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A Content-Addressable Network for Similarity Search in Metric Spaces Fabrizio Falchi

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A Content-Addressable Network for Similarity Search in Metric Spaces Fabrizio Falchi University of Pisa Masaryk University Faculty of Informatics Department of Department of Information Engineering Information Technologies Doctoral Course in Doctoral Course in Information Engineering Informatics Ph.D. Thesis A Content-Addressable Network for Similarity Search in Metric Spaces Fabrizio Falchi Supervisor Supervisor Supervisor Lanfranco Lopriore Fausto Rabitti Pavel Zezula 2007 Abstract Because of the ongoing digital data explosion, more advanced search paradigms than the traditional exact match are needed for content- based retrieval in huge and ever growing collections of data pro- duced in application areas such as multimedia, molecular biology, marketing, computer-aided design and purchasing assistance. As the variety of data types is fast going towards creating a database utilized by people, the computer systems must be able to model hu- man fundamental reasoning paradigms, which are naturally based on similarity. The ability to perceive similarities is crucial for recog- nition, classification, and learning, and it plays an important role in scientific discovery and creativity. Recently, the mathematical notion of metric space has become a useful abstraction of similarity and many similarity search indexes have been developed. In this thesis, we accept the metric space similarity paradigm and concentrate on the scalability issues. By exploiting computer networks and applying the Peer-to-Peer communication paradigms, we build a structured network of computers able to process similar- ity queries in parallel. Since no centralized entities are used, such architectures are fully scalable. Specifically, we propose a Peer- to-Peer system for similarity search in metric spaces called Met- ric Content-Addressable Network (MCAN) which is an extension of the well known Content-Addressable Network (CAN) used for hash lookup. A prototype implementation of MCAN was tested on real-life datasets of image features, protein symbols, and text — observed results are reported. We also compared the perfor- mance of MCAN with three other, recently proposed, distributed data structures for similarity search in metric spaces. Acknowledgments It is a great honor and privilege for me to have an international joint supervision of my Ph.D. dissertation. I want to thank the Univer- sity of Pisa and the Masaryk University, Brno for their agreement regarding this joint supervision. I am grateful to my supervisors Lanfranco Lopriore, Fausto Rabitti and Pavel Zezula for their guid- ance and encouragement. Since I was not just a Ph.D. student at the University of Pisa but also at the Masaryk University, I had the opportunity to work with several researchers in Brno, especially Michal Batko, David Novak and Vlastislav Dohnal. The discus- sions we had and the work we did together were fundamental for the completion of this thesis. This thesis would not have been possible without the substan- tial help from Claudio Gennaro and without the discussions I had with Giuseppe Amato and Pasquale Savino. I also thank all the other people at the Networked Multimedia Information Systems laboratory of the Institute of Information Science and Technolo- gies, especially Fausto Rabitti who is the head of the laboratory and his predecessor Costantino Thanos. I would also like to thank Cosimo Antonio Prete and Sandro Bartolini for teaching me the basics of research during my master thesis. Finally, I thank my mother and father for supporting me during my studies. To Moly Contents Abstract iii Acknowledgments v Table of Contents vii Introduction xiii I.1 Similarity Search ................... xiv I.2 Statement of the Problem .............. xv I.2.1 Purpose of the Study ............. xvi I.2.2 Significance of the Study ........... xvii I.2.3 Limitations of the Study ........... xviii I.2.4 Summary ................... xix 1 The Similarity Search Problem 1 1.1 Similarity Search ................... 1 1.2 Similarity Queries ................... 3 1.2.1 Range Query ................. 4 1.2.2 Nearest Neighbor Query ........... 4 1.2.3 Combinations of Queries ........... 5 1.2.4 Complex Similarity Queries ......... 5 1.3 Metric Spaces ..................... 7 1.3.1 Metric Distance Measures .......... 9 1.4 Access Methods for Metric Spaces .......... 12 1.4.1 Ball Partitioning ............... 12 1.4.2 Generalized Hyperplane Partitioning .... 12 1.4.3 Exploiting Pre-Computed Distances ..... 13 ix x CONTENTS 1.4.4 Hybrid Indexing Approaches ......... 13 2 Distributed Indexes 15 2.1 Scalable and Distr. Data Structures ......... 16 2.2 Peer-to-Peer Systems ................. 19 2.2.1 Characterization ............... 21 2.2.2 The lookup problem ............. 