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The management of geometric objects is a prime example of an application where efficiency is the bottleneck; this bottleneck cannot be eliminated without using suitable access structures. The most popular approach for handling complex spatial objects in spatial access methods is to use their minimum bounding boxes as a geometric key. Obviously, the rough approximation by bounding boxes provides a fast but inaccurate filter for the set of answers to a query. In order to speed up the query processing by a better approximation quality, we investigate six different types of approximations. Depending on the complexity of the objects and the type of queries, the approximations 5-corner, ellipse and rotated bounding box clearly outperform the bounding box. An important ingredient of our approach is to organize these approximations in efficient spatial access

As CPU performance has rapidly improved, increased pressure has been placed on the performance of accessing external data in order to keep up with demand. Increasingly often the I/O subsystem and related software is unable to meet this demand and valuable CPU resources are left underutilized while users are forced to wait longer than necessary for results. This problem is especially acute for many scientific applications which use large data sets and require high performance. This paper presents our experiences with working to alleviate an I/O bottleneck in radio astronomy applications at the National Radio Astronomy Observatory (NRAO). We first present our model, the ExtensibLe File Systems model (ELFS), for improving both the performance of data access and the usability of access software. We then present the current situation at NRAO, our solution, performance results and our plans for future work. 1 . 1: Introduction Over the past decade raw CPU processing power has increased dr...

Spatial join finds pairs of spatial objects having a specific spatial relationship in spatial database systems. Since spatial join is a fairly expensive operation, we need an efficient algorithm taking advantage of the characteristics of available spatial access methods. In this paper, we propose a spatial join algorithm using corner transformation and show its excellence through experiments. To the extent of authors' knowledge, the spatial join processing using corner transformation is new. In corner transformation, two regions in one file joined with two adjacent regions in the other file share a large common area. The proposed algorithm utilizes this property in order to reduce the number of disk accesses for spatial join. Experimental results show that the performace of the algorithm is generally better than that of the R*-tree based algorithm proposed by Brinkhoff et al. This is a strong indication that corner transformation is a promising category of spatial access methods and th...

We propose the notion of tight-coupling [8] to add new data types into the DBMS engine. In this paper, we introduce the Odysseus ORDBMS and present its tightly-coupled IR features (U.S. patented). We demonstrate a web search engine capable of managing 20 million web pages in a nonparallel configuration using Odysseus. 1

ii Abstract This thesis presents three trie organizations for various binary tries. The new trie structures have two distinctive features: (1) they store no pointers and require two bits per node in the worst case, and (2) they partition tries into pages and are suitable for secondary storage. We apply trie structures to indexing, storing and querying both text and spatial data on secondary storage. We are interested in practical problems such as storage compactness, I/O efficiency, and large trie construction. We use our tries to index and search arbitrary substrings of a text. For an index of 100 million keys, our trie is 10 %- 25 % smaller than the best known method. This difference is important since the index size is crucial for trie methods. We provide methods for dynamic tries and allow texts to be changed. We also use our tries to compress and approximately search large dictionaries. Our algorithm can find strings with k mismatches in sublinear time. To our knowledge, no other published sublinear algorithm is known for this problem. Besides, we use our tries to store and query spatial data such as maps. A trie structure is proposed to permit querying and retrieving spatial data at arbitrary levels of resolution, without reading from secondary storage any more data than is needed for the specified resolution. The trie structure also compresses spatial data substantially. The performance results on map data have confirmed our expectations: the querying cost is linear in the amount of data needed and independent of the data size in practice. We give algorithms for a set of sample queries including geometrical selection, geometrical join and the nearest neighbour. We also show how to control query cost by specifying an acceptable resolution.

this paper discusses our approach and the current NRAO environment in more detail and then presents the details of phase one of the project, including a brief discussion of the file structure chosen, a sketch of the implementation and performance results and observations. The final section presents our future plans for the remainder of the project and beyond. 4

Recent data stream systems such as TelegraphCQ have employed the well-known property of duality between data and queries. In these systems, query pro-cessing methods are classified into two dual categories – data-initiative and query-initiative – depending on whether query processing is initiated by selecting a data element or a query. Although the duality property has been widely recognized, pre-vious data stream systems do not fully take advantages of this property since they use the two dual methods independently: data-initiative methods only for continu-ous queries and query-initiative methods only for ad-hoc queries. We contend that continuous query processing can be better optimized by adopting an approach that integrates the two dual methods. Our primary contribution is based on the obser-vation that spatial join is a powerful tool for achieving this objective. In this paper, we first present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We then present a continuous query processing algorithm based on spatial join, which we name Spatial Join CQ. This algorithm processes continuous queries by finding the pairs of overlapping re-gions from a set of data elements and a set of queries, both defined as regions in the multi-dimensional space. The algorithm achieves the advantages of the two dual methods simultaneously. Experimental results show that the proposed algorithm outperforms earlier algorithms by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join queries. 1 I

The R -Tree index is a popular multidimensional index used in several extensible and GIS-oriented database systems. In this paper, we show that a simple refinement of the search algorithm of the R -Tree---which is common to all variants of the R-Tree---offers significant speedups in most cases, with little or no worst-case performance penalty. The idea is essentially to use a conjunction of linear constraints (rather than a minimum bounding retangle) to approximate the query and to use this tighter bounding envelope to determine when the query overlaps with an R -Tree node. This raises an important question: How can we efficiently check whether the query envelope overlaps the minimum bounding box for a tree node? Linear Programming (LP) offers one solution, but it is susceptible to numeric approximation errors. One of the contributions of this paper is a new algorithm for performing this check check that is more efficient than LP and free from numeric errors. We also present...

Geographic database systems, known as geographic information systems (GISs) particularly among noncomputer scientists, are one of the most important applications of the very active research area named spatial database systems. Additionally to other spatial database systems where query and manipulation processing are emphasized, the most important purpose of a GIS is to analyze geographical data. The basic building block for analysis operations in GIS is the operation map overlay. However, available map overlay algorithms suffer from poor performance. In this paper, we present an efficient map overlay algorithm using as ingredients spatial access methods and state-of-the-art computational geometry concepts. An experimental performance analysis of our implemented map overlay algorithm demonstrates the fruitfulness of the marriage of spatial access methods with computational geometry methods for improving the efficiency of GISs. The possibility of coupling these two areas is based on the fact that both use spatial order relations in order to increase the performance of query processing.