Universite Libre de Bruxelles

Faculte des Sciences Appliquees

Service Ingenierie Informatique

et de la Decision (CoDE)

Designing Conventional, Spatial,

and Temporal Data Warehouses:

Concepts and Methodological Framework

Elzbieta Malinowski

Work directed by: Professor Esteban Zimanyi

These presentee en vue de l’obtentiondu grade academique de

Docteur en Sciences Appliquees

Annee academique 2006-2007

To Yamil,my one and only love and husband,who throughout this time has been

a patient friend, an untiring supporter,and a wise adviser

Abstract

Decision support systems are interactive, computer-based information systems thatprovide data and analysis tools in order to better assist managers on different levelsof organization in the process of decision making. Data warehouses (DWs) have beendeveloped and deployed as an integral part of decision support systems.

A data warehouse is a database that allows to store high volume of historical datarequired for analytical purposes. This data is extracted from operational databases,transformed into a coherent whole, and loaded into a DW during the extraction-transformation-loading (ETL) process.

DW data can be dynamically manipulated using on-line analytical processing (OLAP)systems. DW and OLAP systems rely on a multidimensional model that includes mea-sures, dimensions, and hierarchies. Measures are usually numeric additive values thatare used for quantitative evaluation of different aspects about organization. Dimen-sions provide different analysis perspectives while hierarchies allow to analyze measureson different levels of detail.

Nevertheless, currently, designers as well as users find difficult to specify multidi-mensional elements required for analysis. One reason for that is the lack of conceptualmodels for DW and OLAP system design, which would allow to express data require-ments on an abstract level without considering implementation details. Another prob-lem is that many kinds of complex hierarchies arising in real-world situations are notaddressed by current DW and OLAP systems.

In order to help designers to build conceptual models for decision-support systemsand to help users in better understanding the data to be analyzed, in this thesis wepropose the MultiDimER model – a conceptual model used for representing multidi-mensional data for DW and OLAP applications. Our model is mainly based on theexisting ER constructs, for example, entity types, attributes, relationship types withtheir usual semantics, allowing to represent the common concepts of dimensions, hier-archies, and measures. It also includes a conceptual classification of different kinds ofhierarchies existing in real-world situations and proposes graphical notations for them.

On the other hand, currently users of DW and OLAP systems demand also theinclusion of spatial data, visualization of which allows to reveal patterns that are diffi-cult to discover otherwise. The advantage of using spatial data in the analysis processis widely recognized since it allows to reveal patterns that are difficult to discoverotherwise.

However, although DWs typically include a spatial or a location dimension, thisdimension is usually represented in an alphanumeric format. Furthermore, there isstill a lack of a systematic study that analyze the inclusion as well as the managementof hierarchies and measures that are represented using spatial data.

i

ii ABSTRACT

With the aim of satisfying the growing requirements of decision-making users, weextend the MultiDimER model by allowing to include spatial data in the differentelements composing the multidimensional model. The novelty of our contribution laysin the fact that a multidimensional model is seldom used for representing spatial data.To succeed with our proposal, we applied the research achievements in the field ofspatial databases to the specific features of a multidimensional model. The spatialextension of a multidimensional model raises several issues, to which we refer in thisthesis, such as the influence of different topological relationships between spatial objectsforming a hierarchy on the procedures required for measure aggregations, aggregationsof spatial measures, the inclusion of spatial measures without the presence of spatialdimensions, among others.

Moreover, one of the important characteristics of multidimensional models is thepresence of a time dimension for keeping track of changes in measures. However, thisdimension cannot be used to model changes in other dimensions. Therefore, usualmultidimensional models are not symmetric in the way of representing changes formeasures and dimensions. Further, there is still a lack of analysis indicating which con-cepts already developed for providing temporal support in conventional databases canbe applied and be useful for different elements composing a multidimensional model.

In order to handle in a similar manner temporal changes to all elements of a mul-tidimensional model, we introduce a temporal extension for the MultiDimER model.This extension is based on the research in the area of temporal databases, which havebeen successfully used for modeling time-varying information for several decades. Wepropose the inclusion of different temporal types, such as valid and transaction time,which are obtained from source systems, in addition to the DW loading time generatedin DWs. We use this temporal support for a conceptual representation of time-varyingdimensions, hierarchies, and measures. We also refer to specific constraints that shouldbe imposed on time-varying hierarchies and to the problem of handling multiple timegranularities between source systems and DWs.

Furthermore, the design of DWs is not an easy task. It requires to consider all phasesfrom the requirements specification to the final implementation including the ETLprocess. It should also take into account that the inclusion of different data items in aDW depends on both, users’ needs and data availability in source systems. However,currently, designers must rely on their experience due to the lack of a methodologicalframework that considers above-mentioned aspects.

In order to assist developers during the DW design process, we propose a method-ology for the design of conventional, spatial, and temporal DWs. We refer to differentphases, such as requirements specification, conceptual, logical, and physical model-ing. We include three different methods for requirements specification depending onwhether users, operational data sources, or both are the driving force in the process ofrequirement gathering. We show how each method leads to the creation of a conceptualmultidimensional model. We also present logical and physical design phases that referto DW structures and the ETL process.

To ensure the correctness of the proposed conceptual models, i.e., with conven-tional data, with the spatial data, and with time-varying data, we formally definethem providing their syntax and semantics. With the aim of assessing the usability ofour conceptual model including representation of different kinds of hierarchies as well

ABSTRACT iii

as spatial and temporal support, we present real-world examples. Pursuing the goalthat the proposed conceptual solutions can be implemented, we include their logicalrepresentations using relational and object-relational databases. Further, to show theapplicability of the proposed methodology and different approaches used for require-ments specification, we include several real-world examples related to universities.

