We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach. 1

A crucial consideration in environments where data is broadcast to clients is the low bandwidth available for clients to communicate with servers. Advanced applications in such environments do need to read data that is mutually consistent aswell as current. However, given the asymmetric communication capabilities and the needs of clients in mobile environments, traditional serializability-based approaches are too restrictive, unnecessary, and impractical. We thus propose the use of a weaker correctness criterion called update consistency and outline mechanisms based on this criterion that ensure (1) the mutual consistency of data maintained by the server and read by clients, and (2) the currency of data read by clients. Using these mechanisms, clients can obtain data that is current and mutually consistent "off the air", i.e., without contacting the server to, say, obtain locks. Experimental results show a substantial reduction in response times as compared to existing (serializability-based) approaches. A further attractive feature of the approach is that if caching is possible at a client, weaker forms of currency can be obtained while still satisfying the mutual consistency of data.

this article, we discuss location -dependent information access in a mobile-pervasive environment, in particular in a cellular mobile system, and present new research issues arising from on-demand access, broadcast, and data caching

The domain of spatiotemporal applications is a treasure trove of new types of data as well as queries. For designing and testing novel data types and access methods that will support these applications, large spatiotemporal datasets are needed. As in many cases it is not possible to obtain real datasets, either they do not exist or they are not accessible, synthetic datasets are governed by artificial parameters rather than by the (sometimes invisible) rules governing real-world behavior. In this work we show how, at least for some cases, one can generate spatiotemporal datasets that simulate real-world behavior. We illustrate example cases and translate them into appropriate calls of GSTD, a spatiotemporal data generator. The generated data is illustrated using two-dimensional snapshot pictures as well as three-dimensional (two spatial plus one temporal dimension) trajectory images.

We investigate techniques for similarity analysis of spatio-temporal trajectories for mobile objects. Such kind of data may contain a great amount of outliers, which degrades the performance of Euclidean and Time Warping Distance. Therefore, here we propose the use of non-metric distance functions based on the Longest Common Subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various L p Norms and also to Time Warping distance (for real and synthetic data) and we present experimental results that validate the accuracy and efficiency of our approach, especially under the strong presence of noise.

Nowadays the number of mobile devices is increasing rapidly. However there is a lack of data services that can be efficiently performed in mobile computing environments. The main reason is that many well-known techniques have to be adapted to the new environment. Particularly, the processing of location-dependent queries is still a subject of research. In this paper we introduce an approach to deal with location-dependent queries posed in mobile environments. A decentralized solution is proposed using mobile agents, which also help in the optimization of the communication effort. 1.

Abstract. Good data management infrastructures within a medical environment

Data dissemination has significantly served as a scalable data delivery mechanism in wireless networks. However, even though the broadcast traffic has the nature of dynamic changes, most previous research efforts were elaborated upon the premise of static workloads and access patterns without having proper traffic awareness. In this paper, we address the existence of client impatience and accordingly devise an on-line traffic awareness mechanism based on a novel selective deferment and reflection technique (SDR) to estimate the dynamic workloads and access patterns in a granularity of a broadcast cycle. In comparison with prior probing and feedback approaches, our design is of practical usefulness in that it has low complexity and is light-weight without performance degradation. With various dynamic traffic scenarios, the experimental results show that with an increasing/decreasing workload, the real access frequency distribution is bounded by two specific estimated distributions. This fact in turn suggests us to employ a trigonometric tuning method to further enhance the estimation. In addition, we examine that the mean difference between the estimated access frequency distribution and the real one is very small, consequently indicating the feasibility and reliability of our proposed data broadcast mechanism with traffic awareness.

The issue of data broadcast has received much attention in mobile computing. A periodic broadcast of frequently requested data can reduce the workload of the up-link channel and facilitate data access for the mobile user. Many approaches have been proposed to schedule data items for broadcasting. However, the issues of accessing multiple data items on the broadcast channel are less discussed. Two problems are discussed in this paper, that is, deciding the content of the broadcast channel based on the queries from the clients, and scheduling the data items to be broadcast. We will show that these two problems are NP-complete. Different heuristics to these problems are presented and compared through performance evaluations.

Inspired by the characteristics of biologically-motivated systems consisting of autonomous agents, we define the notion of stabilizing consensus in fully decentralized and highly dynamic ad hoc systems. Stabilizing consensus requires non-faulty nodes to eventually agree on one of their inputs, but individual nodes do not necessarily know when agreement is reached. First we show that, similar to the original consensus problem in the synchronous model, there exist deterministic solutions to the stabilizing consensus problem tolerating crash faults. Similarly, stabilizing consensus can also be solved deterministically in presence of Byzantine faults with the assumption that n > 3f where n is the number of nodes and f is the number of faulty nodes. Our main result is a Byzantine consensus protocol in a model in which the input to each node can change finitely many times during execution and eventually stabilizes. Finally we present an impossibility result for stabilizing consensus in systems of identical nodes.