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Big Data Technologies & Applications

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Author: Sebnem Rusitschka, Siemens AG
Presented at: BYTE 1st Workshop - Lyon, 11 September 2014

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Big Data Technologies & Applications

  1. 1. EU BYTE 1st Workshop - Lyon, 11 September 2014 Big Data Technologies & Applications Restricted © Siemens AG 2014. All rights reserved Sebnem Rusitschka Siemens AG
  2. 2. Restricted © Siemens AG 2014. All rights reserved Big Data Technologies & Applications Overview  The Evolution of Big Data Technologies  Analytics & Big Data Applications  Emerging Big Data Needs & Trends  Key Take Aways in Panel Discussion  Detailed analyses see http://byte-project.eu/ 2014-09-11 Sebnem Rusitschka Siemens AG
  3. 3. Innovations in distributed storage and computing enable cost-effective handling of the 3 Vs Unrestricted © Siemens AG 2014. All rights reserved A short history of Big Data Technologies 3 2014-09-11 Sebnem Rusitschka Siemens AG
  4. 4. 2013 brought about a common understanding that technologies are there to query all your data Unrestricted © Siemens AG 2014. All rights reserved The “Lambda Architecture” introduced by Nathan Marz 4 2014-09-11 Sebnem Rusitschka Siemens AG
  5. 5. Cost-effective handling of analytics will foster advancing analytical capabilities of businesses What will happen? What shall we do? Unrestricted © Siemens AG 2014. All rights reserved Value and Complexity Inform Analyze Act Descriptive What happened? Examples • Plant operation report • Fault report Why did it happen? Current penetration across all industries (according to Gartner 2013) Adopt d by vast majority 99% Diagnostic • Alarm management • Root cause identification Adopted by minorities 30% Predictive • Power consumption prediction • Fault prediction Still few adopters 13% Prescriptive • Operation point optimization • Load balancing Very few early adopters 3% 2014-09-11 Sebnem Rusitschka Siemens AG
  6. 6. Industry Applications Example: Real-time prescriptive analytics for gas turbines Benefits • Improved turbine ramp-up with less vibrations (lower maintenance needs) • Reduced NOx Emissions • Increase of turbine efficiency in operations • Guiding turbine development process in planning Unrestricted © Siemens AG 2014. All rights reserved Streaming Data: ca. 5,000 variables / s Input data and model results Complete Data and Dependency Analysis plus Learning Optimization Modules Real-time Data Analysis (1,000 Neural Models) Source: Siemens AG 2014-09-11 Sebnem Rusitschka Siemens AG
  7. 7. There is a trade-off between enhancing interpretability of data and preserving privacy & confidentiality Increasing Importance of Data Interpretability  Semantic heterogeneity due to variety of data/description owners: Over 60 % of all Linked Open  EU Optique: aims at giving end users scalable semantic access to Big Data, e.g. by inferring and (semi-) automating semantic linkage of data, correlations, and knowledge. Increasing Importance of Security, Legal, Social Aspects  Big Data Analytics circumvents anonymization: 4 spatio-temporal points, approximate places and times, are enough to uniquely identify 95% of 1.5M people in a mobility database with metadata 2)  EU BYTE: taking European Big Data technology roadmaps to the next level by focusing on maximizing positive and diminishing negative externalities, by analyzing sustainable business models 1) V. Christophides, “Web Data Management: A Short Introduction to Data Science”, Lecture Notes, Spring 2013, p. 15, 2) de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel (March 25, 2013). "Unique in the Crowd: The privacy Unrestricted © Siemens AG 2014. All rights reserved Emerging Big Data Needs and Trends (1/2) Data use proprietary vocabulary 1) http://www.csd.uoc.gr/~hy561/Lectures13/CS561Intro13.pdf bounds of human mobility". Nature srep. doi:10.1038/srep01376. 7 2014-09-11 Sebnem Rusitschka Siemens AG
  8. 8. Analytics needs to better blend with available and emerging big data computing Challenge Need  Analytics becomes part of each step of the data refinery pipeline, e.g. by  detecting and remedying data quality issues at acquisition time  analyzing effective use and untapped potentials in data usage  big data storage & computing to enable ease of use for data scientists  analytics workflows & management to enable ease of use for business users 1) Paradigm 4, “Leaving Data on the Table”, Survey, 1 July 2014. http://www.paradigm4.com/wp-content/uploads/2014/06/P4PR07012014.pdf Unrestricted © Siemens AG 2014. All rights reserved Emerging Big Data Needs and Trends (2/2) Although 49 % of the data scientist could not fit their data into relational databases anymore: only 48 % have had used Hadoop or Spark 76 % of those could not work effectively 1) The Evolution from Query Engine to Analytics Engine  Abstraction from underlying 8 2014-09-11 Sebnem Rusitschka Siemens AG
  9. 9. Looking forward to questions & feedback! Unrestricted © Siemens AG 2014. All rights reserved Contact Sebnem Rusitschka Senior Key Expert Prescriptive Analytics & In-field Applications Siemens AG Corporate Technology Business Analytics & Monitoring Otto-Hahn-Ring 6 D-81379 Munich Phone: +49 (89) 636-44127 Fax: +49 (89) 636-41423 Mobile: +49 (172) 357 59 35 E-mail: sebnem.rusitschka@siemens.com siemens.com/innovation
  10. 10. Unrestricted © Siemens AG 10 2014-09-11 Sebnem Rusitschka Siemens AG 2014. All rights reserved

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