Spatial Database Support for Virtual Engineering Dissertation im Fach Informatik an der Fakultät für Mathematik und Informatik der Ludwig-Maximilians-Universität München von Martin Pfeifle Tag der Einreichung: 21. April 2004 Tag der mündlichen Prüfung: 15. Oktober 2004 Berichterstatter: Prof. Dr. Hans-Peter Kriegel, Ludwig-Maximilians-Universität München Prof. Dr. Bernhard Seeger, Philipps-Universität Marburg i Acknowledgments Many people supported and encouraged me in the past three years while I was working on this dissertation. I would like to thank them here, even if I cannot mention them all by name. I would like to express my deepest thanks to my supervisor Prof. Dr. Hans-Peter Kriegel. Without his confidence in me and my ideas, and without the productive and inspiring working atmosphere he created, this work could never have come into ex- istence. I am also very grateful to Prof. Dr. Bernhard Seeger for his interest in my work and his immediate willingness to act as a second referee. This work would not have been possible without the cooperation of my colleagues in the database group. In particular, I would like to thank Dr. Marco Pötke and Prof. Dr. Thomas Seidl who introduced me into the area of spatial databases and guided my first research efforts. Their suggestions and judgments have always been a rich source of inspiration. Furthermore, I would like to thank Matthias Renz, Peter Kunath, Stefan Brecheisen, Eshref Januzaj, Peer Kröger, Matthias Schubert, Karin Kailing and Stefan Schönauer for constructive and productive team-work. I also appreciate the substantial help of the students whose study thesis or diploma thesis I supervised, including Stefan Brecheisen, Peter Kunath, Felix Leis, Olaf Schmitt, Maximillian Viermetz, Petra-Maria Strauß, Ralf Hofmann, Hans Maier, Wolfgang Mühlberger, Markus Veith, Marc Hiller and Michael Passer. I want to express special thanks to Franz Krojer, for taking care of our technical environment, and to Susanne Grienberger, for bearing much of the administrative burdens. Furthermore, I would like to thank Stefan Brecheisen and Peter Kunath for care- fully reading significant portions of this work and for providing valuable hints on improving the presentation. ii In particular, I would like to express my deepest thanks to my parents, who con- stantly supported me. They brought me up and taught me the readiness to work hard for my goals. I know that I would be nothing without them. Last but not least, my special thanks go to my wife Valerie for her encouragement and sacrificial love. Without her considerateness, this work could never have been accomplished. She took care of our two lovely little sons, Samuel and Benito, and answered their prob- ing questions about the whereabouts of their father with a patient smile: “He is writ- ing his dissertation in Munich”. Thank you! Martin Pfeifle Munich, April 2004. iii Abstract The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Shorter product cycles and a greater diversity of models are becoming decisive competitive factors in the hard-fought automobile and plane market. In order to support engineers to create complex prod- ucts when being pressed for time, systems are required which answer collision and similarity queries effectively and efficiently. In order to achieve industrial strength, the required specialized functionality has to be integrated into fully-fledged database systems, so that fundamental services of these systems can be fully reused, including transactions, concurrency control and recovery. This thesis aims at the development of theoretical sound and practical realizable algorithms which effectively and efficiently detect colliding and similar complex spatial objects. After a short introductory Part I, we look in Part II at different spatial index struc- tures and discuss their integrability into object-relational database systems. Based on this discussion, we present two generic approaches for accelerating collision queries. The first approach exploits available statistical information in order to accelerate the query process. The second approach is based on a cost-based decompositioning of complex spatial objects. In a broad experimental evaluation based on real-world test data sets, we demonstrate the usefulness of the presented techniques which allow interactive query response times even for large data sets of complex objects. In Part III of the thesis, we discuss several similarity models for spatial objects. We show by means of a new evaluation method that data-partitioning similarity models yield more meaningful results than space-partitioning similarity models. We intro- duce a very effective similarity model which is based on a new paradigm in