Digital Plattform

Digital Plattform

In the Innovation Hub 13 project, transfer-relevant information and results of regional players are collected, processed and outlined in a suitable manner. In a first step, in house university knowledge, methods and technologies are gathered and subsequently linked with relevant information from regional players and the regional economy. Furthermore, the demands and problem situations of all players in the region can be taken into account.

The data will be collected by means of a digital platform and will be made accessible to regional players. In doing so, transfer-relevant topics and the respective regional players can more readily be identified and brought together.Thus, through a high cross-linkage of all partners, a significantly higher efficiency of transfer activities is more likely to happen.

 

What are the objectives of the digital platform?

The digital platform is expected to be the digital twin of the Innovation Hub 13 , will provide a wide range of information and thus will digitally represent and reflect all content of the Innovation Hub, including test beds and showrooms.

All players involved are going to be embedded into this process. Companies, researchers, students and citizens alike can contribute to the platform by gathering data and can subsequently benefit from semantic research. Here, the semantic meaning of a research request is at the focus of attention with not being the definition of the requested research which is searched for, but its relevance and significance: the use of synonyms and complex combinations included.

In order to determine possible cooperation or other collaboration between actors, matching supported by AI (artificial intelligence), i.e. a combination of given data and the search, is to be implemented. The so-called recommendation engine makes suggestions which result fits best to the search. To ensure this, the engine uses machine learning algorithms. These algorithms learn from examples and, after a learning phase, can apply their experience to new data sets or searches. To do this, these algorithms create a statistical model and recognize patterns in new data. As a result, intelligent recommendations are provided. Translated with www.DeepL.com/Translator (free version)

The digital platform shall also serve the transfer scouts as a tool for target-oriented transfer adjusted to the different players‘ demands. The functionality of the semantic research within the Recommendation Engine is a core element in this process.

 

Scenarios for the Recommendation Engine to be applied

There are many application fields (use cases) for the Recommendation Engine to come into play. We provide you with several examples where intelligent matching supports this process:

Use Case Hochschule sucht PartnerThe first use case is aligned to Universities . The first use case is aligned to universities. Their employees can enter their data/project idea in the form of a free text, for example the existing abstract, and the Recommendation Engine will deliver suitable project advertisements and industry partners. For universities, this process significantly facilitates the search for cooperation partners and possible project initiations.
Use Case Industriepartner sucht PartnerIn that case of application, industry players can indicate their technical problems in text form with the Recommendation Engine delivering suitable professorships and funding options offered by federal and regional government. As a consequence, companies with a former high threshold of getting in contact with universities, can now easier reach out to universities. Furthermore, the search for suitable partners and funding opportunities is facilitated with the potential result of an earlier project start.
Use Case Unternehmen sucht TestbedWhen a Company has developed a new product, it needs extensive product testing in various form and scenarios. In the Recommendation Engine, the respective company can enter the product information into the system in form of a free text with the algorithms delivering suitable test fields and testing ways.
Use Case Land sucht AusschreibungspartnerIn order to precisely address federal tenders, the system, after having supplied strategic objectives, is supposed to identify suitable partners from industry and science who can subsequently be directly informed about the ongoing tenders. In doing so, we increase the likeliness of suitable tenders or requests.
Use Case Student sucht AbschlussarbeitStudents will have the opportunity to enter their skills, prior knowledge and interests into the Recommendation Engine. According to individual needs, the algorithm can present suitable final theses or job opportunities. The result is a simplified search for students, and, as a direct consequence, companies are likely to receive more applications from interested, suitable applicants who maybe would have not applied in other respects.
Use Case Fördermittelgeber sucht PartnerIn order to find the suitable contact for funding programmes, various Funding bodies can use the Recommendation Engine. They can enter their funding programme into the engine in free text form with the algorithm delivering the suitable companies or professorships who can subsequently be directly informed about the respective funding programme.
These application fields and scenarios show the wide range of applications which the Recommendation Engine supported by machine learning is going to enable on a long-term perspective. The Recommendation Engine is under development, use cases are steadily implemented.

Current state

Screenshot digitale PlattformFor the time being, a first alpha version of the platform with a small scope of functions has been established open for testing purpose done by external users (the original developers not included). The alpha version contains the basic functionalities and can be used by, for example, organisations (companies, universities etc.) or third parties through entering data with regard to project results or laboratory equipment. An output of entities and the drafting of transfer profiles has been implemented. The screenshot shows the starting page of the first version of the digital platform. This version allows for a search and filtering of the above-mentioned entities, for example companies. In the future, it is planned to complete the entities respectively and to integrate them into the Recommendation Engine. It will be surely interesting for you to see future developments happen.
It will be surely interesting for you to see future developments happen.
Sie dürfen gespannt sein, wie es weitergeht.

Do you have questions, suggestions, ideas or specific projects? We are looking forward to talking to you!

 
 
Contact
Job offers

Technical University of Applied Sciences Wildau

 
 

Hochschulring 1
15745 Wildau

Map

www.th-wildau.de

 

Brandenburg Technical University Cottbus-Senftenberg

 

Platz der Deutschen Einheit 1
03046 Cottbus

→ Map

→ www.b-tu.de

 

The "Innovation Hub 13 - Fast Track to Transfer" of the Technical University of Wildau and the Brandenburg Technical University of Cottbus-Senftenberg is one of the 29 selected winners of the federal government funding initiative "Innovative College", equipped with funds of the Federal Ministry of Education and Research BMBF And the state of Brandenburg. Further information can be found at www.innovative-hochschule.de

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