Research activities at BigData joint research laboratory
The BigData@polito laboratory offers computation capabilities to any researcher that is willing to experiment with Big Data Technologies.
The research group involved in the project offer also support by offering their experiences and competence in the fields. Four departments are involved in the laboratory at the moment, covering a diverse set of competences. In particular
The TNG (Telecommunication Network Group) of the DET has experiences in the usage of Big Data technologies to analyse large dataset collected from the Internet. Within the EC FP7 project mPlane, they have being sucessfully leveraging the Big Data laboratory cluster to analyse TeraBytes of traces that they collect by passively analysing Internet traffic. They have being developing algorithm to process the data in a scalable way and extract useful insights about the traffic, for both anomaly detection, and better traffic knowledge. For a list of publication in this field, you can check the list on the mPlane website.
The DBDMG (DataBase and Data Mining Group) of the DAUIN carries out its research activity in various areas within the field of data mining and databases. Data mining tackles the study of algorithms aimed at discovering "hidden" information stored in large data collections. The problem of extracting such information is a rather complex due to the large number of variables that must be taken into account. Moreover, the complexity significantly increases when increasing the volume of the data to be processed (Big Data). Big Data underlines the limits of the existing data mining techniques and poses new challenges for the design of novel algorithms to address data analysis. The research activity of the DBDMG focuses on the study of algorithms for diverse data mining tasks on Big Data, including association rule mining to discover correlation among data at different abstraction levels, the extraction of knowledge for performing predictions (classification task), grouping of similar data (clustering task). The data analytic algorithms, in the Big Data context, must provide the necessary scalability, accessibility, extensibility, and flexibility. The proposed algorithms are validated in different application contexts (e.g., network traffic data analysis, text mining and social network applications, health and medical applications, financial applications).
The impact of Big Data is visible at the methodological level since it is rapidly changing methods and contents of Mathematics, Statistics and other Data Sciences. The applied side of DISMA is composed of statisticians, algebrists and numerical analysts devoted to understanding how to exploit both traditional and novel mathematical methods when facing the challenges brought up by Big Data. Several consulting activities are performed with stakeholding companies, private and public research centers and academic centers. At the same time, new ways of teaching Data Science are experienced, namely in the current edition of the Master in Big Data Engineering sponsored by DISMA and funded by the EU through the Social Fund.
DIGEP department studies Big Data from a managerial point of view and is involved in research activities aimed at identify how Big Data impact at the firm and industry level. Specifically, research investigations have the aim of: 1) identifying the Big Data capabilities that firms have to develop from the perspective of ICT management; 2) the value creation opportunities that established firms and startups have to build through innovative business models based on big data; 3) how competition dynamics and industry value chain change due to the changes produced by Big Data. The domain object by this stream of studies are information intensive industries, especially those with a lot of consumer data - such as retail, travel and transport, telecommunications, media and entertainment, and financial services. Currently, the research team at the DIGEP department is involved in research activities concerning the tourism and hospitality industry. In the future, the researchers/professors of this team are planning to focus their research activities on the other industries characterized by a high data orientation and that dispone of a lot of consumer data to process, starting from one of the more promising industry: the financial services.