Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects

Location Dependent Queries (LDQs) benefit from the rapid advances in communication and Global Positioning System (GPS) technologies to track moving objects\u27 locations, and improve the quality-of-life by providing location relevant services and information to end users. The enormity of the underlying data maintained by LDQ applications - a large quantity of mobile objects and their frequent mobility - is, however, a major obstacle in providing effective and efficient services. Motivated by this obstacle, this thesis sets out in the quest to find improved methods to efficiently index, access, retrieve, and update volatile LDQ related mobile object data and information. Challenges and research issues are discussed in detail, and solutions are presented and examined. --Abstract, page iii

Dynamic-parinet (D-parinet) : indexing present and future trajectories in networks

While indexing historical trajectories is a hot topic in the field of moving objects (MO) databases for many years, only a few of them consider that the objects movements are constrained. DYNAMIC-PARINET (D-PATINET) is designed for capturing of trajectory data flow in multiple discrete small time interval efficiently and to predict a MO’s movement or the underlying network state at a future time. The cornerstone of D-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes tuned for a given query load and a given data distribution in the network space. D-PARINET studies continuous update of trajectory data and use interpolation to predict future MO movement in the network. PARINET and D-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS using simulated traffic data shows that DPARINET is robust and significantly outperforms the R-tree based access methods