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IBM Smarter Business 2012 - Big Data Analytics
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IBM Smarter Business 2012 - Big Data Analytics

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Det finns en enorm potential i analys, förädling, modellering och åskådliggörande av de enorma datamängder som genomsyrar såväl näringsliv som samhälle. För att realisera denna potential räcker det …

Det finns en enorm potential i analys, förädling, modellering och åskådliggörande av de enorma datamängder som genomsyrar såväl näringsliv som samhälle. För att realisera denna potential räcker det inte med kapacitet att lagra, förmedla och söka igenom data. Till det behövs istället Big Data Analytics, storskalig analys av enorma datamängder. Vi presenterar här den forskningsagenda för Big Data Analytics som SICS tillsammans med IBM och ytterligare parter från industri och akademi håller på att ta fram.

Talare: Daniel Gillberg, Research Group Leader, SICS, Anders Holst Senior Research Scientist, SICS, samt Flemming Bagger, IBM

Besök http://smarterbusiness.se för mer information.

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  • An enormous amounts of data permeate societyBoth the data itself and how it is usedDeeper analysis of audio and video
  • * A move from instance based to model based approaches
  • Transcript

    • 1. Big Data Analytics– Challenge and Opportunity
    • 2. 83x6,000,000 users on Twitter 500,000,000 users on Twitter pushing out 300,000 pushing out 400,000,000 tweets per day tweets per day 1333x
    • 3. Where is big data coming from? 4.6 30 billion RFID billion 12+ TBs tags today camera of tweet data (1.3B in 2005) phones every day world wide 100s of millions data every? TBs of of GPS day enabled devices sold 25+ TBs annually of 2+ log data billion every day people 76 million smart meters on the in 2009… 200M by 2014 Web by end 2011
    • 4. The Characteristics of Big DataCost efficiently Responding to the Collectively analyzingprocessing the increasing Velocity the broadening Varietygrowing Volume 50x 35 ZB 30 Billion RFID 80% of the sensors and worlds data is counting unstructured 2010 2020 Establishing the By 2015, 80% of all available data will be uncertain - The number of networked devices will be double the entire Veracity of big global population data sources - The total number of social media accounts exceeds the entire global population
    • 5. Big Data is a Hot topic- Because it is possible to Analyze ALL Available Data• The percentage of available data an enterprise can analyze is decreasing proportionately to the available to that enterprise– Quite simply, this means as enterprises, we are getting ―more naive‖ about our business over time• Just collecting and storing “Big Data” doesn’t drive a cent of value to an organization’s bottom line• Cost effectively manage and analyze ALL available data in its native form unstructured, structured, realtime streaming…….Internal and external Data AVAILABLE to an organization Data an organization can PROCESS
    • 6. Business-centric Big Data Platform • ―Big data‖ isn’t just a technology —it’s a business strategy for capitalizing on information resources • Getting started is crucial • Success at each entry point is accelerated by products within the Big Data platform • Build the foundation for future requirements by expanding further into the big data platform6
    • 7. Different data workloads have different characteristics Database services that handle large volumes of transactions with System for Transactions high availability, scalability and integrity Data Warehouse services for System for Analytics complex analytics and reporting powered by on data up to petabyte scale - Netezza technology with minimal administration Operational Warehouse services for continuous ingest of operational data, complex analytics, and System for a large volume Operational Analytics of concurrent operational queries
    • 8. Big Data Analytics – A national researchinitiative
    • 9. Big Data Analytics – A national research initiativeDaniel GillbladResearch Group Leader, Senior Research ScientistSICS, Swedish Institute of Computer Science
    • 10. Background• There is a very large potential, both societal and commercial, in the analysis, refinement, modeling, and visualization these data sets• Capacity to store, transfer, and search is not enough - analytics is critical
    • 11. Additional business value of Analytics• Predict and optimize business outcomes• New services and applications, both for end-users and industry• New value chains, were different actors can create and exchange new analysis services
    • 12. A national Big Data Analytics initiative① A strategic nation-wide research and innovation agenda – Input from several sectors and application areas – Both new businesses built on analytics applications and traditional industry – Input from academia, both as developers and as users② A national Big Data Analytics network – Open to all interested parties – Industry and academia with an active interest in Big Data Analytics
    • 13. Focus areas Control and planning Visualization Focus areas { Analytics Computation Storage Collection
    • 14. Current constellation
    • 15. Research and development challenges• Huge businesses are built on Big Data Analytics today, but a large number of issues must be resolved to fully realize the potential• Three examples
    • 16. Example 1: Large-scale physics experimentation• Challenges: Scale (storage, computation), scalable analytics
    • 17. Example 2: Social network mining• Challenges: Unstructured data, biased data, data access
    • 18. Example 3: Access network pattern mining• Challenges: Integrity issues, distributed mining, service frameworks
    • 19. Long term trends• Currently dominating approach will continue to be successful, but will be complemented due to – Too much data, unstructured data, noisy data – Limited access – security, integrity, legal, and business – Fast data generation, situation awareness• The consequences are – Analysis closer to data generation / collection – No storage - Catching information on the fly – Distributed analysis with incomplete data – Real time collection, real time analytics
    • 20. Research challenges• Research challenges on different levels: – The sensor/collection level – The algorithmic/analytical level – The system level – The organisational level
    • 21. Technical challenges, examples• Computational and storage framework development• Analysis of unstructured data• Distributed analysis• Efficient analysis algorithms• Stream mining• Managing sample bias• Managing uncertain and missing data
    • 22. Platform and organisational challenges, examples• Service and analytics frameworks, exchanging models and data• API:s and standards• Privacy, integrity, security, and legal• Business models
    • 23. Contacts• If you are interested in the Swedish Big Data Analytics Network, feel free to contact Daniel Gillblad Anders Holst dgi@sics.se aho@sics.se +46 8 633 15 68 +46 8 633 15 93

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