similarity iv search, namely the use of vector set represented objects. In order to guarantee effi- cient query processing, suitable filters are introduced for accelerating similarity que- ries on complex spatial objects. Based on clustering and the introduced similarity models we present an industrial prototype which helps the user to navigate through massive data sets. v Abstract (in German) Ein schneller und reibungsloser Entwicklungsprozess neuer Produkte ist ein wich- tiger Faktor für den wirtschaftlichen Erfolg vieler Unternehmen insbesondere aus der Luft- und Raumfahrttechnik und der Automobilindustrie. Damit Ingenieure in immer kürzerer Zeit immer anspruchsvollere Produkte entwickeln können, werden effek- tive und effiziente Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten benötigt. Um den hohen Anforderungen eines produktiven Einsatzes zu genügen, müssen entsprechend spezialisierte Zugriffsmethoden in vollwertige Datenbanksysteme integriert werden, so dass zentrale Datenbankdienste wie Trans- aktionen, kontrollierte Nebenläufigkeit und Wiederanlauf sichergestellt sind. Ziel dieser Doktorarbeit ist es deshalb, effektive und effiziente Algorithmen für Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten zu ent- wickeln und diese in kommerzielle Objekt-Relationale Datenbanksysteme zu integrieren. Im ersten Teil der Arbeit werden verschiedene räumliche Indexstrukturen zur effi- zienten Bearbeitung von Kollisionsanfragen diskutiert und auf ihre Integrations- fähigkeit in Objekt-Relationale Datenbanksysteme hin untersucht. Daran an- knüpfend werden zwei generische Verfahren zur Beschleunigung von Kollisionsan- fragen vorgestellt. Das erste Verfahren benutzt statistische Informationen räumlicher Indexstrukturen, um eine gegebene Anfrage zu beschleunigen. Das zweite Verfahren beruht auf einer kostenbasierten Zerlegung komplexer räumlicher Datenbank- Objekte. Diese beiden Verfahren ergänzen sich gegenseitig und können unabhängig voneinander oder zusammen eingesetzt werden. In einer ausführlichen experiment- ellen Evaluation wird gezeigt, dass die beiden vorgestellten Verfahren interaktive Kollisionsanfragen auf umfangreichen Datenmengen und komplexen Objekten er- möglichen. vi Im zweiten Teil der Arbeit werden verschiedene Ähnlichkeitsmodelle für räum- liche Objekte vorgestellt. Es wird experimentell aufgezeigt, dass datenpartitionier- ende Modelle effektiver sind als raumpartitionierende Verfahren. Weiterhin werden geeignete Filtertechniken zur Beschleunigung des Anfrageprozesses entwickelt und experimentell untersucht. Basierend auf Clustering und den entwickelten Ähnlich- keitsmodellen wird ein industrietauglicher Prototyp vorgestellt, der Benutzern hilft, durch große Datenmengen zu navigieren. vii Survey of Chapters PART I. INTRODUCTION TO VIRTUAL ENGINEERING 1 Introduction 3 2 Spatial Engineering Databases 15 PART II. DATABASE SUPPORT FOR DIGITAL MOCKUP 3 Object Relational Indexing 29 4 A Cost Model for Spatial Intersection Queries 67 5 Statistic-Driven Acceleration of Relational Index Structures 95 6 Cost-based Decompositioning of Complex Spatial Objects 121 PART III. DATABASE SUPPORT FOR SIMILARITY SEARCH 7 Foundations of Similarity Search 171 8 Similarity Models for Voxelized CAD Data 199 9 Effectiveness of Similarity Models 223 10 Efficiency of Similarity Models 243 11 BOSS: Browsing Optics-Plots for Similarity Search 289 12 Conclusions 313 viii Survey of Chapters ix Table of Contents Acknowledgments . .i Abstract . .iii Abstract (in German) . v Survey of Chapters. vii Table of Contents . .ix PART I. INTRODUCTION TO VIRTUAL ENGINEERING 1Introduction 3 1.1 Industrial Applications . 4 1.1.1 Digital Mockup . 5 1.1.2 Similarity Search . 6 1.1.3 Further Applications . 7 1.2 Outline of the Thesis . 10 1.2.1 Virtual Engineering (Part I) . 11 1.2.2 Database Support for Digital Mockup (Part II) . 11 1.2.3 Database Support for Similarity Search (Part III) . 12 2 Spatial Engineering Databases 15 2.1 Spatial Engineering Data . 16 2.1.1 Triangle Meshes. 17 2.1.2 Voxelized Data . 18 2.1.3 Feature Vectors. 21 x Table of Contents 2.2 Spatial Engineering Query Processing . 22 2.2.1 Effective Query Processing . 22 2.2.2 Efficient Query Processing . 23 PART II. DATABASE SUPPORT FOR DIGITAL MOCKUP 3 Object Relational Indexing 29 3.1 Object Relational Databases. 30 3.1.1 Classification of Data Models . 30 3.1.2 Abstract Data Types. 32 3.2 Extensible Query Language . 32 3.2.1 Extensible Indexing . 32 3.2.2 Extensible Optimizing . 34 3.3 Implementation of Access Methods . 36 3.3.1 Integrating Approach . 37 3.3.2 Generic Approach . 41 3.3.3 Relational Approach . 43 3.4 Basics of Relational Access Methods . 44 3.4.1 Paradigms of Access Methods . 44 3.4.2 Relational Storage