On challenges with time-stamped data in Siemens Energy Services Restricted Siemens AG All rights reserved

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1 Semantic Days, Stavanger / May 2013 On challenges with time-stamped data in Siemens Energy Services

2 st 2nd 3rd 4th Accessing and understanding Big Data is a vital challenge for many Siemens businesses Visualize & Advice (Semantic) Search Question answering Visual analytics (Context sensitive) Reporting Model & Analyze Knowledge modeling Reasoning Rules / Constraints Mathematical modeling & optimization Natural language processing Data mining Machine learning Engineering Integrate & Manage NoSQL Data warehouse Data stream processing Manufacturing Production Data Sources Unstructured data Integrate & reuse... Structured data Rotating Equipment Operations... along the full value chain... within and across domains Medical Devices Trains Service & Maintenance Page 2 May 2013 Corporate Technology

3 st 2nd 3rd 4th Enabling more users to work with data in less time Visualize & Advice (Semantic) Search Visual analytics Question answering (Context sensitive) Reporting Model & Analyze Knowledge modeling Reasoning Rules / Constraints Mathematical modeling & optimization Less expert time needed Integrate & Manage Natural language processing NoSQL Data mining Machine learning Data warehouse Data stream processing End-user data access as a commodity Data Sources Unstructured data Structured data Top ten failures last 10 weeks Performance trend last 5 years Overall analysis of product line Correlation of fault with burn temp Querying operatorspecific patterns office, standard DBMS, service platforms,... Dedicated tools or costly adaptation of tools for data analytics, data warehousing, visualisation,... weeks weeks weeks days days + data access, data integration, data interpretation Special solutions restricted to use-case specific functionalities Standard Software Special Solutions Page 3 May 2013 Corporate Technology

4 Siemens is organized in 4 Sectors: Industry, Energy, Healthcare and Infrastructure & Cities Siemens: Facts and Figures Siemens sectors Key figures FY 2012 Industry Divisions: Industry Automation Drive Technologies Customer Services Energy Divisions: Fossil Power Generation Wind Power Oil & Gas Energy Service Power Transmission Solar & Hydro 2) Healthcare Divisions: Imaging & Therapy Systems Clinical Products Diagnostics Customer Solutions Infrastructure & Cities Divisions: Rail Systems Mobility & Logistics Low and Medium Voltage Smart Grid Building Technologies Osram 2) Sales: ~ 78 bn. Locations: In 190 countries Employees: ~370,000 R&D expenses: ~ 4.2 bn. R&D engineers: ~29,500 Inventions: ~8,900 Active patents: ~57,300 ~ 21 bn. 1) ~ 28 bn. 1) Corporate functions Corp. Finance Corp. Technology Corp. Development ~ 14 bn. 1) ~ 18 bn. 1) Corporate Technology 1) Sales in FY ) Not included in sales figure Page 4 May 2013 Corporate Technology

5 Siemens is organized in 4 Sectors: Industry, Energy, Healthcare and Infrastructure & Cities The Siemens Optique Team Siemens sectors Industry Energy Healthcare Infrastructure & Cities Trygve Oei Akselsen Jean-Emmanuel Bieber Divisions: Industry Automation Drive Technologies Customer Services Divisions: Fossil Power Generation Wind Power Oil & Gas Energy Service Power Transmission Solar & Hydro 2) Divisions: Imaging & Therapy Systems Clinical Products Diagnostics Customer Solutions Divisions: Rail Systems Mobility & Logistics Low and Medium Voltage Smart Grid Building Technologies Osram 2) Endre Brekke Anthony Latimer Holger Stender Stuart Watson ~ 21 bn. 1) ~ 28 bn. 1) Corporate functions Corp. Finance Corp. Technology Corp. Development ~ 14 bn. 1) ~ 18 bn. 1) Corporate Technology Thomas Hubauer Steffen Lamparter Mikhail Roshchin 1) Sales in FY ) Not included in sales figure Page 5 May 2013 Corporate Technology

