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Record Evolution Platform

Pilot Project with Continental AG

Optimizing brake systems with the Record Evolution Platform

Together with the HBS R&D division at Continental, we developed a pilot project for identifying brake squeal noises using Big Data and AI approaches on IoT data collected from test vehicles. 

Using the Record Evolution platform for end-to-end IoT development, we implemented IoT data collection strategies, Big Data analytics & AI solutions in mobile IoT scenarios. The goal was to improve Noise, Vibration and Harshness (NVH) measurements to better evaluate the circumstances under which brake squeal noise occurs. 

We developed an end-to-end process starting with data collection from test vehicles, transferring the data to an on-premises cloud environment, data processing in the cloud, and the creation of a database of structured and harmonized high-quality data. We then collaborated on the creation of diverse analytical insights into the data and created a custom AI algorithm for our edge devices to classify NVH events with high accuracy in near real-time.

To collect raw IoT data, we developed a measurement device based on Raspberry Pi and enhanced it with professional measuring electronics. Devices were installed in multiple test vehicles and were connected to the platform. With the help of the Record Evolution Reflasher, platform users can now easily add new vehicles to their test fleet with just a few clicks and so enrich the existing data pool.

The IoT apps developed on the Record Evolution Platform send data directly to the Continental cloud where the platform is installed on-premises. From there, the platform securely and reliably manages all IoT devices that operate outside of the company network. 

The outcome: Using our collaborative platform for IoT and data science, we have built a comprehensive system for IoT data collection and analysis. This system can serve as a foundation for the development of multiple use cases. For example, we can develop apps for NVH detection and real-time NVH prediction based on the identification of patterns that arise prior to an NVH event. 

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big data analysis

A Future-Ready Data Platform for Investor Relations Managers

Designing and implementing a robust architecture for low-latency Big Data analytics

EQS Group Big Data Platform Project

We built a data platform that allows our client to scale their analytics operations by adding more compute sources and even more complex analytics. 

EQS Group offers an online platform that provides services to investor relations managers. These managers advertise their company shares to potential institutional buyers such as investment funds. To create a detailed market overview for investor relations managers, EQS has to prepare and analyse massive amounts of data.

The challenge: EQS has to process large amounts of data for its clients and ensure quick response times on user requests. To overcome this challenge, a data architecture was needed that delivers low-latency analytics on Big Data. We created and implemented a design for exactly these requirements while keeping scalability and cost-efficiency in mind. 


We based the whole data architecture on a dynamically scaling cloud infrastructure and thereby fully leveraged the cost-saving potential of the cloud without compromising the ability to scale in data volumes to hundreds of billions of rows and compute resources to hundreds of CPU and terabytes of memory.

The outcome: EQS is now able to provide the specific aggregated insights their clients need and at the same time deliver the row-level details for concrete data-driven actions. Furthermore, the platform now allows EQS to use machine learning algorithms on Big Data to solve highly specialised problems in the field of investor relations and give their clients the necessary edge in the market.

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data transformation

Improving Traceability in the Automotive Industry

Building a Data Transformation Pipeline to Unify Data Across Manufacturing Plants 

automotive project

As part of a large data analytics project at Continental AG, we developed a data transformation and graph analysis pipeline to prepare production step data for a backtracking application. The aim was to analyse and compare the production processes of multiple manufacturing plants.

The early detection of defective components from external suppliers is a major challenge for any automotive company. Given the large number of components that are ultimately used, it is often the case that individual defective components are detected only after installation in finished products. Late detection has numerous negative consequences and is typically associated with miscellaneous unnecessary costs.

Using Big Data technologies such as Apache Spark and Hadoop and mathematical methods such as graph analysis, we developed and implemented a scalable data transformation and cleansing pipeline on AWS Cloud to unify the data from over 100 manufacturing plants and prepare it for further analysis. This way, analysts and data scientists can instantly access and use the unified data in the cloud. 

The outcome: As a result of this collaboration, we have data that, for the first time, allows a comparison across different manufacturing sites and can be readily utilised for further analysis in process optimisation.

