Indexing the past, present, and anticipated future positions of moving objects

Abstract

With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore's Law, it becomes increasingly feasible to store online all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of the essence in this scenario.Existing indexing techniques come in two forms. Some techniques capture the position of an object up until the time of the most recent position sample, while other techniques represent an object's position as a constant or linear function of time and capture the position from the current time and into the (near) future. This article offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially modifies partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a timeslice query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.

Indexing Moving Objects Using Short-Lived Throwaway Indexes

“…All of these methods assume that data would not fit into main memory. Examples include the TR-tree and TB-tree [35], the TPR-tree [48], the TPR*-tree [42], the STP-tree [41] and the R PPF -tree [34]. The most relevant work to our work is the B x -tree [17] as, conceptually, it has some similarities to the MOVIES indexing strategy.…”

“…We assumed cars to travel at a maximum speed of S max = 60m/s= 216km/h. As in similar studies [26,27,34] we initially used the moving object generator of [5]. However, it turned out that that generator does not scale for the massive workloads considered in this paper.…”

Knowledge Representation in Probabilistic Spatio-Temporal Knowledge Bases

“…While there is much work on spatio-temporal databases (Agarwal et al, 2003;Pelanis et al, 2006) and probabilistic spatio-temporal databases (Tao et al, 2005;Zhang, Chen, Jensen, Ooi, & Zhang, 2009;Zheng, Trajcevski, Zhou, & Scheuermann, 2011), these works mainly focus on devising indexing mechanisms and scaling query computation, instead of representing knowledge in a declarative fashion. In particular, Chung, Lee, and Chen (2009) use indexing to speed the computation of range queries and derive a PDF for the location of an object moving in a one-dimensional space by using its past moving behavior or the moving velocity distribution.…”

“…For this reason, researchers have investigated in detail the representation and processing of spatio-temporal data, both in AI (Cohn & Hazarika, 2001;Gabelaia, Kontchakov, Kurucz, Wolter, & Zakharyaschev, 2005;Yaman, Nau, & Subrahmanian, 2004, 2005aKnapp, Merz, Wirsing, & Zappe, 2006) and databases (Agarwal, Arge, & Erickson, 2003;Pelanis, Saltenis, & Jensen, 2006). However, in many cases the location of objects is uncertain: such cases can be handled by using probabilities (Parker, Yaman, Nau, & Subrahmanian, 2007b;Tao, Cheng, Xiao, Ngai, Kao, & Prabhakar, 2005).…”

HERO: Online Real-Time Vehicle Tracking in Shanghai

“…In database community, indexing techniques have been proposed for tracking moving objects but they are based on the assumption of the existence of centralized databases [11][12][13][14]. Despite the large number of existing methods, there is no applicable one for update-intensive applications, where it is infeasible to continuously update the index and process queries at the same time [15].…”