oraclevoice_ cern tests data exploration using big data
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9/12/2016 OracleVoice: CERN Tests Data Exploration Using Big Data, Analytics, And The Cloud
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CERN Tests DataExploration Using BigData, Analytics, AndThe Cloud
For more than half a century, scientists atthe CERN particle physics laboratory(more formally, the EuropeanOrganization for Nuclear Research) havebeen probing the inner workings of theuniverse. Today, the organization, whichwas the birthplace of the World WideWeb, is assessing the potential of thecloud to make its research ITinfrastructure more scalable andeconomical to operate.
Since 2008, scientists from around theglobe have been using the largest andmost powerful particle accelerator in theworld, CERN’s Large Hadron Collider(LHC), to create conditions similar to theBig Bang, and from their studies, gain abetter understanding of how our universeworks.
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Scientists at the CERN particle physics laboratory have been usingthe world’s most powerful particle accelerator to better understandhow our universe works. (Photo courtesy CERN openlab.)
Just four years after the LHC wasinaugurated, researchers discovered theHiggs boson, previously the last unverifiedpiece of what is known as the StandardModel of particle physics. Nevertheless,the standard model explains only about5% of how the universe actually works.Thus, as well as working to better definethe characteristics of the Higgs boson,CERN and the worldwide particle physicsresearch community are now hoping tomake new discoveries that may shed somelight on the rest.
In order to achieve this, researchers areanalyzing huge numbers of subatomicparticle collisions, which create manypetabytes of raw data. Over the comingyears, this analysis will require morecomputing power and storage capacitythan CERN’s data centers—and budget—can currently handle.
CERN’s research is largely funded by 22member countries, which togethercontribute roughly $1.1 billion annually.The proportion of this dedicated to IT hasbeen largely flat for the past 10 years.
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Together, the LHC experiments producemore than 30 petabytes of data per year.CERN’s Data Centre (including its remoteextension in Budapest, Hungary) provides250 petabytes of disk storage space andaround 200,000 computing cores.Analysis of the physics data is madepossible by a global network of more than170 computer centers known as theWorldwide LHC Computing Grid. Eachday, more than 2 million jobs run on thisnetwork.
“Over the next 10 to 15 years, we expectthe total amount of data produced by theexperiments on the LHC to increasesignificantly,” says Alberto Di Meglio,head of CERN openlab, a publicprivatepartnership that accelerates thedevelopment of cuttingedge solutions forthe worldwide LHC community and widerscientific research network. Through thisinitiative, CERN collaborates with leadingICT companies and research institutes.
And that’s just the data coming from theLHC. A successor to the LHC, known asthe Future Circular Collider (FCC), isbeing studied. It would be around fourtimes larger and produce even more datastill.
In addition to managing its own onsitedata centers, Di Meglio believes CERN canefficiently pursue a hybrid model:
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procuring computing and storage servicesasneeded via the cloud. “The cloud isbecoming an increasingly importantcomponent of our researchinfrastructure,” says Di Meglio. However,managing, storing and analyzing data inthe cloud on the scale required by CERNcan be very complex.
CERN openlab Uses Big DataDiscovery
CERN is also working with Oracle BigData Discovery to see how it can be usedto more efficiently and intelligentlyanalyze technical engineering informationproduced by approximately 50,000sensors and other metering devices thecenter uses to capture operational datafrom its accelerator complex. The data isanalyzed to ensure these accelerators areoperating at their full potential—and ifnot, identify what resources are needed sothat they do.
Related: Find more on Oracle BigData Discovery
Using the reliability and simulation toolsthat are built into the Oracle Big DataDiscovery platform, the CERN openlabteam is able to correlate fault conditions
Big Data Discovery Helps CER...
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related to electricity consumption, powerconversion, water usage, and cryogenics.
“These correlations are particularlyimportant because we are conducting avariety of analyses for acceleratorconditions and modes such as coolingdown, warming up, and injecting beams ofenergy,” says Johannes Gutleber, seniorengineer at CERN in charge of acceleratorreliability and availability assessment.
“Previously, we had a plethora ofhomegrown custom and thirdpartyapplications that partially stored customfiles or dumped them into a relationaldatabase management system. While thiswas a great way to capture the data,getting it out to analyze it was acompletely different story,” says Gutleber.
Reliability Analysis and CloudBasedDisaster Recovery
CERN can determine which combinationsof investments in infrastructure andtechnical systems would result in the mostbeneficial outcomes for physics research.
