 3 XLDB Community 4 XLDB Conferences Workshops and Tutorials 5 Tangible results 6 See also 7 References 8 Further reading 9 External linksHistory editIn October 2007 data experts gathered at SLAC National Accelerator Lab for the First Workshop on Extremely Large Databases As a result the XLDB research community was formed to meet the rapidly growing demands of the largest data systems In addition to the original invitational workshop an open conference tutorials and annual satellite events on different continents were added The main event held annually at Stanford University gathers over 300 attendees XLDB is one of the data systems events catering to both academic and industry communities For 2009 the workshop was co located with VLDB 2009 in France to reach out to non US research communities 1 XLDB 2019 followed Stanford s Conference on Systems and Machine Learning SysML 2 Goals editThe main goals of this community include 3 Identify trends commonalities and major roadblocks related to building extremely large databases Bridge the gap between users trying to build extremely large databases and database solution providers worldwide Facilitate development and growth of practical technologies for extremely large data storesXLDB Community editAs of 2013 the community consisted of above one thousand members including Scientists who develop use or plan to develop or use XLDB for their research from laboratories Commercial users of XLDB Providers of database products including commercial vendors and representatives from open source database communities Academic database researchers XLDB Conferences Workshops and Tutorials editThe community meets annually at Stanford University where the main event is held each Spring Those who live too far from California to attend have the opportunity to attend occasional satellite events either in Asia or Europe A detailed report or videos are produced after each workshop Year Place Link Report Comments 2019 Stanford 1 12th XLDB Conference 2018 Stanford 2 11th XLDB Conference 2017 Clermont Ferrand 3 10th XLDB Conference 2016 Stanford 4 9th XLDB Conference 2015 Stanford 5 8th XLDB Conference 2014 Observatorio Nacional Rio de Janeiro 6 Satellite XLDB Workshop in South America 2014 Stony Brook University 7 XLDB Healthcare Workshop 2013 Stanford 8 7th XLDB Conference 2013 CERN Geneva Switzerland 9 Satellite XLDB Workshop in Europe 2012 Stanford 10 11 6th XLDB Conference Workshop amp Tutorials 2012 Beijing China 12 13 Satellite XLDB Conference in Asia 2011 SLAC 14 15 5th XLDB Conference and Workshop 2011 Edinburgh UK 16 not available Satellite XLDB Workshop in Europe 2010 SLAC 17 18 4th XLDB Conference and Workshop 2009 Lyon France 19 20 3rd XLDB Workshop 2008 SLAC 21 22 2nd XLDB Workshop 2007 SLAC 23 24 1st XLDB WorkshopTangible results editXLDB events led to initiating an effort to build a new open source science database called SciDB 4 The XLDB organizers started defining a science benchmark for scientific data management systems called SS DB At XLDB 2012 the XLDB organizers announced that two major databases that support arrays as first class objects MonetDB SciQL and SciDB have formed a working group in conjunction with XLDB This working group is proposing a common syntax provisionally named ArrayQL for manipulating arrays including array creation and query See also editInternational Conference on Very Large Data BasesReferences edit Building the biggest scientific databases symmetry magazine Retrieved 2019 04 15 XLDB Extremely Large Databases 2019 XLDB Extremely Large Databases 2019 Retrieved 2019 04 15 Becla Jacek 2009 XLDB 3 Welcome Retrieved 2009 08 29 Becla Jacek 2008 Report from the SciDB Workshop Retrieved 2008 09 29 permanent dead link Further reading editPavlo A Paulson E Rasin A Abadi D J Dewitt D J Madden S and Stonebraker M A Comparison of Approaches to Large Scale Data Analysis Proceedings of the 2009 ACM SIGMOD https web archive org web 20090611174944 http database cs brown edu sigmod09 benchmarks sigmod09 pdf Becla Jacek Hanushevsky Andrew Nikolaev Sergei Abdulla Ghaleb Szalay Alex Nieto Santisteban Maria Thakar Ani Gray Jim 2006 Designing a multi petabyte database for LSST In Silva David R Doxsey Rodger E eds Observatory Operations Strategies Processes and Systems Vol 6270 pp 62700R arXiv cs 0604112 doi 10 1117 12 671721 S2CID 3204824 Becla J amp Wang D L 2005 Lessons Learned from Managing a Petabyte downloaded from https web archive org web 20110604223735 http www slac stanford edu pubs slacpubs 10750 slac pub 10963 pdf on 2007 11 25 Bell Gordon Gray Jim Szalay Alex 2007 Petascale Computational Systems arXiv cs 0701165 Bibcode 2007cs 1165B a href Template Cite journal html title Template Cite journal cite journal a Cite journal requires journal help Duellmann D 1999 Petabyte Databases ACM SIGMOD Record vol 28 p 506 https web archive org web 20071012015357 http www sigmod org sigmod record issues 9906 index html TutorialSessions Hanushevsky A amp Nowak M 1999 Pursuit of a Scalable High Performance Multi Petabyte Database 16th IEEE Symposium on Mass Storage Systems pp 169 175 http citeseer ist psu edu 217883 html Shiers J Building Very Large Distributed Object Databases downloaded from https web archive org web 20070915101842 http wwwasd web cern ch wwwasd cernlib rd45 papers dbprog html on 2007 11 25 External links editOfficial website XLDBConf s channel on YouTube Retrieved from https en wikipedia org w index php title XLDB amp oldid 1058703776, wikipedia, wiki, book, books, library,
