Many location-based applications require constant monitoring of k-nearest neighbor (k-NN) queries over moving objects within a geographic area. Existing approaches to this problem have focused on predictive queries, and relied on the assumption that the trajectories of the objects are fully predictable at query processing time. We relax this

This paper proposes a generic framework for monitoring continuous spatial queries over moving objects. The framework distinguishes itself from existing work by being the first to address the location update issue and to provide a common interface for monitoring mixed types of queries. Based on the notion of safe region, the client location update strategy is developed based on the queries being monitored. Thus, it significantly reduces the wireless communication and query reevaluation costs required to maintain the upto-date query results. We propose algorithms for query evaluation/reevaluation and for safe region computation in this framework. Enhancements are also proposed to take advantage of two practical mobility assumptions: maximum speed and steady movement. The experimental results show that our framework substantially outperforms the traditional periodic monitoring scheme in terms of monitoring accuracy and CPU time while achieving a close-to-optimal wireless communication cost. The framework also can scale up to a large monitoring system and is robust under various object mobility patterns. 1.

Spatio-temporal objects — that is, objects that evolve over time — appear in many applications. Due to the nature of such applications, storing the evolution of objects through time in order to answer historical queries (queries that refer to past states of the evolution) requires a very large specialized database, what is termed in this article as a spatio-temporal archive. Efficient processing of historical queries on spatio-temporal archives requires equally sophisticated indexing schemes. Typical spatio-temporal indexing techniques represent the objects using minimum bounding regions (MBR) extended with a temporal dimension, which are then indexed using traditional multi-dimensional index structures. However, rough MBR approximations introduce excessive overlap between index nodes which deteriorates query performance. This article introduces a robust indexing scheme for answering spatio-temporal queries more efficiently. A number of algorithms and heuristics are elaborated, which can be used to preprocess a spatiotemporal archive in order to produce finer object approximations which, in combination with a multi-version index structure, will greatly improve query performance in comparison to the straightforward approaches. The proposed techniques introduce a query-efficiency vs. space tradeoff, that can help tune a structure according to available resources. Empirical observations for estimating the necessary amount of additional storage space required for improving query performance by a given factor are also provided. Moreover, heuristics for applying the proposed ideas in an online setting are discussed. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques.

Abstract — Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object’s movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object’s movements are more complicated than what the mathematical formulas can represent. Prediction based on an object’s trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object’s future locations based on its pattern information as well as existing motion functions using the object’s recent movements. Specifically, an object’s trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes. I.

Abstract—This paper considers the problem of efficiently maintaining a clustering of a dynamic set of data points that move continuously in two-dimensional euclidean space. This problem has received little attention and introduces new challenges to clustering. The paper proposes a new scheme that is capable of incrementally clustering moving objects. This proposal employs a notion of object dissimilarity that considers object movement across a period of time, and it employs clustering features that can be maintained efficiently in incremental fashion. In the proposed scheme, a quality measure for incremental clusters is used for identifying clusters that are not compact enough after certain insertions and deletions. An extensive experimental study shows that the new scheme performs significantly faster than traditional ones that frequently rebuild clusters. The study also shows that the new scheme is effective in preserving the quality of moving-object clusters. Index Terms—Spatial databases, temporal databases, clustering. Ç 1

Sensor networks are often used to perform monitoring tasks, such as in animal or vehicle tracking and in surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries that allow us to report interesting events that are observed by nodes in the network that are within certain distance of each other. An event is triggered when a userprogrammable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks using existing communication protocols and then propose an efficient algorithm that can process multiple proximity queries, involving several different event types. Our solution utilizes a distributed routing index, maintained by the nodes in the network that is dynamically updated as new observations are obtained by the nodes. We present an extensive experimental study to show the benefits of our techniques under different scenarios. Our results demonstrate that our algorithms scale better and require orders of magnitude fewer messages compared to a straightforward computation of the queries.

Abstract—Complex queries on trajectory data are increasingly common in applications involving moving objects. MBR or grid-cell approximations on trajectories perform suboptimally since they do not capture the smoothness and lack of internal area of trajectories. We describe a parametric space indexing method for historical trajectory data, approximating a sequence of movement functions with single continuous polynomial. Our approach works well, yielding much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this method, and show through extensive experiments that PA-trees have excellent performance for offline and online spatio-temporal range queries. Compared to MVR-trees, PA-trees are an order of magnitude faster to construct and incur I/O cost for spatio-temporal range queries lower by a factor of 2-4. SETI is faster than our method for index construction and timestamp queries, but incurs twice the I/O cost for time interval queries, which are much more expensive and are the bottleneck in online processing. Therefore, the PA-tree is an excellent choice for both offline and online processing of historical trajectories. Index Terms—Access methods, spatio-temporal databases. 1

Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modeling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the B x-tree that offer insights into the properties of the paper’s proposal. 1.

In spatio-temporal applications, moving objects detect their locations via location-aware devices and update their locations continuously to the server. With the ubiquity and massive numbers of moving objects, many spatio-temporal access methods are developed to process user queries efficiently. Spatiotemporal access methods are classified into four categories: (1) Indexing the past data, (2) Indexing the current data, (3) Indexing the future data, and (4) Indexing data at all points of time. This short survey IS

We study the problem of mining and summarizing multiple time series effectively and efficiently. We propose PLiF, a novel method to discover essential characteristics (“fingerprints”), by exploiting the joint dynamics in numerical sequences. Our fingerprinting method has the following benefits: (a) it leads to interpretable features; (b) it is versatile: PLiF enables numerous mining tasks, including clustering, compression, visualization, forecasting, and segmentation, matching top competitors in each task; and (c) it is fast and scalable, with linear complexity on the length of the sequences. We did experiments on both synthetic and real datasets, including human motion capture data (17MB of human motions), sensor data (166 sensors), and network router traffic data (18 million raw updates over 2 years). Despite its generality, PLiF outperforms the top clustering methods on clustering; the top compression methods on compression (3 times better reconstruction error, for the same compression ratio); it gives meaningful visualization and at the same time, enjoys a linear scale-up. 1.