A data cube model for analysis of high volumes of ambient data

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data

Observation of Fast Evolution in Parity-Time-Symmetric System

To find and realize the optimal evolution between two states is significant both in theory and application. In quantum mechanics, the minimal evolution is bounded by the gap between the largest and smallest eigenvalue of the Hamiltonian. In the parity-time-symmetric(PT-symmetric) Hamiltonian theory, it was predicted that the optimized evolution time can be reduced drastically comparing to the bound in the Hermitian case, and can become even zero. In this Letter, we report the experimental observation of the fast evolution of a PT-symmetric Hamiltonian in an nuclear magnetic resonance (NMR) quantum system. The experimental results demonstrate that the PT-symmetric Hamiltonian can indeed evolve much faster than that in a quantum system, and time it takes can be arbitrary close to zero.Comment: 13 pages, 5 figure