Smart urban spaces need traffic information beyond traditional vehicular traffic. Detailed data about bicycle traffic in a city is highly valuable to adapt traffic lights, plan traffic routes, or provide information about situational travel times. However, traditional induction loops do not work well for bikes, and they cannot give information about routes and travel times. This paper shows how infrastructure data (induction loops) and data from Bluetooth sensing can be fused to derive better information about bicycle traffic in a smart city. We present a novel approach to dynamically determine Bluetooth ratios of different traffic participants based on rare events, and we evaluate the approach with data from a real-world study with 97 registered users, 23,074 Bluetooth devices and more than 174,917 Bluetooth detections over one week in the City of Oldenburg.

Benchmarking spatiotemporal database systems requires the generation of suitable datasets simulating the typical behavior of moving objects. Previous approaches do not consider that in many applications the moving objects follow a given network. In this paper, the most important properties of network-based moving objects are presented. These properties are the basis for specifying and developing a new generator for spatiotemporal data. This generator combines a real network with user-defined properties of the resulting dataset. A framework for using and promoting the generator exists.

We show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so called shared virtual memory which is well suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execution. In order to reduce CPU and I/O cost, the three phases are processed in a fashion that preserves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance comparison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed up under the assumption that the number of disks is sufficiently large.

Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in two-dimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last year's conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidate which is handled by the following two steps. First of all, sophisticated approximations are used to identify answers as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. In the last step, the exact geometry of the remaining candidates has to be tested against the join predicate. The time required for computing spatial join predicates can essentially be reduced when objects are adequately organized in main memory. In our approach, objects are first decomposed into simple components which are exclusively organized by a main-memory resident spatial data structure. Overall, we present a complete approach of spatial join processing on complex spatial objects. The performance of the individual steps of our approach is evaluated with data sets from real cartographic applications. The results show that our approach reduces the total execution time of the spatial join by factors.

The minimum bounding box, the convex hull, the minimum bounding four- and five-corner, rotated boxes, and the minimum bounding ellipses and circles convex conservative approximation methods for handling complex spatial objects in spatial access methods are discussed. Results indicate that, depending on the complexity of the objects and the type of queries, the approximations five-corner, ellipse and rotated bounding box clearly outperform the bounding box. It is the reduced number of false hits that yields a considerable improvement in total query time when using the proposed approximations.>

Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems 1