Issue 72, March 2016
bulletBig Data
bulletInnovation: Siemens' Forecasting Model for Data-Driven Rail Systems
bulletSAP Leverages Big Data to Clear Traffic Jams
bulletInterview with Big Data Expert Prof. Dr. Katharina Morik
bulletgreen spin: Harnessing the Power of Satellite Data for Precision Farming
bulletEvent Review: Big Data - Small Devices
Big Data 
"The Sexiest Job of the 21st Century," according to the Harvard Business Review, is the data scientist. "Think of Big Data as an epic wave gathering now, starting to crest," the article reads. "If you want to catch it, you need people who can surf."

As demand races ahead of supply, the shortage of data scientists is becoming a serious issue for many industries. Estimates indicate that by 2018, the U.S. could be short 140,000 to 190,000 employees possessing deep analytical skills. This is on top of the projected deficit of 1.5 million managers and analysts with the data analysis skills required to make effective decisions.

Recent articles in the New York Times, such as "How Not to Drown in Numbers" and "As Data Deluge Grows, Companies Rethink Storage," highlight some of the challenges posed by the overwhelming proliferation of data today - fueled in part by emerging areas like cloud computing, the Internet of Things (IoT), and Cyber-Physical Systems (CPS).

Future success in the digital economy, however, will be largely contingent on an organization's ability to curate, manage, and leverage this Big Data from a variety of platforms and sources. Furthermore, businesses will increasingly require real-time situational awareness in order to foster new waves of productivity and innovation.

With revenue from Big Data solutions projected to exceed 50 billion euros in 2017, the German government has recognized the significance of this field by tapping into the talents of data scientists through its program "Smart Data - Data Innovations." Established by the German Federal Ministry for Economic Affairs and Energy (BMWi), this program aims to promote 13 innovative projects in industry, mobility, energy, and health as a means of opening up future markets for smart data technologies in Germany.   

When was the last time you experienced inconvenient train delays? Do you wish that railway companies could do a better job of predicting when breakdowns will occur?

Whereas in the past, maintenance traditionally consisted of checking rail vehicles at designated operating centers on a regular basis to resolve obvious problems and maintain machines, digital technologies are opening the door to a new level of service.

Siemens' Allach locomotive plant on the outskirts of Munich is a high-tech hub for train-related data analysis, which has been using special algorithms to predict when potential breakdowns may occur. Since 2014, it has been home to Siemens' Mobility Data Services Center (MDS) where experts have been working hand-in-hand with the facility's Rail Service Center to translate complex streams of mobility-related data into optimized operations.

The data streams from locomotives, high-speed trains, and local trains from Europe and other non-European countries converge at the MDS Center. By drawing upon this data, the organization's 20 programmers, database experts, and implementation managers have developed a data-driven service offering that is unrivaled in the rail sector in terms of real-time train monitoring, forecasting of wear and tear and failure of components, and analysis of complex vehicle problems.

The result: Before a rail vehicle rolls into Siemens' Service Center, its technicians already know what needs to be done, thus keeping the maximum number of trains available for use.

For more information, click here.

Source & Image: Siemens

There's nothing quite as frustrating as sitting in stop-and-go traffic at rush hour. Unfortunately, many cities are experiencing a rapid growth in traffic caused by increasing urbanization and a rising interest in an urban lifestyle. This creates numerous challenges, including greater amounts of traffic and more jams which, in turn, increase the potential for accidents. Passenger transport in Germany alone has grown 20 percent since 1997 - to roughly 50 million cars on the road daily. The situation in other countries such as China, however, is far more extreme.

Due to these developments, reliable and sustainable solutions are urgently needed. Thus, the SAP Innovation Center Network is cooperating with local traffic authorities in urban areas to help them better cope with the growing strains on public and private transportation systems by planning and optimizing cities' traffic patterns.

A Custom Development Project signed in June 2015 between SAP and the city of Nanjing showcases SAP's ability to build a revolutionary Smart Traffic platform. Currently, over 20 billion heterogeneous sensor data per year are generated from vehicles in Nanjing. The challenge that exists is how to homogenize this Big Data to derive value from it and how to answer questions like: How many people are using public transportation right now? How high is the traffic volume downtown? Which events are impacting traffic at the moment? To find answers to these questions is where SAP HANA and its Big Data processing capabilities come into play.

A traffic performance index system, for example, helps the city of Nanjing set up quantitative measurement key performance indicators (KPIs) on live city traffic, thereby offering support in traffic planning processes. This helps proactively prevent potential congestion.

Other Smart Traffic solutions, like electronic tolling, parking guidance and reservation systems, navigation support, and real-time traffic updates, help users understand the as-is situation including the city's dynamic travel patterns to determine and eventually mitigate impacts, such as congestion or car accidents. All of this information flows seamlessly into one digital map that creates a holistic view on the situation on the spot and generates recommendations for traffic planning.  

