Better decisions through Big Data

Big data has gained much interesting in recent years due to the rapid expansion of the massive amount of data that is available for solving different types of tasks within many different application domains. However, today's big data is still on a fairly low level of abstraction when it comes to complex decision support tasks, subject to e.g. high dimensionality and significant portions of uncertainty regarding which patterns to look for in the data.

So far, research on big data has been increasingly busy with focusing on different types of platforms, e.g., Hadoop and Spark, for performing big data analytics utilizing traditional machine learning algorithms

At the same time, within the research field of information fusion there has been an increase in research regarding exploratory analytics, i.e., finding and understanding patterns in data, and predictive modelling, i.e., estimating future values of some important variable, from a higher-level of abstraction typically involving several unknown and uncertain variables that gives some indication of the current situation, e.g., in surveillance scenarios. Information fusion in this respect offers a new perspective on big data that can make a substantial contribution. More specifically, the three main aspects of information fusion: (1) use of multiple sources for identifying patterns and performing predictions, (2) modelling of uncertain information and reliability of sources, and (3) modelling of conceptual, non-measurable and high-dimensional variables, are crucial aspects when it comes to exploiting big data. We denote this view of big data as big data fusion. Big data fusion has the capacity to provide intelligence for decision makers that facilitates more complex accounts of problem domains. Moreover, it has been shown that data-driven decision-making improves organisational performance. However, the role of big data and information fusion in decision support has been very little theorized, making it cross-disciplinary research problem in urgent need of attention.

The project addresses societal and industrial problems in achieving value through improved decision-making from the rapidly increasing volume of digital data. It employs information fusion as the governing decision technology and the principal application areas are bio-informatics and telecommunications. The four-year work programme, examines the research question:

How can big data fusion enable reliable exploratory and predictive modelling on big data for the purpose of decision support?

The underlying decision support model (problematisation, big data fusion, exploratory analysis, predictive modelling, and interpretation) underpins three scientific sub-projects in Bioinformatics, Business Intelligence and Operational Decision Support. The collaborating partners are the University of Skövde, Takara Bio Europe, AstraZeneca, Advectas, and Huawei and the total budget is 29.8 MSEK, of which the Knowledge Foundation contributes with 14.8 MSEK, partner companies contribute 12.3 MSEK, and the University of Skövde contributes 2.7 MSEK.

The outcomes of the project will be:

  • new techniques for exploratory analytics and predictive modelling,
  • methods and techniques for big data fusion in bioinformatics, business intelligence, and operational decision support,
  • demonstrator solutions which may be used to develop commercially viable, systems, and
  • integration of the University of Skövde’s research capacity within decision technology and big data.
 

Project leader

The project in short

Fully name of the project:
Big Data Fusion (BISON)

Durance:
2015–2019 (2015-10-01–2019-09-30)

Financed by:
The Knowledge Foundation

Collaborating partners/Partnership:
Advectas AB, AstraZeneca AB, Huawei Technologies Sweden AB, Takara Bio Europe AB

Sub projects

BISON is hosting four sub projects:

Synergy: Project leader Göran Falkman

Bioinformatics: Project leader Jane Synnergren

Business Intelligence: Project leader Mikael Berndtsson

Operational Decision Support: Project leader Joe Steinhauer

Researchers related to the current project

Mikael Berndtsson, senior lecturer in computer science
Göran Falkman, associate professor in computer science
Tove Helldin, senior lecturer in computer science
Alexander Karlsson, senior lecturer in computer science
Gunnar Mathiason, senior lecturer in computer science
Joe Steinhauer, senior lecturer in computer science
Jane Synnergren, senior lecture in bioinformatics
Maria Riveiro, senior lecturer in computer science
Jeremy Rose, professor of informatics
Benjamin Ulfenborg, post-doctor in bioinformatics
Louise Delsing, PhD student in bioinformatics
Christian Andersson (Takara Bio Europe)
Gabriella Brolén (AstraZeneca)
Catharina Ellerström (Takara Bio Europe)
Peter Larsson (Advectas)
Anders Åhlén (Huawei Technologies Sweden)