Visual Place Recognition (VPR) is essential for autonomous robots and unmanned vehicles, as an accurate identification of visited places can trigger a loop closure to optimize the built map. The most prevalent methods tackle VPR as a single-frame retrieval task, which uses a CNN-based encoder to describe and compare each individual frame. These methods, however, overlook the temporal information between frames. Other methods improve this by searching the database with consecutive frames, which can greatly reduce false positives. Nevertheless, current sequence-based methods typically assume the consecutive image frames to be captured at an approximately constant speed, which is not always the case in practice. Therefore, we propose an adaptive sequence search strategy (AdaptSeq), which can dynamically alter the step size of adjacent frames in the retrieved sequence trajectory. Furthermore, to address false positive retrieval of input frames, we propose a CNN-based discriminator named DDsNet. It can determine whether the top retrieved candidates are true positives based on the learned statistics rather than an artificial threshold. Overall, we construct a novel sequence-based VPR pipeline named AdaptSeqVPR. It utilizes a CNN-based encoder for frame descriptions, and encompasses AdaptSeq and DDsNet for sequence matching. The experimental results indicate that our AdaptSeqVPR exhibits superior performance compared to the baseline SeqSLAM and SeqVLAD. Notably, our method can robustly handle the sequence-based VPR for vehicles traveling at non-uniform speeds in changing environments.

Text representation plays a crucial role in classical text mining, where the primary focus was on static text. Nevertheless, well-studied static text representations including TFIDF are not optimized for non-stationary streams of information such as news, discussion board messages, and blogs. We therefore introduce a new temporal representation for text streams based on bursty features. Our bursty text representation differs significantly from traditional schemes in that it 1) dynamically represents documents over time, 2) amplifies a feature in proportional to its burstiness at any point in time, and 3) is topic independent. Our bursty text representation model was evaluated against a classical bag-of-words text representation on the task of clustering TDT3 topical text streams. It was shown to consistently yield more cohesive clusters in terms of cluster purity and cluster/class entropies. This new temporal bursty text representation can be extended to most text mining tasks involving a temporal dimension, such as modeling of online blog pages.

The emergence of sensor networks enables applications that deploy sensors to collaboratively monitor environment and process data collected. In some scenarios, we are interested in using join queries to correlate data stored in different regions of a sensor network, where the data volume is large, making it prohibitive to transmit all data to a central server for joining. In this paper, we present an in-network synopsis join strategy for evaluating join queries in sensor networks with communication efficiency. In this strategy, we prune data that do not contribute to the join results in the early stage of the join processing, therefore reducing unnecessary communication overhead. In our simulation-based experiments, we study the performance of synopsis join for different join selectivities and investigate the impact synopsis accuracy and message loss. The results show that synopsis join outperforms the centralized join scheme in terms of communication cost, especially for low join selectivities, thus prolonging the lifetime of the sensor network.

k-closest pair query is a useful type of query in many practical applications involving spatial data for decision making. The traditional techniques to handle k-closest pair queries generally assume that the objects are static. In this paper, we study the problem of k-closest pair monitoring (kCPM) over moving objects. Aiming at reducing communication cost for location updates between the clients and the server, our proposed kCPM approach achieves high monitoring accuracy with less CPU utilization compared to existing periodical location update schemes.

Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric. In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length of the shortest path that connects them without crossing any obstacles. We propose efficient algorithms for the most important query types, namely, range search, nearest neighbours, e‐distance joins, closest pairs and distance semi‐joins, assuming that both data objects and obstacles are indexed by R‐trees. The effectiveness of the proposed solutions is verified through extensive experiments.

Given two spatial data sets A and B, a top-k spatial join retrieves the k objects from A or B that intersect the largest number of objects from the other data set. Depending on the application requirements, there exist several variations of the problem. For instance, B may be a point data set, and the goal may be to retrieve the regions of A that contain the maximum number of points. The processing of such queries with conventional spatial join algorithms is expensive. However, several improvements are possible based on the fact that we only require a small subset of the result (instead of all intersection/containments pairs). In this paper, we propose output-sensitive algorithms for top-k spatial joins that utilize a variety of optimizations for reducing the overhead.

