Computer science and engineering

The research in computer science and engineering covers a broad area and BTH focuses on two areas:

Big data and AI:
The researchers are studying different techniques for handling large amounts of data from a technical perspective with regard to, for example, storage and database systems as well as how AI, machine learning and information mining can be used for pattern recognition and trends in large amounts of data.
Parallel computer systems:
The research focuses on parallel computer systems, cloud-based systems and security. The research covers both practical and theoretical aspects of data processing with applications and implementations of different systems.

Big data and AI

In the field of big data and AI, we study and develop methods and techniques for collecting, storing, processing and analysing large amounts of data as well as how data can be used for, for example, prediction, classification and pattern recognition. As these methods require a great deal of computational power, there is a close link to research in parallel computer systems.

The research we conduct is mainly in AI, machine learning, information extraction, large-scale data analysis, planning and scheduling and optimisation. The methods we develop can be used in several areas such as intelligent decision support systems, image analysis and pattern recognition, analysis of historical documents, health care, transport and logistics as well as anomaly and outlier detection.

Parallel computer systems

In the area of parallel computer systems we study computer systems with multiple processing units (processors), e.g., multi-core systems, distributed systems, cloud based systems, GPUs, and heterogeneous systems. The area includes methods and technologies for design, implementation, and analysis of parallel systems, in order to achieve qualities such as correctness, performance, robustness, scalability, high resource utilization, low energy consumptions. The research includes hardware aspects, as well as system and application software, and the interaction between hardware and software. The application focus is primarily algorithms and methods from the data science area, e.g., algorithms for machine learning, data mining, pattern recognition, and large scale analysis of data images.

Specialisations

Intelligent transportation systems

Research in the field of intelligent transportation systems involves the integration of electronic road tolls and how rail traffic can be re-scheduled when disturbances occur. The focus lies on transportation policy analysis, simulations, development of search algorithms and finding sustainable transportation solutions.

Distributed systems

In the distributed systems field architecture of multiprocessors and data in the cloud (a technology based on the use of computers over the Internet) are studied. Another area is the artificial intelligence and human-machine interaction. The focus is on various cloud services and applications in social networks.

Security

In the security research field machine learning techniques and anomaly detection (finding patterns in data that do not conform to an established “normal” behavior) are used to analyze irregularities in large sets of data. The focus is to identify and correct weaknesses in the police- and defense fields, industrial applications and among end users.

Research environment

BigData@bth

Scalable resource-efficient systems for big data analytics

Data will be generated at an ever-increasing rate for the foreseeable future. Added value and cost savings can be obtained by analyzing big data streams. The analysis of large data sets requires scalable and high-performance computer systems. In order to stay competitive and to reduce consumption of energy and other resources, the next generation systems for scalable big data analytics need to be more resource-efficient. The research profile, Scalable resource-efficient systems for big data analytics, combines existing expertise in machine learning, data mining, and computer engineering to create new knowledge in the area of scalable resource-efficient systems for big data analytics. The value of the new knowledge will be demonstrated and evaluated in two application areas (decision support systems and image processing).

The needs and interests of our 9 industrial partners are grouped into industrial challenges. Based on these challenges and in cooperation with our partners we have defined initial sub-projects grouped into four research themes:

Research theme A: Big data analytics for decision support
Research theme B: Big data analytics for image processing
Research theme C: Core technologies (machine learning)
Research theme D: Foundations and enabling technologies

Funding: The Knowledge Foundation (KK-stiftelsen) 2014-2020

Partners: Arkiv Digital AD, Compuverde, Ericsson, Noda Intelligent Systems, Telenor Sverige, and Sony Mobile Communications.

Contact person: Håkan Grahn, hakan.grahn@bth.se

Example of projects

Symphony

The Symphony project aims at investigating and solving the problem of exposing services by a marketplace in Cloudnative and federated environments, addressing the security, confidentiality, privacy and provenance needs for future applications of digital societies. By considering the use-case of data services for managing Renewable Energy Source (RES), the Symphony project seeks to develop solutions for securing, monitoring and accounting of service chains using blockchain-based techniques. In addition, the Symphony project applies a requirements-driven engineering approach that analyses the security needs of Cloudnative systems and of the RES use-ease as considered by the user. The project will implement a prototype for service exposure model using a service chain for RES on TRL 4/5 (“Technology validated in lab /relevant environment (industrially relevant environment in the case of key enabling technologies”).

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Blixten II

The need for and potential of using computational decision-support system for real-time railway traffic management has become more apparent the last years. In the research project TRANSFORM, effective methods to parallelize a sequential scheduling algorithm have been developed and analyzed. The parallelization has contributed to substantial speed-ups as well as increased stability and flexibility, allowing for distributed computations and multiple objectives to be incorporated. The intention is to develop and evaluate these algorithms further within this project BLIXTEN II, with a focus on mainly three different aspects:

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AgileSec – Agile Development of Security Critical Software

Today, the software industry is moving to agile development processes. One of the main ideas is that an agile team has sufficient skills to get the job done, and does not have to rely on external experts. In traditional software development, security issues are handled by experts. However, in agile development, security issues should be handled by the team. The agile process must, therefore, be extended with security related quality control and support. Rigorous quality control may, however, reduce productivity. The level of security expertise may differ significantly from one agile team to another, and it is, therefore, important to adapt security related quality control to the security maturity level of the team. In this application, we argue that an accurate estimate of a team’s security maturity would be helpful in many ways.

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Bonseyes

The Bonseyes project aims to develop a platform consisting of a Data Marketplace, Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Artificial Intelligence in low power IoT devices (“edge computing”), embedded computing systems, or data center servers (“cloud computing”). It will bring about orders of magnitude improvements in efficiency, performance, reliability, security, and productivity in the design and programming of Systems of Artificial Intelligence that incorporate Smart Cyber Physical Systems while solving a chicken-egg problem for organizations who lack access to Data and Models. It’s open software architecture will facilitate adoption of the whole concept on a wider scale.

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TRANS-FORM – Smart transfers through unraveling urban form and travel flow dynamics

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiply actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support.

TRANS-FORM, a cooperation between universities, industrial partners, public authorities and private operators, will develop, implement and test a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioural modelling, passenger flow forecasting and network state predictions into real-time operations.

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CONVINcE

CONVINcE addresses the challenge of reducing the power consumption in IPbased video networks with an end-to-end approach, from the Head End where contents are encoded and streamed to the terminals where they are consumed, embracing the Content Delivery Networks (CDN) and the core and access networks. Energy saving is a key challenge for the European Union and the CONVINcE project contributes to win this challenge. CONVINcE helps the European industry to develop new solutions and products reducing the energy footprint of video delivery networks. The double effect gained is to reduce the energy consumption in Europe and to boost the competiveness of the European industry in the area addressed by the CONVINcE project.

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FLOAT: Real-time decision support for railway traffic management

This project is financed by Trafikverket and is a continuation of the previous research projects OAT+ and EOT (both financed by Trafikverket). Researchers working in the project are currently Ph D student Sai Prashanth Josyula, Post-Doc Omid Gholami and Associate Professor Johanna Törnquist Krasemann, BTH.

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CCC – Center for Computational Criminology

Within the Center for Computational Criminology (CCC) at Blekinge Tekniska Högskola (BTH), research and collaboration have been conducted with law enforcement agencies, mainly the police since 2012. The work was funded by the Swedish Agency for Growth between 2012-2014, by Blekinge Region in 2015 and by Vinnova in 2016 . The purpose of the work of the CCC is to investigate, develop and evaluate automated methods to simplify criminal investigations within law enforcement agencies.

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