Overview of state of art in Data management. Stefano Cozzini CNR/IOM and exact lab srl
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1 Overview of state of art in Data management Stefano Cozzini CNR/IOM and exact lab srl
2 AIM of this short talk Frame the problem and the discussion around DATA: What are big data? Which kind of challenges ahead of us? Disclaimer: Slides and numbers are taken around: there are a lot of data discussing big data J
3 Big Data: a buzzword..
4 Big Data: general definikon
5 The 3 V s of big data.. Velocity Data are produced at speed higher than the speed you are able to move/analyze and understand them.. Variety Data range from simulakon to remote sensing informakon, from instruments to market analysis etc.. datasets come in a variety of data formats and span a variety of metadata standards Volume The amount of data will increase of factor 61 in the next 10 years The amount of data is eskmated to exceed the size of available data infrastructure to store them by 60%. [from The 2011 IDC Digital Universe Study]
6 Data- intensive science A fourth paradigm a[er experiment, theory, and computakon.. It involves colleckng, exploring, visualizing, combining, subse\ng, analyzing, and using huge data colleckons Scientific Data Infrastructure 6 4
7 Challenges & Requirements Challenges: Deluge of observakonal data, exaflood of simulakon model outputs Need for collaborakon among groups, disciplines, communikes Finding insights and discoveries in a Sea of Data Requirements: New tools, techniques, and infrastructure Standards for interoperability InsKtuKonal support for data stewardship, curakon
8 IOPS vs FLOPS HPC is today compute- centric scienkfic compukng needs data accessibility rather than compukng speed Source: IDC DirecKon 2013
9 Roles in Data- intensive Science Scien&sts/researchers: acquire, generate, analyze, check, organize, format, document, share, publish research data Data users: access, understand, integrate, visualize, analyze, subset, and combine data Data scien&sts: develop infrastructure, standards, convenkons, frameworks, data models, Web- based technologies So2ware developers: develop tools, formats, interfaces, libraries, services Data curators: preserve data content and integrity of science data and metadata in archives Research funding agencies, professional socie&es, governments: encourage free and open access to research data, advocate eliminakon of most access restrickons Scientific Data Infrastructure 9
10 Infrastructure for sharing scienkfic ApplicaKons depend on lower layers Sharing requires agreements formats protocols convenkons Data needs metadata Data Scientific Data Infrastructure 10 6
11 Data infrastructure crucial aspects Large datasets: Deal with large datasets and large data rates from experiments Reliability : Increase the level of QoS and SLA, e.g. increasing reliability by replicakng data sources and increasing accessibility by copying source to several places AccounKng Allow monitoring and checking of resource usage IntegraKon Provide the same set of services that are understandable (compakble) between domains InteroperaKon InteroperaKon through common standard schemes Access Broadband data access Allow transparent and secure remote access to data Data preservakon Allow long- term availability of data High quality Quality of data to enable advanced and cross- disciplinary access and enrichment operakons Economic juskficakon As the scienkfic community is operakng on increasingly larger datasets and want to preserve the informakon concerned, the infrastructure provided should have a clear roadmap of technology exchange and backwards compakbility. Access control Provide the infrastructure to allow for fine- grained access control Source: e- irg blue paper on data management 2012 hhp:// irg.eu/images/stories/disseminakon/e- irg- blue_paper_on_data_management_v_final.pdf
12 Data are not just for science.. High- end commercial analykcs pushing up into academia. The journey from science data to industry& commerce can be relakvely short..and plenty of ethical legal and societal implicakons
13
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