6 The Siemens use case: Heterogeneous schemata and parallel streams Energy Service 150 TB Service Center 30GB / 24h Data Center Sensor and Event Data Analytical Data From SCs Other Data DB Thousands DB DB hund reds DB DB hund reds DB Rotating equipement Data Processing and Controlling Infrastructure Data Collector Control Unit Several Thousands Data Collector... Soft Sensor Soft Sensor Sensor 2000 Sensor No unified schema Parallel streams Page 6 May 2013 Corporate Technology

7 Siemens use case data is diverse Sensor/ event data Raw sensor data Pre-processed by soft sensors Pre-processed by soft control units Analytical data data from previous monitoring cases Miscellaneous data Logs Design data of units Customer data (e.g. location) External (e.g. whether condition) Data Center Miscellaneous Data hund reds Sensor and Event Data Thousands Analytical Data From Service Cent. hund reds Page 7 May 2013 Corporate Technology

8 Ultimately, engineers need actionable answers? There is a strange vibration in our turbine. It happened twice in the last two months. Predicted decrease of output by 13%, growth 5% per month. Replace gearbox at next maintenance break (05/27/13) continue work until then.! Analyses Service Ticket Data Access Factory Service Center Data Center UK DE Page 8 May 2013 Corporate Technology

9 Current data access process is slow & expensive Analyses Service Ticket Data Access Information Request Translation Factory Service Center Data Center Specialised UK Query DE up to 2 weeks Engineer IT Expert Data Center Feedback / Answers Data on similar patterns in the last 5 years? ~50 service centers each receiving ~1000 requests / year Known faults for turbines (selected subset from XLS)? You got me wrong, I meant data on!? currently more than 4000 specialized queries Energy Services: > 1 mil. EUR / year (est.) Siemens : > 50 mil. EUR / year (est.) Page 9 May 2013 Corporate Technology

10 Practically, engineers need direct data access Analyses Service Ticket Data Access Information Request Translation Factory Service Center Data Center Specialised UK Query DE up to 2 weeks Engineer IT Expert Data Center Feedback / Answers Optique solution engineer Optique Application flexible,ontology based queries Query translation translated queries disparate sources Data Center timely, complete, and correct results Page 10 May 2013 Corporate Technology

11 > 4000 queries Optique as information middleware in the Siemens O&G services information ecosystem Transplanting the data access heart of The Integrated Technology Platform STA-RMS Top ten failures last 10 weeks Performance trend last 5 years Overall analysis of product line Correlation of fault with burn temp Querying operatorspecific patterns Collection, Transfer, Storage Automated fault detection Real time trouble shooting weeks weeks weeks days days Operational Intelligence / Data analysis Reporting Page 11 May 2013 Corporate Technology

12 Optique as information middleware in the Siemens O&G services information ecosystem Overview of requirements from the Siemens use case Real-time Stream Processing End-user-oriented Query Interface Scalable Query Rewriting Query Evaluation with Elastic Clouds Types & Format Conversions Data Quality Time Synchronization Data Cleaning Bootstrapping & Administration of Data Models & Mappings Support for Query Formulation Expressive Query Language Modeling & Querying Schema & Instance Mapping Language Stream Reasoning Page 12 May 2013 Corporate Technology

13 Heterogeneity is standard... Real-time Stream Processing End-user-oriented Query Interface Scalable Query Rewriting Query Evaluation with Elastic Clouds Challenges with date/time representations in use case data Types & Format Conversions Data Quality Time Synchronization Data Cleaning Bootstrapping & Administration of Data Models & Mappings Support for Query Formulation Stream Reasoning Expressive Query Language Modeling & Querying Schema & Instance Mapping Language Information Request Engineer up to 2 weeks IT Feedback / Answers Page 13 May 2013 Corporate Technology

