Data Innovation Community “Smart Infrastructure”
The new data innovation community “Smart Infrastructure” was kicked off successfully at the 8th strategy board meeting. The new data innovation community that is lead by Veit Hagenmeyer (KIT) and Hellmuth Frey (ENBW) centers around smart data challenges in energy and mobility.
Focus: “Smart Cities”
Urban development and traffic management are also areas where Big Data analyses open up entirely new possibilities. By means of integrated transport communication solutions and intelligent traffic management systems, the traffic in fast-growing, densely populated urban areas can be managed better. In cities, immense masses of data are generated by subway trains, buses, taxis and traffic cameras, just to name a few. The existing IT environment hardly allows for making forecasts or even extended data analyses in order to play through different traffic and transport scenarios. However, that’s the only way to improve the respective services and further urban planning. The moment when information can be analyzed in real-time, correctly interpreted and put into context with historical data, then traffic jams and dangerous situations can be identified at an early stage, leading to a significant decrease in traffic volume, emission and driving time.
The energy industry is facing fundamental changes: the move towards renewable energies; the EU stipulation to install smart meters; the development of new, customer-centered business models, etc. All these changes combine, thus forming entirely new challenges for the IT infrastructure of the energy industry. Utility companies will be able to optimize their business processes and develop new business models by analyzing comprehensive data, both structured and unstructured (e.g. data generated by mobile device apps, web portals or social media). A case in point: Big Data analyses enable better consumption forecasts so that energy providers will be able to better manage and control their energy purchases on the energy markets. Due to Big Data, consumption rate models can be better tailored to specific user groups. Moreover, unhappy customers can be identified more quickly – allowing for measures that ensure a higher customer retention.
The Data Innovation Community “Smart Infrastructures” wants to explore important data-driven aspects in the area of infrastructure optimization, planning and control as well as analytic solutions to support decision making
The Data Innovation Community “Smart Infrastructures” addresses all companies and research institutions interested in conducting joint research with regard to these aspects. This includes energy industry user companies as well as companies from the automation and IT industries.
If you are interested in working with the Data Innovation Community or visit one of its meetings, please contact the DIC leads via e-mail at email@example.com
Members can access the internal forums at SAP Jam.
Over several years, the KIT-FM (Facility Management) has collected data with immense value for the operational management, but also for the planning and implementation of future infrastructure developments. This data is also of great interest for researchers. On the one hand, we will examine how the existing infrastructure data evaluated by Smart Data methods can help to draw more accurate conclusions about the operational management and the infrastructure planning. On the other hand, we will drive forward the usability of this data for research and innovation projects.
Increasing data volumes and increasingly complex calculation models require fast and robust procedures. This is the topic of the BigGIS project, in which integrated procedures for dealing with uncertainty within (geo-)big data are developed. Together with the SDIL, suitable algorithms are implemented, tested and further developed on the basis of temperature data.
An anomaly is generally defined as a deviation from the norm and from the expected behavior. Such anomalies often indicate incidents and constellations that require immediate attention and reaction. In a social network, an anomaly can indicate spontaneous attractions such as demonstrations. Their early detection is crucial for further management. With regard to graph data, anomalies can be modeled as subgraphs, in which the nodes deviate significantly from the norm attribute values and edge distributions. In the case of a dynamic graphs, historical conditions can also be taken into account.
The goal of the project is to develop a method for dynamical heterogeneous graphs, which is able to continuously detect and evaluate anomalies. Thereby, the procedure should be designed for real-time service and should permanently be supplied with a stream of new data. In addition, the method should be scalable and able to process large amount of data. For this, not only an algorithm specially adapted to this problem is necessary, but also supporting index and data structures that provide efficient access to historical data. The applicability and practicability of the procedure should be assessed during the course of the project by means of a prototypical implementation for which we want to use the SDIL platform.
The project “Smart Air Qualitiy Network” (SmartAQnet) is based on a pragmatic, data driven approach since the existing data treasures of mcloud.de are combined for the first time and linked with a networked mobile measurement strategy.
This project is part of the Transforming Transport EU lighthouse project. The TransformingTransport project will demonstrate, in a realistic, measurable, and replicable way the transformative effects that Big Data will have to the mobility and logistics market.
In this project, a traffic-flow forecasting method using environmental models is proposed. Nowadays, traffic flow prediction mainly takes into account information from individual, specific sensors. However, information from neighboring sensors and other sensors in the traffic subnet could be used to improve modern prognosis models.
The LKA Baden-Württemberg has a data pool of up to 150 TB per case. Performance is a critical factor in this context, which is why it is necessary to research in advance which Big Data platform should be used. Thus, the project aims at building prototypes which are then used to analyze the runtime and performance of various platforms.
The global rollout of Smart Meters opens a new business paradigm for utilities with data collection/transaction at such a high volume and velocity. In this project, we develop a toolchain based on In-Memory Data Management and Parallel Data Processing in the GPU. Our aim is to use the processing power of the GPU and the high-throughput and low-latency features of In-Memory databases to develop an adequate Big Data analytics platform. Although the project is primarily concerned with the use case of Smart Meter data, the tool chain is also applicable for Big Data analytics in other domains.
Traditionally, organizations are using rule-based approaches to discover defects in Master Data. The definition of these rules is expensive for organizations and constrained by the availability of resources with the right domain expertise. The aim of this project is to evaluate with recourse to suggested validation rules the applicability of approaches like association rule mining as a way to support Master Data domain experts.
The VDAR-project goes back to the framework project “Software Campus” supported by the Federal Ministry of Education and Research (BMBF). Within the scope of the VDAR-research project,control concepts have been explored that combine the economic system of the electricity market and the physical system of the electricity grid in a decoupled control circuit. The aim is to ultimately improve the availability of energy.
The BMBF-funded project “Smart Data Innovation processes, tools and operational concepts (SDI-X)” explores appropriate tools and best practices. The aim is to not only facilitate extensive data analysis projects between different research and industry partners but also enable their prompt implementation. The results of the projects will be fully integrated into the SIDL and its projects.