Projects for Research and Innovation

The advantage for our clients: We never cease to investigate and implement these findings in business-related services. Therefore, VICO is involved in the world's most innovative projects, like DIMA with partners such as the Technical University of Berlin, the ParStream GmbH, Fraunhofer FIRST and Neofonie and in the leading Big Data and Web Intelligence initiatives at the federal level and in the European Union

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With "DOPA", VICO works with partners in a consortium on behalf of the European Commission for the utilization of Semantic Web technologies, Large Scale Web Crawling and statistical data for risk management in the financial sector. In the DOPA project, funded by the European Commission, a consortium, consisting of the TU Berlin, Online Memory Research SAS, DataMarket EHF, the Occam SRL, the VICO Research & Consulting GmbH and AMI software employs the utilization of Semantic Web technologies, Large Scale Web Crawling and statistical data for risk management in the financial sector.

With "MIA", funded by the German Federal Ministry for Economic Affairs, we see the development of a prototype for an information marketplace in Germany, which enables small businesses to access cloud computing and web mining applications. VICO has been working since 2010 as a consortium in the project "MIA - a marketplace for information and analysis". The MIA project is funded as part of the Trusted Cloud Programme trusted_cloud by the Federal Ministry of Economics. In this project, database systems and information management (DIMA) at the TU Berlin developed a prototype for an information marketplace for Germany under the leadership of the department. With currently more than 6 billion web pages, the German-language Web offers extraordinary potential for many applications. Currently, however, the enormous amount of data in the German-web enterprise is both a promise and a curse. In practice, most methods cannot rely on research laboratories. Despite promising and already exploring methods for the analysis of Web data, there is a lack of viable business models. A major obstacle is the high cost of the collection, provision and evaluation of this database. In order to analyze large parts of the web, first the necessary IT infrastructure must be set up and the data must be obtained. In addition, high implementation costs and costs for the continuous updating and analysis of the data also arise. The associated risks, especially for new business models, are not usually portable for SMEs and thus represent a significant barrier to innovation. Therefore, medium-sized businesses especially have not previously been able to transfer the latest research results in the field of cloud computing and web mining into profitable applications, on cost grounds (due to increasingly shorter innovation cycles). The aim of the MIA project is to create a marketplace infrastructure that enables the finishing, marketing and billing of information and value added services. In the first piloting, the marketplace contained so-called applications, for short MIA-APPs, for the areas of "market research", "Pharma", "Engineering" and "media". Project partners are the DIMA at TU Berlin, the ParStream GmbH, Fraunhofer FIRST and Neofonie.

The objective of the xLiMe project is the extraction of information from different media platforms in different languages to create a cross-lingual and cross-media knowledge database. This process runs in near real-time to continuously update and to obtain a comprehensive overview of the dissemination of information through the media, for example from European communities in Catalonia up to the global spread of English content. Through the combination of speech recognition, natural language processing, machine learning and semantic technologies, the solution for previously open research problems will be developed:

  1. The extraction of automated readable (by machine) information / knowledge (entities, sentiments, events & opinions) from multilingual, multimedia and social media content and integration in the cross-lingual and cross-media knowledge / database,
  2. automated queries for information using structured and unstructured queries in near real-time,
  3. to check origin, needs and dissemination
  4. analysis of the interaction between media exposure and behavioral patterns.

 

 

 

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