14 Clocks tick differently... Real-time Stream Processing End-user-oriented Query Interface Scalable Query Rewriting Query Evaluation with Elastic Clouds Challenges with time synchronization between sources Types & Format Conversions Data Quality Time Synchronization Data Cleaning Bootstrapping & Administration of Data Models & Mappings Support for Query Formulation Stream Reasoning Expressive Query Language Modeling & Querying Schema & Instance Mapping Language Local time settings at control unit Difference in measurement & transmission frequency between devices Jitter may lead to differences between measurement time and storage time Page 14 May 2013 Corporate Technology

15 Right way of expressing time is yet unclear Real-time Stream Processing End-user-oriented Query Interface Scalable Query Rewriting Query Evaluation with Elastic Clouds Challenges with modeling and querying temporal information Quantitative temporal data vs. qualitative temporal knowledge Types & Format Conversions Data Quality Time Synchronization Data Cleaning Bootstrapping & Administration of Data Models & Mappings Support for Query Formulation Stream Reasoning Expressive Query Language Modeling & Querying Schema & Instance Mapping Language Representation & semantics of time needed: valid time vs. timestamp? Intervals?? Temporal query operators Granularity of time? Sequences? Trends? Outliers? Around Information Request 2pm? Translation Specialised Qu Engineer up to 2 weeks IT Expert Page 15 May 2013 Corporate Technology

16 SPARQL is not for turbine engineers Real-time Stream Processing End-user-oriented Query Interface Scalable Query Rewriting Query Evaluation with Elastic Clouds Challenges with bringing direct access to end users Tip temp? [5;10] time! Types & Format Conversions Data Quality Time Synchronization Data Cleaning Bootstrapping & Administration of Data Models & Mappings Support for Query Formulation Stream Reasoning Expressive Query Language Modeling & Querying Schema & Instance Mapping Language Trans Stratigraphic Layers (800) Information Request Top Depth m to m Bottom Depth m to m RECOMMENDATIONS Stratigraphic Units (200) S. Layers correspond to S. Units. and CUSTOMIZATION Engineer Cores (900) S. Layers provide Cores. up to 2 weeks Wellbores (300) S. Layers belong to Wellbores. IT Expert Query Diagram Query Text SELECT turbine_id, loc_lat, loc_lon, FROM turbines_de UNION JOIN maintenance m1, m2 ON WHERE m1.ts < m2.ts AND??? Wellbore Core Feedback / Answers Stratigraphic Layer Results Page 16 May 2013 Corporate Technology

17 Users want complete answers quickly Real-time Stream Processing End-user-oriented Query Interface Scalable Query Rewriting Query Evaluation with Elastic Clouds Optique architecture Types & Format Conversions Data Quality Time Synchronization Data Cleaning Bootstrapping & Administration of Data Models & Mappings Support for Query Formulation Expressive Query Language Modeling & Querying Schema & Instance Mapping Language Stream Reasoning Seconds to minutes Incremental What-if analyses Page 17 May 2013 Corporate Technology

18 Raising the bar year by year The Siemens use case comprises three facets Product Engineering and Maintenance Support Reactive and Preventive Diagnostics Intelligent access to historical sensor and event data (Near) natural-language querying Built-in statistical operators Continuous querying of temporal patterns over real-time streams Declarative, model-based approach Predictive Analytics Integration of additional information (e.g. product line specifications) Univariate time series analytics Analysis of periodic patterns Trend analysis and prognostics (including KPIs & risk calculation) Automated integration of diagnostics results into service scheduling (e.g. rescheduling) Multivariate time-series analysis Correlation analysis Data mining for pattern discovery Decision support based on product-specific shop floor analysis Cluster-analysis (e.g. based on machine, component, productline) Extraction of baseline-models for characteristic features Page 18 May 2013 Corporate Technology

19 Thomas Hubauer Research Scientist CT RTC BAM KMR-DE Otto-Hahn-Ring Munich Germany Phone: +49 (89) Fax: +49 (89) Mobile: +49 (173) thomas.hubauer@siemens.com siemens.com/innovation Page 19 May 2013 Corporate Technology