Existing methods for the magement of multidimensiol data typically do not scale well with an increased number of dimensions or require the unsupported augmentation of the kernel. However, the use of multidimensiol data continues to grow in modern database applications, specifically in spatio-temporal databases. These systems produce vast volumes of multidimensiol data, and as such, data is stored in commercial RDBMS. Therefore, the efficient magement of such multidimensiol data is crucial. Despite it being applicable to any multidimensiol vector data, we consider Radio Frequency Identifications (RFID) systems in this work. Due to RFID's acceptance and rapid growth into new and complex applications, together with the fact that, as with commercial applications, its data is stored within commercial RDBMS, we have chosen RFID as a pertinent testbed. We show that its data can be represented as vectors in multidimensiol space and that the VG-curve combined with Multidimensiol Dymic Clustering Primary Index, which can be integrated into commercial RDBMS, can be used to efficiently access such data. In an empirical study conducted on three, five and nine dimensiol RFID data we show that the presented concept outperforms available off-the-shelf options with a fraction of the required space.

Presented at Conferences

  • ADC 2012 (2012)

    Melbourne, Australia