Hiflylabs is a company that provides Business Intelligence, Consulting, and Customer Development services. Based in Budapest, Hungary, they create business value from data. Hifly carries out BI projects in many areas, from data mining and data warehousing to the solution of Big Data problems. To expand their activity, they established a mobile application development department, which has become a reliable element of our skill set. They have extensive experience with the tools of well-known data warehouse and BI technology vendors, and we also use new generation open-source solutions in their projects.
The core team at Hiflylabs has been working together for 15 years, currently with more than 50 passionate employees: data analysts, data scientists and enthusiastic data ninjas. They have extensive experiences in working and managing multicultural projects and are keen on keeping their exceptional price/performance ratio on all projects.
Hifly will use the CrowdSignals.io data experiment with applying Big Data technology to sensor data, checking performance and usability possibilities.
Jonathan is a Research Scientist at PARC, where his research focuses on the use of machine learning and deep learning in biomedical and physiological data. He is interested in the analysis of data from mobile and wearable devices for pervasive and ubiquitous health management purposes. He has investigated how mobile and wearable technology can be used within health condition management systems for disorders such as panic disorder, post-traumatic stress disorder and depressive disorder. He is particularly interested in the analysis of physiological data in order to infer information about psychological state. His research interests include affective computing, ubiquitous computing, artificial intelligence, machine learning, deep learning, and physiological data analysis. Jonathan holds a Ph.D. from the University of Auckland where his research focused on the use of artificial intelligence for strategy generation in computer games.
Jonathan will use the data that CrowdSignals.io provides to improve and broaden the types of activity recognition that currently exist. In addition, he will use physiological data from wearable devices for affective computing purposes.
The Smart Data Innovation Lab (SDIL) offers big data researchers a unique access to a large variety of Big Data and In-Memory predictive analytics technologies (e.g. SAP HANA, IBM WATSON, Software AG Terracotta). Industry and science collaborate closely in order to find hidden value in big data and generate smart data. Projects are focused on the strategic research areas of Industrie 4.0, Smart Energy, Smart Cities and Personalized Medicine. In order to close today’s gap between academic research and industry problems through a data driven innovation cycle the SDIL provides extensive support to all collaborative research projects free of charge (applications are accepted via the web site).
SDIL provides access to experts and domain-specific skills within Data Innovation Communities fostering the exchange of project results. They further provide the possibility for open-innovation and bilateral matchmaking between industrial partners and academic institutions. Template agreements and processes ensure fast project initiation at maximum legal security fit to the common technological platform. A standardized process allows anyone to set up a new collaborative project at SDIL within 2 weeks. Furthermore, it actively lists data sources such as CrowdSignals.io and lists relevant code artifacts to augment the unique industrial grade solutions provided within the platform.
SDIL is a community effort from both industry and academia in Germany coordinated by Prof. Michael Beigl’s team at TECO. The Karlsruhe Institute of Technology runs the platform.
One use case for CrowdSignals.io data that will be investigated by KIT TECO in personalized medicine is investigating correlations between interruptibility of a user and their context for field research and surveying. They expect to be able to infer interruptibility rules to implement a smart notification management system. It shall handle notifications with respect to the user’s interruptibility with the objective to improve user experience.