Comparing Predictions of Object Movements

Estimating the future location of moving objects using different estimation models, such as linear or probabilistic models, has been investigated extensively. However, the location estimations of those models are generally not comparable. For instance, one model might return a position for some object, another one a Gaussian probability distribution, and a third one a uniform distribution. Similar issues arise for query answers. In this paper, we examine the question how estimations of different models can be compared. To do so, we propose a general model based on the central limit theorem. This allows handling different PDF-based approaches as well as models from the other groups (i.e., linear estimations) in a unified manner. Furthermore, we show how to inject privacy into the general model, a fundamental pre-requisite for user acceptance. Thus, we support well-known approaches like k-anonymity and spatial obfuscation. Based on our general model, we conduct a comprehensive experimental study considering a real-world road network; comparing models form different groups for the first time. Our results, for instance, reveal that estimation models based on individual velocity profiles are not necessarily better than models, which estimate the future location of objects only based on their direction. In more abstract terms, our general model allows comparison of estimation models that could not be compared before and gives way to build models that solve the privacy-accuracy challenge

Exploring pedestrian movement patterns

The main objective of this thesis is to develop an approach for exploring, analysing and interpreting movement patterns of pedestrians interacting with the environment. This objective is broken down in sub-objectives related to four research questions. A case study of the movement of visitors in a natural area is used to develop and demonstrate the approach. To achieve the objectives, four research questions were formulated: • How can movement patterns evidencing the stopping behaviour of pedestrians be detected? • What is the validity of the detected movement patterns for describing stopping behaviour of pedestrians? • How can movement patterns be applied to study the movement behaviour of visitors in natural areas? • How can movement patterns be formalized to represent the interactions between pedestrians and between pedestrians and their environment

Sequencing geographical data for efficient query processing on air in mobile computing.

Three cost models are derived to measure Data Broadcast Wait (DBW), Data Access Time in the multiplexing scheme (ATDataMul) where both data and indices are broadcast in the same channel, and Data Access Time in the separate channel scheme (ATDataSep) where data and indices are broadcast in two separate channels. Hypergraph representations are used to represent the spatial relationships of both point data and graph data. The broadcast data placement problem is then converted to the graph layout problem. A framework for classifying ordering heuristics for different types of geographical data is presented. A low-polynomial cost approximation graph layout method is used to solve the DBW minimization problem. Based on the proven monotonic relationship between ATData Sep and DBW, the same approximation method is also used for AT DataSep optimization. A novel method is developed to optimize ATDataMul. Experiments using both synthetic and real data are conducted to evaluate the performance of the ordering heuristics and optimization methods. The results show that R-Tree traversal ordering heuristic in conjunction with the optimization methods is effective for sequencing point data for spatial range query processing, while graph partition tree traversal ordering heuristic in conjunction with the optimization methods is suitable for sequencing graph data for network path query processing over air.Geographical data broadcasting is suitable for many large scale dissemination-based applications due to its independence of number of users, and thus it can serve as an important part of intelligent information infrastructures for modern cities. In broadcast systems, query response time is greatly affected by the order in which data items are being broadcast. However, existing broadcast ordering techniques are not suitable for geographical data because of the multi-dimension and rich semantics of geographical data. This research develops cost models and methods for placing geographical data items in a broadcast channel based on their spatial semantics to reduce response time and energy consumption for processing spatial queries on point data and graph data

Querying the trajectories of on-line mobile objects

Position data is expected to play a central role in a wide range of mobile computing applications, including advertising, leisure, safety, security, tourist, and traffic applications. Applications such as these are characterized by large quantities of wirelessly Internetworked, position-aware mobile objects that receive services where the objects ’ position is essential. The movement of an object is captured via sampling, resulting in a trajectory consisting of a sequence of connected line segments for each moving object. This paper presents a technique for querying these trajectories. The technique uses indices for the processing of spatiotemporal range queries on trajectories. If object movement is constrained by the presence of infrastructure, e.g., lakes, park areas, etc., the technique is capable of exploiting this to reduce the range query, the purpose being to obtain better query performance. Specifically, an algorithm is proposed that segments the original range query based on the infrastructure contained in its range. The applicability and limitations of the proposal are assessed via empirical performance studies with varying datasets and parameter settings