Distance-Aware Join for Indoor Moving Objects

Abstract

Indoor spaces accommodate large parts of people's lives. Relevant techniques are thus needed to efficiently manage indoor moving objects, whose positions are detected by technologies, such as Assisted GPS, Wi-Fi, RFID, and Bluetooth. Among such techniques, the distance-aware join processing is of importance in practice for indoor spatial databases. Such join operators leverage a series of applications, such as indoor mobile service and facility monitoring. However, distance-aware joining over indoor moving objects is challenging because: (1) indoor spaces are characterized by many special entities and thus render distance calculation very complex; (2) the limitations of indoor positioning technologies create inherent uncertainties in indoor moving objects data. In this paper, we study two representative join predicates in indoor settings, semi-range join and semi-neighborhood join. To implement them, we define and categorize the indoor distances between indoor uncertain objects, and derive different distance bounds that can facilitate the join processing. We design a composite index scheme that integrates indoor geometries, indoor topologies, as well as indoor uncertain objects, and thus supports the join processing efficiently. The results of extensive experimental studies demonstrate that our proposals are efficient and scalable in evaluating distance-aware join over indoor moving objects.

A Standard Indoor Spatial Data Model—OGC IndoorGML and Implementation Approaches

“…However, a simple edge connecting two nodes does not fully reflect the distance information particularly when the room connected to the edge has a big area or complicated geometry [24]. In order to overcome this problem, we need the geometry information of the room or space unit surrounded by architectural components, such as walls and doors.…”

“…While the distances from p to d 1 and from d 3 to q are called point-to-door distance, the distance from d 1 to door d 3 is called door-to-door distance. The point-to-door distance may be computed by line-of-sight [12] or the Minkowski sum [24], while the door-to-door distance can be easily computed by the shortest path algorithm with pre-computed door-to-door graph [12]. Since the vertical distance is not isotropic with horizontal distance, the indoor distance between two points on different floors is computed in different ways from the distance on the same floor.…”

An efficient approximation algorithm for multi-criteria indoor route planning queries

“…Hence, efficient query processing in indoor space has received a great a ention in recent years in which many indexing structures and query processing techniques were proposed. A comprehensive taxonomy for querying indoor data, shortest distance/path, range and k nearest neighbour queries under various se ings can be found in [10,15,16,19]. RTR-Tree and T P 2 R-tree [4] are extensions of R-tree to index trajectories of indoor moving objects.…”

Towards Mobile Information Systems for Indoor Space

“…So a probabilistic threshold is given to fit indoor environment. Reference [67] kept an eye on indoor distance-aware join query. They considered both range join and neighborhood join.…”