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Given two datasets and , their exclusive closest pairs (ECP) join is a one-to-one assignment of objects from the two datasets, such that (i) the closest pair (,) in × is in the result and (ii) the remaining pairs are determined by removing objects , from , respectively, and recursively searching for the next closest pair. An application of exclusive closest pairs is the computation of (car, parking slot) assignments. In this paper, we propose algorithms for the computation and continuous monitoring of ECP joins in memory, given a stream of events that indicate dynamic assignment requests and releases of pairs. Experimental results on a system prototype demonstrate the efficiency of our solutions in practice.

The one-shot shortest path query has been studied for decades. However, in the applications on road networks, users are actually interested in the path with the minimum travel time (the ), which varies as time goes. This motivates us to study the continuous evaluation of fastest path queries in order to capture the dynamics of road networks. Repeatedly evaluating a large number of fastest path queries at every moment is infeasible due to its computationally expensive cost. We propose a novel approach that employs the concept of the affecting area and the tolerance parameter to avoid the reevaluation while the travel time of the current answer is close enough to that of the fastest path. Furthermore, a grid-based index is designed to achieve the efficient processing of multiple queries. Experiments on real datasets show significant reduction on the total amount of reevaluation and therefore the cost for reevaluating a query.

In the -medoid problem, given a dataset , we are asked to choose points in as the . The optimal medoid set minimizes the average Euclidean distance between the points in and their closest medoid. Finding the optimal medoids is NP hard, and existing algorithms aim at approximate answers, i.e., they compute medoids that achieve a small, yet not minimal, average distance. Similarly in this paper, we also aim at approximate solutions. We consider, however, the continuous version of the problem, where the points in move and our task is to maintain the medoid set on-the-fly (trying to keep the average distance small). To the best of our knowledge, this work constitutes the first attempt on continuous medoid queries. First, we consider monitoring, where the points issue location updates whenever they move. A server processes the stream of generated updates and constantly reports the current medoid set. Next, we address monitoring, where we assume that the data points have some computational capabilities, and they take over part of the monitoring task. In particular, the server installs adaptive filters (i.e., permissible spatial ranges, called ) to the points, which report their location only when they move outside their filters. The distributed techniques reduce the frequency of location updates (and, thus, the network overhead and the server load), at the cost of a slightly higher average distance, compared to the centralized methods. Both our centralized and distributed methods do not make any assumption about the data moving patterns (e.g., velocity vectors, trajectories, etc) and can be applied to an arbitrary number of medoids . We demonstrate the efficiency and efficacy of our techniques through extensive experiments.

Recent years have revealed a growing importance of (VR) visualization techniques which offer comfortable means to enable users to interactively explore 3D data sets. Particularly in the field of (CFD), the rapidly increasing size of data sets with complex geometric and supplementary scalar information requires new out-of-core solutions for fast isosurface extraction and other CFD post-processing tasks. Whereas spatial access methods overcome the limitations of main memory size and support fast data selection, their VR support needs to be improved. Firstly, interactive users strongly depend on quick first views of the regions in their view direction and, secondly, they require quick relevant views even when they change their view point or view direction.

We develop novel view-dependent extensions for access methods which support static and dynamic scenarios. Our new human vision-oriented distance function defines an adjusted order of appearance for data objects in the visualization space and, thus, supports quick first views. By a novel incremental concept of view-dependent result streaming which interactively follows dynamic changes of users’ viewpoints and view directions, we provide a high degree of interactivity and mobility in VR environments. Our integration into the new “IndeGS” proves the efficiency of our techniques in the context of post-processing CFD data with dynamically interacting users.

Similarity search algorithms that directly rely on index structures and require a lot of distance computations are usually not applicable to databases containing complex objects and defining costly distance functions on spatial, temporal and multimedia data. Rather, the use of an adequate multi-step query processing strategy is crucial for the performance of a similarity search routine that deals with complex distance functions. Reducing the number of candidates returned from the filter step which then have to be exactly evaluated in the refinement step is fundamental for the efficiency of the query process. The state-of-the-art multi-step -nearest neighbor (NN) search algorithms are designed to use only a lower bounding distance estimation for candidate pruning. However, in many applications, also an upper bounding distance approximation is available that can additionally be used for reducing the number of candidates. In this paper, we generalize the traditional concept of -optimality and introduce the notion of -optimality depending on the distance information available in the filter step. We propose a new multi-step NN search algorithm that utilizes lower- and upper bounding distance information () in the filter step. Furthermore, we show that, in contrast to existing approaches, our proposed solution is -optimal. In an experimental evaluation, we demonstrate the significant performance gain over existing methods.

