Uppsala universitet
Hoppa över länkar

Information Technology


Master Projects

Research at Uppsala DataBase Laboratory (UDBL)

Georgios Fakas
Department of Information Technology
Uppsala University, Uppsala, Sweden

Current active directions include big data, keyword search and ranking on (semi) structure data and (attributed) graphs, semantic data, spatial data, online (geo) social networks. We are also interested in workflow management.

Tore Risch (Amos II,Wrappers)
Department of Information Technology
Uppsala University, Uppsala, Sweden

Other research direction of the group concentrates on developing methods for representation and scalable extensible processing of queries analyzing different kinds of distributed data in terms of semantic 'NoSQL' data representations. Of particular interest is scalable processing of high level queries analyzing high volume data streams. A challenge is to provide scalable processing as the data volume increases and the analyzes become increasingly costly. Our approach is to develop smart query transformation techniques and distributed execution strategies in an extensible platform where external systems, algorithms, and data managers can be plugged-in. More details and projects can be found here.

Our research system Amos II provides a platform for scalable processing of queries to many different kinds of heterogeneous data sources. The queries are expressed in terms of a high level semantic data model. The system enables integration of external storage managers, databases, and computational systems through APIs in several programming languages.

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

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.