Nowadays, a significant number of applications require the organization of data R-Trees In Modern Applications. Front Matter. Pages PDF · R-trees in. R-Trees: Theory and Applications watermarked, DRM-free; Included format: PDF; ebooks can be used on all reading devices Dynamic Versions of R-trees. Request PDF on ResearchGate | R-Trees: Theory and Applications | Space support in databases poses new challenges in every part of a database.
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One of the most influential access methods in the area is the R-tree structure . R -tree applications cover a wide spectrum, from spatial and temporal to image. An application in all these cases should rely on R-trees as a necessary tool for data storage and retrieval. R-tree applications cover a wide spectrum, from spatial. R-Trees: Theory and Applications (Advanced Information and Knowledge knowledge in finite model theory and graph structure theory pdf in addition to the .
Wang, Mohammed J. Zaki, Hannu T. Ko, Ben M. Tan, E. Khor and T. Enquiries concerning reproduction outside those terms should be sent to the publishers.
The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Preface Spatial data management has been an active area of intensive research for more than two decades. It seems that due to the modern demanding applications and after academia paved the way, recently the industry has recognized the use and necessity of R-trees.
The simplicity of the structure and its resemblance to the B-tree allowed developers to easily incorporate the structure into existing database management systems to support spatial query processing. Book Organization The book contains nine chapters organized in four parts. Chapter 1 is the introductory chapter. In this chapter we give a brief introduction to the area, present the basic notations and the corresponding descriptions, and present the original R-tree access method, which is the root of the family tree of access methods presented in the book.
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Chapter 2 is devoted to the description of the most promising dynamic variations of the R-tree. These variations are optimized taking into consideration that the dataset to be organized is given a priori. The second part of the book is composed of two chapters and covers query processing techniques that have been proposed to operate with R-trees. Chapter 4 studies fundamental query types such as range queries, nearest-neighbor queries, and spatial join queries.
Each method is studied in detail, and the corresponding algorithm is given in pseudo-code where appropriate. Chapter 5 explores more complex query types such as categorical range queries, multiway spatial joins, closest-pair queries, incremental processing, and approximate query techniques. These queries are characterized by higher computation costs and greater complexity than the fundamental ones, and therefore are covered separately.
The adaptation of the R-tree to modern application domains is discussed in the third part, which comprises two chapters. Chapter 6 studies the application of R-tree-like access methods to spatiotemporal database systems. The fundamental characteristic of these systems is that they handle temporal information in addition to the spatial properties of objects. Chapter 7 discusses the use of R-trees in multimedia databases, data warehouses, and data mining tasks.
The exploitation of the R-tree by the aforementioned domains has proven very promising to faster algorithms and query processing techniques, taking into consideration the complexity of objects and the computationally intensive operations required.
The last part of the book comprises two chapters. Chapter 8 studies query optimization issues for R-tree based query processing.
Formulae are given for various query types that estimate the corresponding cost of the operation. These formulae are valuable for cost-based query optimization and selectivity estimation in modern database systems. Chapter 9 discusses some implementation issues regarding the R-tree access method.
Moreover, several research prototypes have implemented the R-tree to index spatial or multi-dimensional objects. The Epilogue at the end of the book summarizes our work and gives some directions for future research in the area. Intended Audience We believe that this book or parts of it will be valuable to course instructors, undergraduate and postgraduate students studying access methods for advanced applications. Moreover, it will be a valuable companion to researchers and professionals working with access methods, because it presents in detail a broad range of concepts and techniques related to indexing, query processing, query optimization, and implementation.
Finally, practitioners working in the development of access methods and database systems can use this book as a reference. More precisely, undergraduate students can focus on Chapters 1 through 4 and Chapter 9 to grasp the main characteristics of the R-tree and related access methods, and to understand how query processing is performed in a spatial database system.
Course instructors and researchers should study all the material to select parts of the book required for class presentation or further research in the area. We would like to thank the co-authors of our papers: P. Bozanis, S. Brakatsoulas, A. Corral, M. Egenhofer, C. Faloutsos, C. Gurret, Y. Karydis, N. Mamoulis, E. Nardelli, M. Nascimento, D.
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
Papadias, D. Pfoser, G.
Proietti, M. Ranganathan, P. Rigaux, J.
Silva, M. Scholl, T.
Sellis, E. Stefanakis, V.
Vasaitis, M. Vassilakopoulos, and M. We take the opportunity to thank Antonio Corral for his contribution in approximate queries cost models for distance joins, and Elias Frentzos for his contribution in query optimization issues. Finally, we would like to thank Catherine Drury and Michael Koy from Springer, for their great help and support toward the completion of this project. Their comments and suggestions were very helpful in improving the readability, organization, and overall view of the book.
We hope that this book will be a valuable reference to the expert reader and a motivating companion for the non-expert who whishes to study the theory and applications of the R-tree access method and its variations. VII List of Figures. XV List of Tables. When a leaf node is reached, the contained bounding boxes rectangles are tested against the search rectangle and their objects if there are any are put into the result set if they lie within the search rectangle.
For priority search such as nearest neighbor search , the query consists of a point or rectangle. The root node is inserted into the priority queue. Until the queue is empty or the desired number of results have been returned the search continues by processing the nearest entry in the queue.
Tree nodes are expanded and their children reinserted. Leaf entries are returned when encountered in the queue. At each step, all rectangles in the current directory node are examined, and a candidate is chosen using a heuristic such as choosing the rectangle which requires least enlargement. The search then descends into this page, until reaching a leaf node. If the leaf node is full, it must be split before the insertion is made.
Again, since an exhaustive search is too expensive, a heuristic is employed to split the node into two. Adding the newly created node to the previous level, this level can again overflow, and these overflows can propagate up to the root node; when this node also overflows, a new root node is created and the tree has increased in height.
Choosing the insertion subtree[ edit ] At each level, the algorithm needs to decide in which subtree to insert the new data object. When a data object is fully contained in a single rectangle, the choice is clear. When there are multiple options or rectangles in need of enlargement, the choice can have a significant impact on the performance of the tree. In the classic R-tree, objects are inserted into the subtree that needs the least enlargement.
At leaf level, it tries to minimize the overlap in case of ties, prefer least enlargement and then least area ; at the higher levels, it behaves similar to the R-tree, but on ties again preferring the subtree with smaller area. Splitting an overflowing node[ edit ] Since redistributing all objects of a node into two nodes has an exponential number of options, a heuristic needs to be employed to find the best split.Moreover, several research prototypes have implemented the R-tree to index spatial or multi-dimensional objects.
R-trees : Theory and Applications by Yannis Theodoridis
To resolve false alarms, the candidate objects must be examined. For every rectangle in a node, it has to be decided if it overlaps the search rectangle or not. From the reviews: Space support in databases poses new challenges in every part of a database management system and the capability of spatial support in the physical layer is considered very important. Ranganathan, P.
Start by pressing the button below! Greene's split. Dynamic Versions of R-trees. For priority search such as nearest neighbor search , the query consists of a point or rectangle.
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