Projects for Internships and BSc/MSc Theses

This page describes projects for BSc/MSc internships and theses in the area of DB, in particular temporal and spatio-temporal databases. A project typically consists in the development, implementation, and evaluation of algorithmic solutions. If you are interested in one of the following projects or need more information, please contact me (gamper_at_inf.unibz.it) or Anton Dignös (dignoes_at_inf.unibz.it).

Projects in the area of temporal databases

Temporal data is ubiquitous, and we can observe an increasing interest in temporal data in recent years, e.g., web data analytics and streaming data. In contrast, the support for processing such data in DBMSs is limited. The SQL standard and most DBMSs offer support to store temporal data, but the support for query processing is very limited.

• Efficient approximate temporal aggregation: Aggregation is an important but time-consuming operation in temporal databases. Rather than computing an exact solution, this project aims at developing an algorithm to compute an approximate solution for temporal aggregation, which can be efficiently implemented in relational databases and scales for large amounts of data.

• Implementation of temporal operatos with UDFs: While the storage of temporal data is supported by most database management systems, the support for querying such data is still very limited. This project aims at implementing temporal query support by means of so-called user-defined functions.

• Supporting the processing of time series data in relational databases: Time series (TS) data is an important category of data in many application areas, e.g., sensor applications, IoT. To process such data, ad hoc time series data management systems have been developed. This project aims at developing a solution to process TS data in relational database systems.

Projects in collaboration with external institutions or companies

• Predictive maintenance for printers.
  BSc/MSc project in collaboration with the company Durst, Brixen.
  Objectives: Analyze sensor data from printing devices in order to predict the best time point for the next maintenance steps.
  Technologies: Pattern matching and mining in time series, machine learning and regression analysis.

• Error correction in time series data.
  BSc/MSc project in collaboration with the company TechnoAlpin, Bozen.
  Objectives: Analyze meteorological data from different sensors in order to detect sensor errors as well as the reason for the errors.
  Technologies: Similarity search in time series and mining of similar situations in the past.

• Interpolation of meteorological data.
  BSc/MSc project in collaboration with the company TechnoAlpin, Bozen.
  Objectives: Interpolate meteorological parameters such as temperature, humidity and wind speed for locations where no sensors are available by analyzing the parameters from neighbouring sensors.
  Technologies: Similarity search in time series and mining of similar situations in the past.