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Research at Uppsala DataBase Laboratory (UDBL)

Kjell Orsborn
Department of Information Technology
Uppsala University, Uppsala, Sweden


Within the combined fields of Database Technology and Data Analytics there are several research challenges that currently attracts a lot of interest and that are part of our current research plan. Research in database technology is commonly dealing with high-level and scalable data management and an important aspect of data analytics involves  data analysis of large-scale data sets and streams. Key issues are to provide the data processing both at the edge in cyber-physical systems (industrial equipment) and at an aggregated level in the cloud (or in a corresponding environment). In addition to processing performance, high level access to data and data analytics (i.e. query-based numerical operators) capabilities are vital for the overall process performance. Another important aspect is to support efficient query optimization and indexing techniques for data, data streams and for corresponding analytical queries since these mechanisms can reduce the computational complexity and thus are important for supporting green computing.

We are investigating and developing capability to efficiently handling data and data streams in industrial processes which is critical for transforming the current manufacturing industry with the overall goal of improving productivity and quality of industrial processes and products. A critical area within this context is scalable capability to collect, process, analyse, and visualize data streams to support cyber-physical systems, exemplified by machining and production processes, hydraulic power systems, and heavy vehicles in production. For this purpose, we work on enabling scalable query-based data analytics and visualization of data and streaming data based on for example computational, array and NoSQL data and data stream management systems that can be deployed both in an edge and cloud environment. Figure 1 below illustrates the initial idea of industrial internet where (aggregated) data analytics is supplied in a cloud environment where as Figure 2 completes the overall data analytics process with an edge-based perspective with a much tighter analytics loop that can react to sudden changes in industrial processes.

Figure 1: Industrial internet
(source https://www.ge.com/reports/post/76430585563/                           Figure 2: An example of an edge analytics architecture
new-industrial-internet-report-from-ge-finds/)

This type of knowledge and skills are also being carried over to our students in our educations and are already being planned to be part of the suggested new educational programs in the area of industrial analytics (Civilingenjörsprogram and MSc program). Also PhD courses are being developed for main-memory dbms’s, array dbms’s and also for streaming dbms’s.

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