Data and Business Analytics
Data and Business Analytics
Competence in mathematical and statistical analysis, optimisation, modelling and the application and implementation of machine learning algorithms.
Numbers and individual facts do not in themselves increase understanding, but the information must first be arranged, classified, statistically recorded, interpreted and placed in a context through analysis. Following digitalisation and the more widespread use of measurement technology, the monitoring of many different phenomena, that is, data collection, has increased to a significant extent.
In general, data analytics is a study of the relationship between individual measuring points. Nowadays, the term is often used in connection with computer-aided processing of extensive data through various computational methods. In business analytics, the tools are almost the same, but the focus is more market-oriented.
We offer expertise in mathematical and statistical analysis, optimisation, modelling and the application and implementation of machine learning algorithms. In cooperation with the Digital Solutions & Platforms research area, the solutions based on analytics are combined with technical implementations that are easily accessible for the user.
In terms of industrial applications, we have taken part in developing the production line of KONE and in reducing the consumption of district heating in the City of Tampere. The solution developed for KONE focused on minimising the impact of bottlenecks on the efficiency of a production line. As part of the Smart City project in Tampere, the HVAC systems in Vuores School, amongst other buildings, are monitored. With the methods of machine learning and artificial intelligence, the resulting data is used in the creation a computational model for reducing energy consumption.
We participate in bioeconomy research in the Bioeconomy 4.0, Carbon 4.0, Good for cattle and HämIncent projects, to mention a few examples. In these projects, we interpret the behaviour of cows through video monitoring, monitor the development of a cricket population in rearing containers, and analyse the state of a crop on the basis of satellite images, and the need for thinning a forest on the basis of drone photos. The tools include Python- and MATLAB -based solutions for modelling, traditional statistical analytics, image measurement and machine learning. In the Field Observatory cooperation, we developed a user interface for the visualisation of carbon sequestration of the farmland of www.fieldobservatory.com.
We provide students with opportunities to learn through assignments connected to practice.
In previous projects, we have, for example,
- implemented data transfer for the measurement of conditions in a multi-layer crop-container and a barn,
- carried out image recognition of the behaviour or cows,
- created a model for temperature distribution in a grain silo
- explored the potential of open data for determining the dynamic risk of road transport
- You can also read Genrikh Ekkerman’s blog post about his traineeship and work in the unit: From a trainee to working with an international giant
The size of the projects has varied from small-scale course work to larger entities, internships, student exchanges and theses.
Do not hesitate to contact the team members!
We are involved in several HAMK Smart and HAMK projects. This tab presents our activities through our own role in these projects.
In the Bioeconomy 4.0 project, our team participates in research related to the behaviour and productivity of dairy cattle. We analyse, among other things, the impact of barn conditions on productivity and the behaviour patterns of cows with the help of machine vision.
Our team participates in the curation, processing and analysis of research data collected by drones. We have image material from the forests of Evo, the Mustiala biocarbon field and urban green spaces, amongst others. The work will also continue in the Carbon Lane project that is currently starting. The project explores carbon drawdown solutions for urban green areas.
Good for cattle
The team is involved in a study of the preservation of feed and the monitoring of bunker silos. The focus is on developing ways to identify feed contamination, which would reduce, for example, financial losses and the use of resources. The team’s work includes transferring information on temperature distribution and calculating the three-dimensional distribution.
In cooperation with HAMK Bio, the team is involved in the development of a system for monitoring crickets. The team examines the monitoring of the growth and activity of a cricket population through image analysis.
In cooperation with HAMK Bio, the team carried out the visualisation of mass flows within a biogas refinery, as well as implementing the framework of a simulation model for analysing the regional impact of the refinery. Read more in our article: https://timreview.ca/article/1421
The team carried out a pilot project for the use of Telia’s mobile location data in event planning.
The operation of HVAC systems in buildings in the City of Tampere is modelled through the methods of machine learning and AI. The resulting model is used for minimising energy consumption by keeping the indoor air conditions favourable.
AIKO– Diverse knowhow in foresight: visualisation of information in the internationalisation of SMEs
The team carried out a report on the use of open data related to traffic safety. The work continued later in a thesis.
The team is involved in developing learning research and learning analytics related to a VR game.
Research publications on the analysis of barn conditions and behaviour of cows:
- Barn 4.0 — continuous measuring of barn conditions, in Suomen Maataloustieteellisen Seuran Tiedote, 2020.
- Deep learning image recognition of cow behaviour near an automatic milking robot with an open data set (under peer review)
- Puurtinen, J: Tekoäly kuvantunnistuksessa (2021)
- Sebastiaan, R: Classifying road risks – Merging and analysing data from geographical data source (2020)
Research articles in bioeconomy:
- Koskela, O., Dempers, C., Kymäläinen, M., Nummela, J., Simulating a Biorefinery Ecosystem to Manage and Motivate Sustainable Regional Nutrient Circulation, In Technology Innovation Management Review, February 2021 issue.
- Niemitalo, O., Koskinen, E., Hyväluoma, J., Lientola, E., Lindberg, H., Koskela, O., Kunttu, I., A Year Acquiring and Publishing Drone Aerial Images in Research on Agriculture, Forestry, and Private Urban Gardens, In Technology Innovation Management Review, February 2021 issue.
- Olli Koskela
- Research Manager
- Tel. +358 50476 1766
- Data and Business Analytics research area leader
- Olli Niemitalo
- Senior Data Scientist
- Analysis of spatial data and time series, such as in satellite and drone images. Development and documentation of practices related to research data management and the use of machine learning through CSC services. Digital signal processing, bioinformatics and algorithms.
- Genrikh Ekkerman
- Student Assistant, research
- Extracting, processing, analyzing and utilizing data. Projects: Smart City and Traffic 4.0
- Mikael Ketonen
- Project Worker
- Machine and deep learning with MATLAB®. Projects: Bioeconomy 4.0, Carbon 4.0, Smart City, Good for cattle
- Iivo Metsä-Eerola
- Project Worker
- Project: SmartCity