25 2.3 Unstructured Peer-to-Peer Systems ......... 27 2.4 Structured Peer-to-Peer Systems ........... 29 2.4.1 Introduction to DHTs ............ 29 2.4.2 Chord ..................... 36 2.4.3 Pastry ..................... 39 2.4.4 Tapestry .................... 41 2.4.5 Chimera .................... 42 2.4.6 Z-Ring ..................... 42 2.4.7 Content Addressable Network CAN ..... 43 2.4.8 Kademlia ................... 43 2.4.9 Symphony ................... 46 2.4.10 Viceroy .................... 47 2.4.11 DHTs Comparison .............. 49 2.4.12 DHTs Related Works ............. 49 2.4.13 P-Grid ..................... 50 2.4.14 Small-World and Scale-Free ......... 51 2.4.15 Other Works ................. 52 2.5 Metric Peer-to-Peer Structures ............ 52 2.5.1 GHT∗ and VPT∗ ............... 53 2.5.2 M-Chord .................... 57 2.6 Peer-to-Peer and Grid Computing .......... 59 3 Content-Addressable Network (CAN) 63 3.1 Node arrivals ..................... 64 3.1.1 Finding a Zone ................ 66 3.1.2 Joining the Routing .............. 67 3.2 Routing ........................ 67 3.3 Node departures .................... 69 3.4 Load Balancing .................... 70 0.0. CONTENTS xi 3.5 M-CAN: CAN-based Multicast ........... 70 3.6 CAN Related Works ................. 74 4 MCAN 77 4.1 Mapping ........................ 78 4.1.1 Pivot Selection ................ 79 4.2 Filtering ........................ 80 4.3 Regions ........................ 81 4.4 Construction ..................... 82 4.5 Insert ......................... 83 4.6 Split .......................... 84 4.7 Execution End Detection ............... 85 4.8 Range Query ..................... 86 4.9 Nearest Neighbor query ............... 88 5 MCAN Evaluation 95 5.1 Dimensionality of the Mapped Space ........ 95 5.2 Range query ...................... 98 5.3 Nearest Neighbor query ............... 103 5.3.1 Number of peers involved in query execution 106 5.3.2 Total number of distance computations ... 107 5.3.3 Parallel cost of kNN ............. 112 5.3.4 Candidate results ............... 112 6 MCAN Comparison with other structures 117 6.1 Experiments Settings ................. 118 6.2 Measurements ..................... 119 6.3 Scalability with Respect to the Size of the Query . 120 6.4 Scalability with Respect to the Size of Datasets .. 130 6.5 Number of Simultaneous Queries .......... 136 6.6 Comparison Summary ................ 140 7 Conclusions 143 7.1 Research Directions .................. 144 Bibliography 145 xii CONTENTS Index 174 Introduction Traditionally, search has been applied to structured (attribute- type) data yielding records that exactly match the query. However, to find images similar to a given one, time series with similar devel- opment, or groups of customers with common buying patterns in respective data collections, the traditional search technologies sim- ply fail. In these cases, the required comparison is gradual, rather than binary, because once a reference (query) pattern is given, each instance in a search file and the pattern are in a certain relation which is measured by a user-defined dissimilarity function. These types of search are more and more important for a va- riety of current complex digital data collections and are generally designated as similarity search. Thus similarity search has become a fundamental computational task in many applications. Unfortu- nately, its cost is still high and grows linearly with respect to the dataset size which prevents the realization of efficient applications for the ever-increasing amount of digital information being created today. In the last few years, Peer-to-Peer systems have been widely used particularly for file-sharing, distributed computing, Internet- based telephony and video multicast. The growth in the usage of these applications “is enormous and even more rapid than that of the World Wide Web” [183]. In a Peer-to-Peer system autonomous entities (peers) aim for the shared usage of distributed autonomous resources in a networked environment. Taking advantage of these distributed resources, scalability issues of centralized solutions for specific applications have been overcome. In this thesis we present a scalable distributed similarity search xiv INTRODUCTION structure for similarity search in metric spaces, which is called Met- ric Content-Addressable Network (MCAN). MCAN extends the well known structured Peer-to-Peer system CAN described in Chap- ter 3 [p.63], to generic metric space objects. MCAN uses the Peer- to-Peer paradigm to overcome scalability issues of centralized struc- tures for similarity search, taking advantage of distributed resources over the Peer-to-Peer network. I.1 Similarity Search Similarity search is based on gradual rather than exact relevance and is used in content-based retrieval for queries involving complex data types such as images, videos, time series, text and DNA se-
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