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.
|Tidsskrift||IEEE Transactions on Knowledge and Data Engineering|
|Status||Udgivet - 2015|