“We are now using Oracle Big DataDiscovery to set up an architecture for thereliability and availability analysis of thesystems within the proposed FCCaccelerator complex. Ultimately we arelooking to build a dashboard where we canlook at the entire machine complex anddetermine the extent to which introducingfault tolerances and upgradingaccelerators would improve reliabilitywithout breaking the budget,” saysGutleber.
The CERN openlab team is also testingOracle Database Cloud to understand how
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it can be used as a disaster recoverysolution.
“While we are just beginning to testdisaster recovery in the cloud, we believe itwill prove to be a powerful and costeffective solution for data security andbusiness continuity. We have been pleasedwith our initial investigations with theOracle Database Backup Cloud Serviceand the Oracle Java Cloud Servicesolutions, and look forward to continuingour investigations with Oracle DatabaseCloud,” says Eric Grancher, group leaderof database services in the CERN ITDepartment and CERN coordinator forthe collaboration with Oracle throughCERN openlab.
Related: Oracle Database Cloud
Based on testing carried out at thelaboratory, Manuel Martin Marquez, aCERN data scientist, believes that thevisual interface of Oracle Big DataDiscovery has the potential to help theresearch scientists—most of whom aren’tdata scientists—extract and interpret datafrom the Hadoop platform. They couldthen easily create analytics applicationsand share their findings, says Marquez.
“Essentially, Oracle Big Data Discoverycan make life easier for the users by doingthe dirty work for them,” says Marquez.
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Wringing value from big data is becomingeasier, thanks to muchimproved softwaretoolkits that could potentially ease thepentup demand for hardcore datascientists.
In our datadriven world, that progresscouldn’t come fast enough. A quick searchfor data scientist jobs on LinkedIn netsmore than 30,000 positions available inthe US alone. Those openings call for threedistinct skillsets:
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The algorithm expert who can buildhypothesis and statistical models,frequently hired by startups and marketingagencies.
It’s a rare person who has all three ofthese abilities, yet a number of jobopenings require all of the above. But asbig data tools get easier to use,organizations will have less need for thehighly technical (and now scarce) datascientist, says Jeff Pollock, Oracle vicepresident of product management.
Source: iStockphoto
In the early days of big data, algorithmshad to run serially in R, a languagedesigned for statistical analysis, and thenthe data had to be loaded into Hadoop forfurther work, which meant heavy lifting onthe I/O side of things as well.
“Now we’re seeing a lot more of the mostpopular R algorithms being ported to runin parallelized big data environments, soyou no longer have to do this I/O work,”Pollock says. “You’re actually bringing thealgorithms to the data instead of bringingthe data to the algorithms.”
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That also means organizations can split upcomplex statistical analysis into manysmaller projects and run them at the sametime.
“The tools didn’t exist just a few years ago,so you had to build custom statisticalmodels or write your own program,basically build things from the groundup,” he says. Which is why the demand fordata scientists began to explode.
Oh, Say Can You See
Another big change is the advent ofvisualization tools (such as Oracle BigData Discovery) that make it possible fornontechnical business people to analyzemountains of unstructured data inHadoop as well as structured data in adata warehouse. The user isn’t even awareof the statistical models and machinelearning libraries being used to parse thedata and populate the graphics on thescreen.
“Business users are used to reporting toolsthat are great when you know thequestions you need to ask, and you need tosee your daily reports or your weeklyreports about those questions,” Pollocksays. “But a discovery tool is basicallywhat a data scientist would use when it’snot clear what questions to even ask.”
So if an inventory manager wants to findout which products sell better based onseasonal patterns or weather events, a
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discovery tool can show data correlationsthat nobody would have ever thought toask about. One early example wasWalmart’s discovery that strawberry PopTarts are a topselling item when peopleare preparing for a hurricane. Withoutdata discovery and statistical analysis, whowould have ever thought to query aboutthat correlation?
Now, easytouse discovery tools makethis kind of pattern analysis available toordinary business users—no PhD inmathematics or statistics required.
Shift Happens
Will the demand for data scientists waneas big data technology and tools mature?Consider two recent analyst reports:
Gartner finds that more than 75% ofcompanies are investing or planning toinvest in big data analysis in the next twoyears, and business unit heads rather thanCIOs will initiate almost half of thoseprojects.
Ovum predicts that the big data softwaresector will grow nearly sixfold by 2019.“The experimental era of big data iscoming to an end,” says Tom Pringle,Ovum practice leader and coauthor of thereport. “Organizations are formalizingtheir use of big data technology to realizethe business value they expect to find.”
While there will always be a role formathematicians and statisticians in thisdatadriven business environment,Pollock says, “what they’ll be focused on isbuilding out the heavy predictive modelinginstead of having to be Hadoopsuperheroes.”
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