If an airline is able to predict when a part is going to fail and to prevent it from happening, extra costs and passenger annoyance during flight can be avoided. Combining flight analytics and sensor data from engines with customer data, airlines can better manage flight disruptions, not to mention missed connections. This is achieved by using data that the aircraft generated during flights through their deep analysis in order to detect possible malfunctions, performing predictive maintenance, anticipating problems before it is raised.
Traditional data-mining methods are effective on uniform data sets such as flight tracking data or weather. Integrating heterogeneous data sets introduces complexity in data standardization, normalization, and scalability. The variability of underlying data warehouse can be leveraged using big data infrastructure for scalability to identify trends and create actionable information.
The massive availability of data requires complex and performing architectures to support deep large-scale analysis.. Furthermore, data can be so huge that an intelligent support shall be provided by the platform itself, that is, the end user shall be reinforced and guided in the analysis by means of an intelligent support.
This can be only achieved by means of the adoption of novel approach to process large amount of data and extract useful information (data mining, machine learning, AI).
The platform as well shall be able to support all analyses without requiring huge investment: this can be achieved by adopting COTS components for the HW platform and state of the art solutions for SW platform: big data management (Hadoop), analytic (Spark).
Furthermore, the platform and related SW and algorithms shall be exploitable in different contexts as well, for example both in on-premises adoption (as proposed in the current document) and on cloud, according to different exploitable paths. In fact, although for a specific context (NGTCR), both the HW architecture, the SW stack and large part of the algorithms can be exploited also in different contexts and domains (e.g. fixed wings aircraft).
Main Objectives are:
• Define the most appropriate infrastructure to support large amount of data collected during flights test. Both HW platform and SW toolset to support storing, retrieval, data analysis
• Define the best approach to extract useful information from the data recorded according to the identified main purposes
Main Specific Objectives
• Select the appropriate platform for big data support
• Select the most promising techniques to support data analytics
• Allow analysis of flight data in combination with external data (i.e. weather)
• Support computation of prediction or suggestion based on data
• Implementation of novel predictive algorithms based on machine learning techniques
