The automated processing of large amounts of data often still poses problems for companies. This is especially true for the use of machine learning methods to analyse this data to generate information. New methods for integration, storage and efficient access, as well as increasing computing power, make it possible to use analytical procedures that were previously too resource-intensive for individual companies to carry out.

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Photo: Colourbox

Duration: 01.06.2018 - 20.04.2021

Funded by: EU European Social Fund (ESF)

Participating institutions of Leipzig University:
Department of Research and Development (University Computer Centre (URZ)).
WINF/Information Management (Institute of Information Systems)
Automatic Language Processing (Institute of Computer Science)

Project description

Project approach

The project pursues the development of an interdisciplinary approach to the near-real-time, automated processing of data for information retrieval using ML methods.

Project goal

The aim is to develop prototypical solutions that demonstrate the use of machine learning methods in overcoming current challenges and have the potential to give companies in Saxony a competitive edge.

Implementation

To this end, specific sub-projects will be implemented within work package WP 6, which are oriented towards the application examples. One way of ensuring that the objectives are achieved is that at least one company and one research partner will be jointly involved in the solution and implementation for each application case.

The interdisciplinary development of methods and architectures to support the overall objective and the joint development of new content is to be promoted through the joint development of an architectural and methodological framework. Based on a scientific approach, suitable methods are to be evaluated and adapted to the specific needs of the InnoTeam project.

A reference architecture should enable the handling of heterogeneous data, which will occur due to very different use cases, as well as their processing by means of machine learning methods for all partners, as it were.

In addition to the processing of use cases, commonalities across use cases are to be identified, which can provide new opportunities for cooperation. Based on these commonalities, concrete ideas for follow-up projects or new business models are to be developed, thus promoting the sustainability of the project. The aim is also to sound out synergy potentials and identify best practices that enable existing solutions to be transferred to other use cases, for example. This should also ensure that knowledge is transferred between the cooperations.

To ensure the interdisciplinary exchange of knowledge and experience, regular workshops are planned, which will have a subject-specific focus. This is to ensure that partner-specific knowledge can be shared with other participants in the project consortium.

Furthermore, better networking of the individual staff members across the partners should ensure a greater understanding of the needs of the partner organisations, which in turn will indirectly influence the solutions and generate synergy effects in the long term.

Consortium

ExB Forschung & Entwicklung GmbH

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quapona technologies GmbH

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SOFISTIQ International GmbH & Co. KG

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eccenca GmbH

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Leipzig University

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Project funding

This project is funded by the European Union, the European Social Fund and is co-financed by tax funds on the basis of the budget passed by the members of the Saxon Parliament.

Persons involved

Dr. Stefan Kühne

Dr. Stefan Kühne

Project leader

Dittrichring 18-20
04109 Leipzig

 Johannes Schmidt

Johannes Schmidt

Research Fellow

Dittrichring 18-20
04109 Leipzig