The emergence of location-aware services calls for new real-time spatio-temporal query processing algorithms that deal with large numbers of mobile objects and queries. In this demo, we present PLACE (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server addresses scalability by adopting an incremental evaluation mechanism for answering concurrently executing continuous spatiotemporal queries. The PLACE server supports a wide variety of stationery and moving continuous spatio-temporal queries through a set of pipelined spatio-temporal operators. The large numbers of moving objects generate real-time spatio-temporal data streams.

Abstract. The query for retrieving the rank of all neighbors of a moving object at any given time, a continuous rank query, is an important case of continuous nearest neighbor (CNN) queries. An application for ranking queries is given by an ambulance driver who needs to keep track of the closest hospitals at all times. We present a set of incremental algorithms that facilitate efficient rank updates for some or all neighbors of a moving query point. The proposed algorithms allow us not only to maintain the exact rank of all n neighbors at any given time but also to track the rank of a subset of all neighbors. We show that updates for these continuous rank queries can be performed in linear time for arbitrary polygonal curves in two dimensions and in logarithmic time for movements along a fixed direction. Instead of using Voronoi diagrams, our algorithms are based on small subsets of all bisectors between neighbors. We prove that it is sufficient to keep track of only n − 1 bisectors for all n neighbors. The algorithms for maintaining the rank only require minimal incremental updates on the bisector sets. 1

Abstract—Given two datasets A and B, their exclusive closest pairs (ECP) join is a one-to-one assignment of objects from the two datasets, such that (i) the closest pair (a, b) in A × B is in the result and (ii) the remaining pairs are determined by removing objects a, b from A, B respectively, and recursively searching for the next closest pair. A real application of exclusive closest pairs is the computation of (car, parking slot) assignments. This paper introduces the problem and proposes several solutions that solve it in mainmemory, exploiting space partitioning. In addition, we define a dynamic version of the problem, where the objective is to continuously monitor the ECP join solution, in an environment where the joined datasets change positions and content. Finally, we study an extended form of the query, where objects in one of the two joined sets (e.g., parking slots) have a capacity constraint, allowing them to match with multiple objects from the other set (e.g., cars). We show how our techniques can be extended for this variant and compare them with a previous solution to this problem. Experimental results on a system prototype demonstrate the efficiency and applicability of the proposed algorithms.

Data Stream Management Systems (DSMSs) must support optimized execution scheduling of multiple continuous queries on massive, and frequently bursty, data streams. Previous approaches on optimizing memory consumption or response time (i.e., latency) usually produce very different algorithms. In this paper, we extend the popular chart-partitioning procedure, which was previously used for memory optimization on simple operator paths, to minimize latency as well as memory on complex query-graphs with tuple-sharing forks. Furthermore, we test the performance of algorithms that only assume knowledge of the average behavior of tuples and operators, against a theoretical one that assumes detailed knowledge on the behavior of individual tuples. These experiments show that the practical algorithms closely approximate the performance of the optimal ones. Categories and Subject Descriptors H.2.4 [Database Management]: Systems—Query processing;

The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest. In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and locationdependent queries are defined and classified, and different query processing approaches are reviewed and compared.

The emergence of modern data-intensive applications requires sophisticated database techniques for processing advanced types of user queries on massive data. In this paper, we study such a new type of query, called progressive queries. A progressive query is defined as a set of interrelated and incrementally formulated step-queries. A step-query in a progressive query PQ is specified on the fly based on the results of previously-executed step-queries in PQ. Hence, a progressive query cannot be formulated in advance before its execution, which raises challenges for its processing and optimization. We introduce a query model to characterize different types of progressive queries. We then present a new index structure, called the collective index, to efficiently process progressive queries. The collective index technique incrementally evaluates step-queries via dynamically maintained member indexes. Utilizing the special structure of a collective index, the (member) indexes on the input relation(s) of a step-query are efficiently transformed into indexes on the result relation. Algorithms to efficiently process single-input (unary) linear and multipleinput (join) linear progressive queries based on the collective index are presented. Our experiment results show that the proposed collective index technique outperforms the conventional query processing methods in processing progressive queries.

The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest. In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and locationdependent queries are defined and classified, and different query processing approaches are reviewed and compared.

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