MPŠ MP&Scaron MP&Scaron MP&Scaron Avtorji

Jožef Stefan
Postgraduate School

Jamova 39
SI-1000 Ljubljana

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10


Course Description

Sensor Data Analysis


Sensor Technologies


prof. dr. Dunja Mladenić


• knowing properties of sensor data and sensor metadata and importance of considering sensor location and time sensor measurement when relevant,
• understanding handling of sensor data, data pre-processing and integration of different data sources potentially of different modality,
• understanding and using sensor data analysis methods,
• comparing and choosing the appropriate data sources for a given application requirements of sensor data analysis, selecting pre-processing steps, suitable data analysis methods and experimental setting.

• ability of comparative analysis of sensor data based on their properties and in relation to the application requirements,
• constructing a meaningful, feasible pipeline for handling and pre-processing sensor data and connection to other data,
• capability of selecting and applying sensor data analysis methods on potentially large amount of data potentially arriving with high intensity over data stream,
• capability of evaluating and comparing results of sensor data analysis.


• Basic properties of sensor data and sensor metadata from the viewpoint of sensor data analysis including static data, data connected to sensor location and dynamic data connected to time.
• Comparison of handling sensor data stored in a database and handling sensor data obtained from a real-time data stream.
• Pre-processing of sensor data including: data cleaning, sensor data enrichment using background knowledge and/or context.
• Integration of other relevant data (e.g. weather, text messages) with sensor data.
• Basic and advanced methods for sensor data analysis including pattern matching and recognition, anomaly detection, modelling and prediction.
• In-depth individual study of a real case related to student’s research interests: selection of data sources and a relevant problem to be addressed via sensor data analysis, selection of appropriate data pre-processing and suitable data analysis methods.

Course literature:

• Charu C. Aggarwal (ed.): Managing and Mining Sensor Data, 2013, Springer.
• Joao Gama and Mohamed M. Gaber (eds.): Learning from Data Streams, Springer, 2007.
• Jure Leskovec, Anand Rajaraman and Jeff Ullma: Mining of Massive Datasets, 2013

• IEEE Transactions on Knowledge and Data Engineering.
• Data Mining and Knowledge Discovery, Springer.

Significant publications and references:

• 45 original scientific articles,
• 3 invited lectures,
• 140 conference contributions,
• 1 patent application,
• 2 monographs,
• 26 other completed works,
• Web of Science: 1100 pure citations,
• Hirsch index h = 15.

Representative references:
• KENDA, Klemen, FORTUNA, Carolina, MORARU, Alexandra, MLADENIĆ, Dunja, FORTUNA, Blaž, GROBELNIK, Marko. Mashups for the web of things. In: ENDRES-NIGGEMEYER, Brigitte (ed.). Semantic mashups : intelligent reuse of web resources. Berlin; Heidelberg: Springer, 2013.
• GROBELNIK, Marko, MLADENIĆ, Dunja. Automated knowledge discovery in advanced knowledge management. Journal of knowledge management, ISSN 1367-3270, 2005, vol. 9.
• MLADENIĆ, Dunja, EDDY, William F., ZIOLKO, Scott. Data mining of baskets collected at different locations over one year. Informatica, ISSN 0350-5596, 2001, 25:3.
• Nosilka raziskovalnih projektov, ki vključujejo analizo senzorskih podatkov: 7.OP Mobis - Personalized Mobility Services for energy efficiency and security through advanced Artificial Intelligence techniques, 7. OP ENVISION Environmental Services Infrastructures with ontologies.
• Master Thesis Supervisor: Alexandra Moraru, Enrichment of sensor descriptions and measurements using semantic technologies.


Seminar work with presentation and defence of the solution for the selected problem (60%)
Oral exam (40%)

Students obligations:

Seminar work with presentation and defence of the solution for the selected problem.
Oral exam.