Source: SAP Innovation Center Network
Image: Tetra Images


Prof. Dr. Katharina Morik recently spoke at the GCRI's "Big Data - Small Devices" event on the impact and challenges of using distributed computational power and data. In her talk, she discussed resource-aware data science and presented the smartphone as a resource-restricted system, drawing upon studies of thousands of app usages. She also described logistics hardware made by her Collaborative Research Center SFB 876 and explained resource-restricted machine learning. 

Since 2011, she has been leading SFB 876 on resource-aware data analysis, an interdisciplinary center comprising 14 projects, 20 professors, and about 50 Ph.D. students or postdocs. She also serves as Professor of Computer Science and Chair of the Artificial Intelligence Group at TU Dortmund University.

In her interview with GCRI, Prof. Dr. Morik discusses how she defines Big Data and how it will transform society in the future. She addresses the key focus areas of SFB 876 and describes some of her current projects. Lastly, she shares her thoughts on what will be the most significant changes in Big Data over the next decade. To read the full interview, click here.

Prof. Dr. Morik earned her Ph.D. in 1981 from the University of Hamburg and her Habilitation in 1988 from the TU Berlin. Starting with natural language processing, her interests transitioned to machine learning, ranging from inductive logic programming to statistical learning and then to the analysis of very large data collections, high-dimensional data, and resource awareness. She is a member of acatech - the National Academy of Sciences and Engineering as well as the author of more than 200 papers in well-acknowledged conferences and journals.

When was the last time you heard the words "farmer" and "Big Data" in the same sentence? Scientific and technological advances are opening up a whole new world of possibilities for the modern farmer - with data-based solutions enabling more efficient agricultural management.

The Big Data analytics firm green spin, coming out of Würzburg, Germany, is an agri-tech start-up that analyzes satellite data to improve farmers' harvest. Its engineers also specialize in analytics for climate records and soil data. Its team is composed of professionals who are not only experts in the automation of data processing and crop modeling, but also in the generation of user-centric and easy-to-use products. All of the company's ideas and products are based on freely available global data sets.

green spin offers a simple and practical web-based, precision agriculture tool called mofato to help farmers cultivate their fields more efficiently and sustainably. This tool helps farmers implement variable-rate application maps for fertilization and plan soil sampling campaigns based on crop yield zone maps.

With mofato, the company helps farmers save time while cultivating individual zones of their fields with improved cost efficiency. The fully automated Big Data analytics and cloud-based objective database seamlessly form the backbone of the system. With just the click of a button, farmers can implement precision agriculture methods. Up to 14 years of field-based data can also be analyzed to help users make the best decisions. The product is currently available for free in Germany; green spin is working to extend its offerings to other countries in the future.

Founded in 2013, green spin has received several awards from the European Space Agency (ESA) and the German government. It was a prizewinner in the 2014 Germany Land of Ideas competition, recipient of the 2014 "Hochschulgründerpreis" (Collegiate Founder's Prize), and among the top ten teams in Northern Bavaria's 2014 Business Plan Competition. green spin is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi), Europäischer Sozialfunds für Deutschland (ESF), European Union, Existenzgründen aus der Wissenschaft (EXIST), ESA Business Incubation Centre Bavaria, and ESRI Emerging Business Partner.

Image Caption: The four person team of green spin (from L to R): Clemens Delatrée, Dr. Sebastian Fritsch, Gunther Schorcht, and Heiko Fabritius. The other images show sample products of green spin.

Source & Image: green spin

The amount of digitally recorded information in today's world is growing exponentially. As data flows throughout every sector of our global economy, questions emerge from commercial, government, and non-profit organizations interested in the vast possibilities of this information.

On March 7, a panel of U.S. and German IT experts convened at the GCRI to discuss the relationship between Big Data and resource-restricted systems. Questions addressed included whether new methods for machine learning are needed that respect local constraints on energy, computational power, and varying communication links as well as whether access to Big Data helps in the automated discovery of hypotheses and their validations.

Prof. Dr. Katharina Morik from TU Dortmund University presented the smartphone as a resource-restricted system, drawing upon studies of thousands of app usages. She was joined by Professor of Physics Prof. Dr. Wolfgang Rhode, who explored how real-time Big Data analytics are revolutionizing science. Prof. Dr. Rhode spoke, in particular, about the telescope as a resource-restricted system. Associate Professor of Computer Science Prof. Dr. Kristian Kersting focused on how Big Data analytics enables better logistics and transport, presenting real-time traffic as a resource-restricted system. Lastly, Dr. Claudia Perlich from the New York-based start-up Dstillery elaborated on her experience in real-time advertising. She spoke about real-time services on apps and how Big Data analytics can help create new business plans.

Dr. Tina Eliassi-Rad, Associate Professor of Computer Science at Northeastern University in Boston, MA, and Rutgers University (currently on leave of absence), served as moderator for the discussion.

The German Center for Research and Innovation (GCRI) hosted the event in cooperation with the University Alliance Ruhr (UA Ruhr).

A photo gallery, video, and podcast of the event are now available online.   

Image: Nathalie Schueller