MPŠ
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Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

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Course Description

Sensor Data Analysis

Program

Sensor Technologies

Lecturers:

prof. dr. Dunja Mladenić

Goals:

Objectives:
• 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.

Competencies:
• 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.

Content:

• 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:

Books:
• 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 http://infolab.stanford.edu/~ullman/mmds.html

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

Significant publications and references:

Bibliography:
• 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.

Examination:

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.

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