RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

Efficient Query Routing in Distributed Spatial Databases

Spatial databases are prominently used in Geographic Information System (GIS) applications. However, many of the current architectures rely on a centralized data repository. The next evolution will be GIS applications that utilize and integrate a multitude of remotely accessible data sets, for example via Web services. Our involvement in a project where geotechnical borehole information is retrieved from a large number of repositories that are under different administrative control has motivated us to design an efficient distributed access structure and routing middleware for spatial queries. In this study we present our middleware design based on distributed R-tree and Quadtree index structures. Importantly, the framework supports both spatial range and k nearest neighbor queries. We have performed a theoretical analysis and simulations with synthetic and real data sets. The results show a large reduction in message traffic to a level only slightly above what is minimally necessary. Categories and Subject Descriptors H.2.4 [Database Management]: Systems—distributed databases

Design of a Large Scale Data Stream Recorder

Abstract: Presently, digital continuous media (CM) are well established as an integral part of many applications. In recent years, a considerable amount of research has focused on the efficient retrieval of such media. Scant attention has been paid to servers that can record such streams in real time. However, more and more devices produce direct digital output streams. Hence, the need arises to capture and store these streams with an efficient data stream recorder that can handle both recording and playback of many streams simultaneously and provide a central repository for all data. We propose a design for a large scale data stream recorder. Our goal was to introduce a unified architecture that integrates multi-stream recording and retrieval in a coherent manner. The discussion raises practical issues such as support for multizone disk drives, variable bit rate media, and disk drives that have a different write than read bandwidth. We provide initial solutions for some issues while others will need to be investigated further.