Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region). More

The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R # -tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

With the advent of XML as a standard for data representation and exchange on the Internet, storing and querying XML data becomes more and more important. Several XML query languages have been proposed, and the common feature of the languages is the use of regular path expressions to query XML data. This poses a new challenge concerning indexing and searching XML data, because conventional approaches based on tree traversals may not meet the processing requirements under heavy access requests. In this paper, we propose a new system for indexing and storing XML data based on a numbering scheme for elements. This numbering scheme quickly determines the ancestor-descendant relationship between elements in the hierarchy of XML data. We also propose several algorithms for processing regular path expressions, namely, (1) ##-Join for searching paths from an element to another, (2) ##-Join for scanning sorted elements and attributes to find element-attribute pairs, and (3) ##-Join for finding Kleene-Closure on repeated paths or elements. The ##-Join algorithm is highly effective particularly for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation show that the proposed algorithms can process XML queries with regular path expressions by up to an or- # This work was sponsored in part by National Science Foundation CAREER Award (IIS-9876037) and Research Infrastructure program EIA-0080123. The authors assume all responsibility for the contents of the paper. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its...

. Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Querying is based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both querying and indexing. 1 Introduction From a user's point of view, the performance of an information retrieval system can be measured by the quality and speed with which it answers the user's information need. Several factors contribute to overall performance: -- the time required to run each individual query, -- the quality (precision/recall) of each i...

We show how to index mobile objects in one and two dimensions using efficient dynamic external memory data structures. The problem is motivated by real life applications in traffic monitoring, intelligent navigation and mobile communications domains. For the 1-dimensional case, we give (i) a dynamic, external memory algorithm with guaranteed worst case performance and linear space and (ii) a practical approximation algorithm also in the dynamic, external memory setting, which has linear space and expected logarithmic query time. We also give an algorithm with guaranteed logarithmic query time for a restricted version of the problem. We present extensions of our techniques to two dimensions. In addition we give a lower bound on the number of I/O's needed to answer the d-dimensional problem. Initial experimental results and comparisons to traditional indexing approaches are also included. 1 Introduction Traditional database management systems assume that data stored in the database rem...

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With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While algorithms have been proposed that compute RNN queries for non-moving objects, there have been no proposals for answering RNN queries for continuously moving objects. Another such query is the nearest neighbor (NN) query, which has been studied extensively and in many contexts. Like the RNN query, the NN query has not been explored for moving query and data points. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. As a part of the solution to this problem and as a separate contribution, an algorithm for answering NN queries for continuously moving points is also proposed. The results of performance experiments are reported.

We consider the problem of indexing general database workloads (combinations of data sets and sets of potential queries). We define a framework for measuring the efficiency of an indexing scheme for a workload based on two characterizations: storage redundancy (how many times each item in the data set is stored), and access overhead (how many times more blocks than necessary does a query retrieve). Using this framework we present some initial results, showing upper and lower bounds and trade-offs between them in the case of multi-dimensional range queries and set queries. 1 Introduction The success and ubiquity of the relational data model arguably owes much to the B-tree, the access method breakthrough that accompanied it with superb timing [2]. It seems likely that access methods will continue to play an important role in, and largely determine the viability of, the novel data models currently under intense scrutiny in the database research community. The B-tree is widely recognized...

Answering aggregate queries like SUM, COUNT, MIN, MAX, AVG in an approximate manner is often desirable when the exact answer is not needed or too costly to compute. We present an algorithm for answering such queries in multi-dimensional databases, using selective traversal of a Multi-Resolution Aggregate (MRA) tree structure storing point data. Our approach provides 100% intervals of confidence on the value of the aggregate and works iteratively, coming up with improving quality answers, until some error requirement is satisfied or time constraint is reached. Using the same technique we can also answer aggregate queries exactly and our experiments indicate that even for exact answering the proposed data structure and algorithm are very fast. 1 Introduction We deal with the problem of answering aggregate queries in a multi-dimensional space containing point data items. The data space is R space ` ! d where d is the dimensionality. Data items are pairs (loc; values) where loc 2 R spa...

The dataset used for this benchmark is taken from the publicly available Barton Libraries dataset [1]. This data is provided by the Simile Project [3], which develops tools for library data management and interoperability. The data contains records that compose an RDF-formatted dump of the MIT Libraries Barton catalog, converted from raw data stored in an old library format standard called MARC (Machine Readable Catalog). Because of the multiple sources the data was derived from and the diverse nature of the data that is cataloged, the structure of the data is quite irregular. At the time of publication of this report, there are slightly more than 50 million triples in the dataset, with a total of 221 unique properties, of which the vast majority appear infrequently. Of these properties, 82 (37%) are multi-valued, meaning that they appear more than once for a given subject; however, these properties appear more often (77 % of the triples have a multi-valued property). The dataset provides a good demonstration of the relatively unstructured nature of Semantic Web data. 2. LONGWELL OVERVIEW Longwell [2] is a tool developed by the Simile Project, which provides a graphical user interface for generic RDF data exploration in a web browser. It begins by presenting the user with a list of the values the type property can take (such as Text or Notated Music in the library dataset). The user can click on the types of data he desires to further explore. Longwell shows the list of currently filtered resources (RDF subjects) in the main portion of the screen, and a list of filters in panels along the side. Each panel represents a property that is defined on the current filter, with popular object values for that property and their frequency also presented in this box. If the user selects an object value, this filters the working set of resources to those that have that property-object value defined,