Section: Computer Science and Engineering
Tutor: AMIGONI FRANCESCO Major Research topic
:Data Reduction of Monitored Data
Advisor: PERNICI BARBARAAbstract:
Nowadays, monitoring data are collected and processed in various domains, while the utilization of monitoring data remains an open question. The scale and size of data brings information systems a heavy burden, which requires new techniques to derive information quickly. We design a data reduction technique based on correlation model, and exploit it in data centers. On the other hand, the variety of monitoring data has brought many new observations of the environments, while how to integrate those new data into knowledge still needs investigation. We explore the possibility of utilizing various monitoring data in IoT to enhance the adaptability of services, namely, enable the service to diagnose and adapt to sensor faults.
Data Reduction in Monitored Data
Nowadays, increasing time series data has brought new challenges in many domains because their massive instances, dimensions, speed and complexity. In order to solve this problem, data reduction techniques are becoming an integral part of future systems, especially for Monitoring System. Different from most data reduction techniques which are based on information theory, this paper is planning to explore a novel model-based method, which tries to build piecewise regression models only for correlated data with guidance of priori knowledge, avoiding unnecessary computation between unrelated data streams. Until now, we have designed a primal data reduction framework, corresponding experiments and evaluation criteria.
Advisor: Professor Barbara Pernici