The IoT/CPS Big Data Challenge

Transcription

1 The IoT/CPS Big Data Challenge Stamatis Karnouskos SAP Road4FAME EU-Consultation Meeting, 22 May 2015, Brussels, Belgium Data acquisition becoming easy, finegrained, real-time, low-cost 0 sync (msec) Implicit Data Generation Automated Capturing & selected sync 2 1

2 Machine to Business Integration 3 Towards High-Performing Complex Systems of Systems Large-scale Management / Control 4.Enforce 1.Monitor / Act Future (Enterprise) System of Systems and beyond SmartCity, SmartGrid, Factory of the Future 3.Decide 2.Analysis / Prediction Main IoT/CPS focus Automated decision Big Data Insights 4 2

3 Focus of IoT/CPS Efforts so far Enabling Devices to participate in advanced infrastructures Integration with devices / systems / services Migration aspects Embedding intelligence in devices Data Collection Interfacing with traditional tools/systems/services Visualization of Data Simplistic Processing (e.g. missing data, aggregation, etc.) Some operational analytics, predictive analytics the investment has been on infrastructure buildup/modernization 5 But Data silos exist (explicitly or implicitly e.g. due to technology/access/semantics etc.) Specific Data is collected in order to be evaluated for specific purposes limited usage outside that purpose; additional stuff is discarded/not shared ( now new insights). Access to data meaningful utilization of it Incremental benefits have been realized, but still in an evolutionary manner from existing approaches Decisions are partially enhanced but only at high level Large scale optimizations are still work in progress Automated decision making still in early stages for large systems and system-ofsystems Governance of Data (incl. security, trust, privacy, lifecycle management etc.) and the potential derived knowledge at early stages there is still room for significant advances (we are still at the dawn of the new era) 6 3

4 How to benefit from the IoT/CPS? source: Internet graphic Think differently acquire new insights & make these insights available to non-experts. 7 What is next? Understand and Capitalize on IoT Data Enabling: data preparation, iterative modeling & analysis, experimentation in lab (understanding, innovating ) vs. productive/operational use (SLA, performance, accuracy, uptime ) in factory Process Big Data and transform it to knowledge Understand complex systems/behaviors Understand not directly measured elements Experiment with new models / algorithms / techniques Take advantage of state of the art concepts/technologies/tools (incl. Data Science, Data Mining, Pattern Recognition, Machine Learning, ) Data lifecycle, governance & sharing management Automated Decision & Management Information/Knowledge-Driven Decision Making Real-Time Big Data CPS/IoT Analytics combined with Real-time control/management Tangible business benefits (e.g. optimization, new revenue streams, etc.) 8 4

5 An IoT/CPS focus area view Next Generation Knowledge-Driven Game Changer: Analytics on IoT/CPS Big Data + Creation of Actionable Knowledge Maturing Area & Expansion to Large Scale Apps, Services, Tools, Systems Visualization Decision Support Simulation Data Analytics Data Management Data Acquisition Device Integration Provision of tools, algorithms, models, methodologies etc. Take advantage of modern technologies and realize new approaches e.g. not possible 5 years ago. Empower non-experts to take advantage of data analytics derived actionable knowledge 9 From CPS/IoT Data to Information / Knowledge- Driven Integration & Collaboration Apps, Services, Tools, Systems Data-driven focus Today Information/Know ledge-driven focus Tomorrow Visualization Decision Support Simulation Data Analytics Data Management Data Acquisition Device Integration M2B: Machine 2 Business M2M: Machine 2 Machine M2M, M2B, B2M System of Systems Data/Info/Knowledge governance 10 5

6 Based-Cyber-Physical-Systems-IMC- AESOP/dp/ / Thank You Q&A Stamatis Karnouskos Machine-Internet-Things/dp/ X/ SAP Vincenz-Priessnitz-Strasse 1 D Karlsruhe, Germany stamatis.karnouskos@sap.com 11 6