Existing models for nearest neighbor search in multidimensional spaces are not appropriate for query optimization because they either lead to erroneous estimation or involve complex equations that are expensive to evaluate in real-time. This article proposes an alternative method that captures the performance of nearest neighbor queries using approximation. For uniform data, our model involves closed formulae that are very efficient to compute and accurate for up to 10 dimensions. Further, the proposed equations can be applied on nonuniform data with the aid of histograms. We demonstrate the effectiveness of the model by using it to solve several optimization problems related to nearest neighbor search.

In this paper we propose an approach that enables mobile clients to determine the validity of previous queries based on their current locations. In order to make this possible, the server returns in addition to the query result, a validity region around the client's location within which the result remains the same. We focus on two of the most common spatial query types, namely nearest neighbor and window queries, define the validity region in each case and propose the corresponding query processing algorithms. In addition, we provide analytical models for estimating the expected size of the validity region. Our techniques can significantly reduce the number of queries issued to the server, while introducing minimal computational and network overhead compared to traditional spatial queries.

Spatio-temporal databases store information about the positions of individual objects over time. In many applications, however, such as traffic supervision or mobile communication systems, only summarized data, like the average number of cars in an area for a specific period, or the number of phones serviced by a cell each day, is required. Although this information can be obtained from operational databases, its computation is expensive, rendering online processing inapplicable. A vital solution is the construction of a spatio-temporal data warehouse. In this paper, we describe a framework for supporting OLAP operations over spatio-temporal data. We argue that the spatial and temporal dimensions should be modeled as a combined dimension on the data cube and we present data structures which integrate spatio-temporal indexing with pre-aggregation. While the well-known materialization techniques require a-priori knowledge of the grouping hierarchy, we develop methods that utilize the proposed structures for efficient execution of ad-hoc group-bys. Our techniques can be used for both static and dynamic dimensions.

Overlapping and multi-version techniques are two popular frameworks that transform an ephemeral index into a multiple logical-tree structure in order to support versioning databases. Although both frameworks have produced numerous efficient indexing methods, their performance analysis is rather limited; as a result, there is no clear understanding about the behavior of the alternative structures and the choice of the best one, given the data and query characteristics. Furthermore, query optimization based on these methods is currently impossible. These are serious problems due to the incorporation of overlapping and multi-version techniques in several traditional (e.g. banking) and emerging (e.g. spatio-temporal) applications. In this paper, we propose frameworks for reducing the performance analysis of overlapping and multi-version structures to that of the corresponding ephemeral structures, thus simplifying the problem significantly. The frameworks lead to accurate cost models that predict the sizes of the trees, the node accesses and query selectivity. Although we focus on B-tree-based structures, the proposed models can be employed with a variety of indexes.

Overlapping and multiversion techniques are two popular frameworks that transform an ephemeral index into a multiple logical-tree structure in order to support versioning databases. Although both frameworks have produced numerous efficient indexing methods, their performance analysis is rather limited; as a result there is no clear understanding about the behavior of the alternative structures and the choice of the best one, given the data and query characteristics. Furthermore, query optimization based on these methods is currently impossible. These are serious problems due to the incorporation of overlapping and multiversion techniques in several traditional (e.g., financial) and emerging (e.g., spatiotemporal) applications. In this article, we reduce performance analysis of overlapping and multiversion structures to that of the corresponding ephemeral structures, thus simplifying the problem significantly. This reduction leads to accurate cost models that predict the sizes of the trees, the node/page accesses, and selectivity of queries. Furthermore, the models offer significant insight into the behavior of the structures and provide guidelines about the selection of the most appropriate method in practice. Extensive experimentation proves that the proposed models yield errors below 5 and 15% for uniform and nonuniform data, respectively.