Big Data Algorithms for IoT, Profiling and Predictive Maintenance

PhD Program in Computer and Control Engineering

Supervisors

Elena Baralis – elena.baralis@polito.it
Tania Cerquitelli – tania.cerquitelli@polito.it
Marco Mellia – marco.mellia@polito.it

PhD Student: Eliana Pastor

Context of the research activity

The availability of massive datasets currently denoted as “big data” characterizes many application domains (e.g., IoT‐based systems, energy grids). Big Data stresses the limits of existing data mining techniques and sets new horizons for the design of innovative techniques to address data analysis.

Data is generated at a fast growing pace in application domains where its collection is automated. For example, IoT sensor data streams are generated by intelligent devices embedded in cars and by smart appliances of different kinds. Effective analytics techniques may generate system (or user) profiles, which can be exploited to predict future behaviors. This approach finds interesting applications in the context of predictive maintenance, in which learning the system behavior will allow the prediction of failures (e.g., power failures in transmission lines, engine faults in cars).

Objectives

The objective of the research activity is the definition of big data analytics approaches to analyze IoT streams for a variety of applications (e.g., sensor data streams from instrumented cars).
The following steps (and milestones) are envisioned.

• Data collection and exploration. The design of a framework to store relevant information in a data lake. Heterogeneous data streams encompassing custom proprietary data and publicly available data will be collected in a common data repository. Tools for explorative analysis will be exploited to characterize data and drive the following analysis tasks.
• Big data algorithms design and development. State-of-the-art tools and novel algorithms designed for the specific data analysis problem will be defined (e.g., to predict component failures).
  Knowledge/model interpretation. The understanding of a discovered behavior requires the interaction with domain experts, that will allow operational validation of the proposed approaches.

Skills and competencies for the development of the activity

The candidate should have excellent programming skills, programming experience in the Hadoop/Spark ecosystem, good knowledge of machine learning algorithms.

 

Further information about the PhD program at Politecnico can be found here

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