Big Data Algorithms for IoT, Profiling and Predictive Maintenance

PhD Program in Computer and Control Engineering


Elena Baralis –
Tania Cerquitelli –
Marco Mellia –

PhD Student: Eliana Pastor

Context of the research activity

In the last decade, Internet‐related technologies have enabled radical transformations in the structures of many industries, causing the rise of new rules and roles that circumscribe the division of labour and the distribution of profits along a value chain. In this scenario, the tourism industry is one of the settings where changes enabled by the Internet are more visible. New mechanisms of intermediation over the Internet are marginalising the role of traditional ‘bricks‐and‐mortar’ travel agencies, and are changing the way travellers’ experiences are created and shared. In such scenario, big data techniques can support the data gathering on the Internet about customers’ behaviour, reviews of hotels, and hotel pricing strategy, in order to support hotel managers in their decision making process.

Based on data collected from the Internet through big data techniques, many analyses can be conducted in order to understand the impact of the Internet in the tourism sector at different levels of analysis. For example, data collected from the Internet through big data techniques could be useful for understanding the dynamics behind the pricing strategy of hotels, the key actors that create economic value in the hospitality industry and in shaping the rules of competition, and the way hotels can control fake reviews that can undermine the decisions of actual and prospect customers in favour of online intermediaries. The research activity fits to the aims and scope of research of the SmartData@PoliTo interdepartmental center, since it requires competencies in data acquisition from the Internet and in data analytics for econometric analyses.


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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