Mobile services is emerging as an important application area for spatio-temporal database management technologies. Service users are often constrained to a spatial network, e.g., a road network, through which points of interest, termed data points, are accessible. Queries that implement services will often concern data points of some specific type, e.g., Thai restaurants or art museums. As a result, the relatively few data points are relevant to a query in comparison to the number of network edges, meaning that queries, e.g., nearest-neighbor queries, must access large portions of the network.

Existing query processing techniques pre-compute distances between data points and network vertices for improving the performance. However, pre- computation becomes problematic when the network or data points must be updated, possibly concurrently with the querying; and if the data points are moving, the existing techniques are inapplicable. In addition, multiple pre-computed structures must be maintained—one for each type of data point. We propose a versatile pre-computation approach for spatial network data. This approach uses a grid for pre-computing a simplified network. The above-mentioned shortcomings are avoided by making the pre-computed data independent of the data points. Empirical performance studies show that the structure is competitive with respect to the existing, more specialized techniques.

With the increasing availability of spatial data in many applications, spatial clustering and outlier detection have received a lot of attention in the database and data mining community. As a very prominent method, the spatial scan statistic finds a region that deviates (most) significantly from the entire dataset. In this paper, we introduce the novel problem of mining regional outliers in spatial data. A spatial regional outlier is a rectangular region which contains an outlying object such that the deviation between the non-spatial attribute value of this object and the aggregate value of this attribute over all objects in the region is maximized. Compared to the spatial scan statistic, which targets global outliers, our task aims at local spatial outliers. We introduce two greedy algorithms for mining regional outliers, growing regions by extending them by at least one neighboring object per iteration, choosing the extension which leads to the largest increase of the objective function. Our experimental evaluation on synthetic datasets and a real dataset demonstrates the meaningfulness of this new type of outliers and the greatly superior efficiency of the proposed algorithms.

In-network aggregation has been proposed as one of the main mechanisms for reducing messaging cost (and thus energy) in prior sensor network database research. However, aggregated values of a sensor field are of limited use in natural science domains because many phenomena, e.g., temperature and soil moisture, are actually continuous and thus best represented as a continuous surface over the sensor fields. Energy efficient collection of readings from all sensors became a focus in recent research literature. In this paper, we address the problem of interpolating maps from sensor fields.

We propose a spatial autocorrelation aware, energy efficient, and error bounded framework for interpolating maps from sensor fields. Our work is inspired by spatial autocorrelation based interpolation models commonly used in natural science domains, e.g., kriging, and brings together several innovations. We propose a two round reporting framework that utilizes spatial interpolation models to reduce communication costs and enforce error control. The framework employs a simple and low overhead in-network coordination among sensors for selecting reporting sensors so that the coordination overhead does not eclipse the communication savings. We conducted extensive experiments using data from a real-world sensor network deployment and a large Asian temperature dataset to show that the proposed framework significantly reduces messaging costs.

Massive data streams of positional updates become increasingly difficult to manage under limited memory resources, especially in terms of providing near real-time response to multiple continuous queries. In this paper, we consider online maintenance for spatiotemporal summaries of streaming positions in an aging-aware fashion, by gradually evicting older observations in favor of greater precision for the most recent portions of movement. Although several functions have been proposed for approximation of time series, we opt for a simple, yet quite efficient scheme that achieves contiguity along all retained stream pieces. To this end, we adapt an amnesic tree structure that effectively meets the requirements of time-decaying approximation while taking advantage of the succession inherent in positional updates. We further exemplify the significance of this scheme in two important cases: the first one refers to trajectory compression of individual objects; the other offers estimated aggregates of moving object locations across time. Both techniques are validated with comprehensive experiments, confirming their suitability in maintaining online concise synopses for moving objects.

The notion of a is a fundamental metaphor in spatial disciplines. However, there currently exist no adequate data models for maps that define a precise spatial data type for for use in spatial systems. In this paper, we consider a subclass of map geometries known as that are able to model maps containing region features. However, spatial partitions are defined using concepts such as infinite point sets that cannot be directly represented in computers. We define a graph theoretic model of spatial partitions, called , based on discrete concepts that can be directly implemented in spatial systems.