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UI Solutions

Modern Industrial UI for Optical Metrology

User-centric UI for the intuitive and seamless operation of optical distance and thickness measurements

IoT and data science consulting: visualizing optical sensor data

Working with end users, we developed a new customer-centric UI that is so simple and intuitive that you do not need to read any operations manual. 

Precitec manufactures sensors for optical distance and thickness measurement. A sensor consists of an optical probe head and a spectrometer. Our goal was to develop an elegant, easy-to-use, and human-centered user interface that allows users to configure the spectrometer fast and visualise the measurement data in an appealing and simple manner. 

Record Evolution used state-of-the-art UX paradigms and the latest web technologies to develop a new modern UI.

With the help of Design Thinking and the definition of dedicated end-user personas, we created three user journeys for different kinds of usage of the applications. Depending on the expert level, the user can see less or more complex configuration options; the expert mode with a command-line interface allows for full control over the device. The setup and configuration process of new measurement devices was simplified with guided wizards and preset smart values.

This user-friendly software helps customers to distribute “ready-to-use” devices that the end user can use directly without further training, which reduces support time. The attractive clean look helps sales representatives during demonstrations of the devices to customers and during trade shows.

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UI Solutions

Networked Mobility: New Approaches to Human-machine Interaction in Industrial UI

An integrated cockpit display for a new driving experience in future mobility scenarios

Collaborating with different departments at Continental,  we co-developed a cockpit demonstrator. The interactive display solution offers an experience of full connectivity, paving the way towards safer and stress-free driving. 

Record Evolution worked on the development of a human-machine interface (HMI) using state-of-the-art connectivity technologies and modern web architectures. We established real-time connections between a multitude of functional components in the car such as self-shading windows, an AI assistant, door sensors, interior lighting, autonomous driving controls and more.

All these components were gesture-controlled by mobile phone or tablet as well as a large touch-sensitive dashboard HMI containing apps for many different use cases. 

Our developers created a framework for the integration of various apps and implemented a video player, a music application, navigation, email and weather services in addition to the essential driving controls such as rear-view mirror video, speedometer and fuel/fuel gauge. The implemented connectivity solutions and modular app structure contributed to the display’s unique look & feel.  

The display demonstrator operates in two modes: In automated driving mode, the display extends to its fullest capacity to serve as a customizable infotainment hub for both driver and passengers. In manual driving mode, half of the screen disappears into the cockpit and all apps smoothly adapt to the new screen format so that the driver can focus on the driving experience.

The integrated cockpit display was one of Continental’s highlight exhibits during the Frankfurt Motor Show IAA 2019, allowing visitors to board the cockpit and freely interact with the innovative solution within a driving simulator.

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Big Data & IoT

Big Data & IoT in Product Development

Noise detection for the determination of interference signals

In this project with a large automotive supplier, conclusions about the causes of certain product properties were to be determined on the basis of large quantities of measurement data using statistical methods.

One focus was the automated and reliable detection of specific noises (vibrations) from sound tracks/vibration pickups, which had a large number of interfering signals.

Another focus was the root cause analysis and prediction of these noises based on the additionally detected more than 40 sensor tracks. In order to provide the necessary data volumes, this project developed and implemented a prototypical IoT architecture for the collection of real-time sensor data from vehicles in a cloud-based data store. Only open-source technologies were used.

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Kundenprojekte - Big Data und IoT bei Continental

Risk Analysis

A Platform for Risk Analysis

Integration of source data systems for efficient evaluation

Kundenprojekte - Risikoanalyse bei ParcIT

In one of our projects, we set up a comprehensive risk analysis platform for approximately 1,100 German Volksbanken and Raiffeisenbanken in parcIT GmbH

Several source data systems were integrated and cleaned up for efficient evaluation. In addition, various classes of analyses were prepared, including calibration and selectivity of classification procedures, default correlation and migration analyses.

We have successfully used open-source software and customized hardware as our technical basis.

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