Distributed Information Systems and Data Mining in Self-Organizing Networks

The diffusion of sensors and devices to generate and collect data is capillary. The infrastructure that envelops the smart city has to react to the contingent situations and to changes in the operating environment. At the same time, the complexity of a distributed system, consisting of huge amounts of components fixed and mobile, can generate unsustainable costs and latencies to ensure robustness, scalability, and reliability, with type architectures middleware. The distributed system must be able to self-organize and self-restore adapting its operating strategies to optimize the use of resources and overall efficiency. Peer-to-peer systems (P2P) can offer solutions to face the requirements of managing, indexing, searching and analyzing data in scalable and self-organizing fashions, such as in cloud services and big data applications, just to mention two of the most strategic technologies for the next years. In this thesis we present G-Grid, a multi-dimensional distributed data indexing able to efficiently execute arbitrary multi-attribute exact and range queries in decentralized P2P environments. G-Grid is a foundational structure and can be effectively used in a wide range of application environments, including grid computing, cloud and big data domains. Nevertheless we proposed some improvements on the basic structure introducing a bit of randomness by using Small World networks, whereas are structures derived from social networks and show an almost uniform traffic distribution. This produced huge advantages in efficiency, cutting maintenance costs, without losing efficacy. Experiments show how this new hybrid structure obtains the best performance in traffic distribution and it a good settlement for the overall performance on the requirements desired in the modern data systems.