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This work introduces decentralized query processing techniques based on MIDAS, a novel distributed multidimensional index. In particular, MIDAS implements a distributed k-d tree, where leaves correspond to peers, and internal nodes dictate message routing. MIDAS requires that peers maintain little network information, and features mechanisms that support fault tolerance and load balancing. The proposed algorithms process point and range queries over the multidimensional indexed space in only O(log n) hops in expectance, where n is the network size. For nearest neighbor queries, two processing alternatives are discussed. The first, termed eager processing, has low latency (expected value of O(log n) hops) but may involve a large number of peers. The second, termed iterative processing, has higher latency (expected value of O(log n) hops) but involves far fewer peers. A detailed experimental evaluation demonstrates that our query processing techniques outperform existing methods for settings involving real spatial data as well as in the case of high dimensional synthetic data. [ABSTRACT FROM AUTHOR]

The widespread availability of GPS and the growing popularity of location based social networking applications such as Flickr, Yelp, etc., enable more and more users to share their route activities or trajectories. At the same time, the recent advancement in large-scale 3D modeling has inspired applications that combine visibility and spatial queries, which in turn can be integrated with user trajectories to provide answers for many real-life user queries, such as "How can I choose the route which provides the best view of a historic site?". In this work, we propose and investigate the k Aggregate Maximum Visibility Trajectory (k AMVT) query and its variants. Given sets of targets, obstacles, and trajectories, the k AMVT query finds top-k trajectories that provide the best view of the targets. We extend the k AMVT query to incorporate different weights (or preferences) with trajectories and targets. To provide an efficient solution to our problem, we employ obstacle and trajectory pruning mechanisms. We also employ an effective target ordering technique, which can further improve query efficiency. Furthermore, we extend the proposed queries to introduce preferences on trajectories in situations where smaller trajectories are preferred due to time constraints, or trajectories closer to the query user are preferred. To verify the efficiency and effectiveness of our solutions, we conduct an extensive experimental study using large synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Finding the occurrences of structural patterns in XML data is a key operation in XML query processing. Existing algorithms for this operation focus almost exclusively on path patterns or tree patterns. Current applications of XML require querying of data whose structure is complex or is not fully known to the user, or integrating XML data sources with different structures. These applications have motivated recently the introduction of query languages that allow a partial specification of path patterns in a query. In this paper, we consider partial path queries, a generalization of path pattern queries, and we focus on their efficient evaluation under the indexed streaming evaluation model. Our approach explicitly deals with repeated labels (that is, multiple occurrences of the same label in a query). We show that partial path queries can be represented as rooted dags for which a topological ordering of the nodes exists. We present three algorithms for the efficient evaluation of these queries. The first one exploits a structural summary of data to generate a set of path patterns that together are equivalent to a partial path query. To evaluate these path patterns, we extend a previous algorithm for path-pattern queries so that it can work on path patterns with repeated labels. The second one extracts a spanning tree from the query dag, uses a stack-based algorithm to find the matches of the root-to-leaf paths in the tree, and merge-joins the matches to compute the answer. Finally, the third one exploits multiple pointers of stack entries and a topological ordering of the query dag to apply a stack-based holistic technique. We analyze our algorithms and perform extensive experimental evaluations. Our experimental results show that the holistic algorithm outperforms the other ones. Our approaches are the first ones to efficiently evaluate this class of queries in the indexed streaming model. [ABSTRACT FROM AUTHOR]