Contents

Acknowledgements xi

1 Introduction 11.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 Conventional data warehouses . . . . . . . . . . . . . . . . . . . 21.1.2 Spatial databases and spatial data warehouses . . . . . . . . . . 41.1.3 Temporal databases and temporal data warehouses . . . . . . . 51.1.4 Conceptual modeling . . . . . . . . . . . . . . . . . . . . . . . . 61.1.5 Methodology for database and data warehouse design . . . . . . 6

1.2 Motivation and objectives of the thesis . . . . . . . . . . . . . . . . . . 71.2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2.2 Objectives of the thesis . . . . . . . . . . . . . . . . . . . . . . . 10

1.3 Scope and contributions of the thesis . . . . . . . . . . . . . . . . . . . 111.3.1 Conventional data warehouses . . . . . . . . . . . . . . . . . . . 111.3.2 Spatial data warehouses . . . . . . . . . . . . . . . . . . . . . . 131.3.3 Temporal data warehouses . . . . . . . . . . . . . . . . . . . . . 14

1.4 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 Conventional data warehouses 172.1 Data warehouse structures and data manipulation . . . . . . . . . . . . 18

2.1.1 Star, snowflake, and starflake schemas . . . . . . . . . . . . . . 182.1.2 Aspects of data manipulation in DWs . . . . . . . . . . . . . . . 18

2.2 The MultiDimER model . . . . . . . . . . . . . . . . . . . . . . . . . . 202.2.1 Model definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.2.2 Model particularities . . . . . . . . . . . . . . . . . . . . . . . . 222.2.3 Metamodel of the MultiDimER model . . . . . . . . . . . . . . 23

2.3 Mapping from conceptual to relational models . . . . . . . . . . . . . . 232.3.1 Rationale for choosing relational model . . . . . . . . . . . . . . 242.3.2 Mapping rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.4 Data warehouse hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . 262.4.1 Simple hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.2 Non-strict hierarchies . . . . . . . . . . . . . . . . . . . . . . . . 392.4.3 Multiple alternative hierarchies . . . . . . . . . . . . . . . . . . 442.4.4 Parallel hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . 462.4.5 Hierarchy categorization . . . . . . . . . . . . . . . . . . . . . . 48

2.5 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

v

vi CONTENTS

2.6 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3 Spatial data warehouses 573.1 Spatial databases: General concepts . . . . . . . . . . . . . . . . . . . . 583.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.2.1 Using the object-relational model . . . . . . . . . . . . . . . . . 613.2.2 Using spatial extensions of DBMSs . . . . . . . . . . . . . . . . 623.2.3 Preserving semantics . . . . . . . . . . . . . . . . . . . . . . . . 63

3.3 Spatially-extended MultiDimER model . . . . . . . . . . . . . . . . . . 633.4 Spatial levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.4.1 Conceptual representation . . . . . . . . . . . . . . . . . . . . . 653.4.2 Logical representation of spatial levels . . . . . . . . . . . . . . 663.4.3 Mapping of levels with spatial descriptive attributes . . . . . . . 68

3.5 Spatial hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683.5.1 Conceptual representation . . . . . . . . . . . . . . . . . . . . . 693.5.2 Topological relationships between spatial levels . . . . . . . . . 753.5.3 Logical representation of spatial hierarchies . . . . . . . . . . . . 78

3.6 Spatial fact relationships and measures . . . . . . . . . . . . . . . . . . 833.6.1 Conceptual representation . . . . . . . . . . . . . . . . . . . . . 843.6.2 Mapping of spatial fact relationships . . . . . . . . . . . . . . . 903.6.3 Mapping of spatial measures . . . . . . . . . . . . . . . . . . . . 91

3.7 Metamodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 933.8 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

4 Temporal data warehouses 994.1 Temporal databases: General concepts . . . . . . . . . . . . . . . . . . 1004.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1024.3 Temporal types in DWs . . . . . . . . . . . . . . . . . . . . . . . . . . 1034.4 Temporal extension for the MultiDimER model . . . . . . . . . . . . . 1044.5 Mapping of temporal types . . . . . . . . . . . . . . . . . . . . . . . . . 1074.6 Temporal levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

4.6.1 Conceptual representation . . . . . . . . . . . . . . . . . . . . . 1094.6.2 Mapping of temporal levels . . . . . . . . . . . . . . . . . . . . . 110

4.7 Temporal hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144.7.1 Conceptual representation . . . . . . . . . . . . . . . . . . . . . 1144.7.2 Mapping of child-parent relationships . . . . . . . . . . . . . . . 119

4.8 Temporal measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1254.8.1 Temporal support for non-aggregated measures . . . . . . . . . 1264.8.2 Temporal support for aggregated measures . . . . . . . . . . . . 1304.8.3 Mapping of fact relationships with temporal measures . . . . . . 133

4.9 Metamodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1344.10 Summary of mapping rules . . . . . . . . . . . . . . . . . . . . . . . . . 1374.11 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1384.12 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

CONTENTS vii

5 A methodology for conventional DW design 145

5.1 Conventional database design . . . . . . . . . . . . . . . . . . . . . . . 146

5.2 Current approaches for data warehouse design . . . . . . . . . . . . . . 147

5.2.1 Data warehouse lifecycle . . . . . . . . . . . . . . . . . . . . . . 147

5.2.2 Data mart and data warehouse design . . . . . . . . . . . . . . 150

5.2.3 Design phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

5.2.4 Methods for requirements specifications and DW design . . . . . 151

5.3 Phases for data warehouse design . . . . . . . . . . . . . . . . . . . . . 152

5.4 A motivating case study . . . . . . . . . . . . . . . . . . . . . . . . . . 153

5.5 Requirements specification and conceptual design . . . . . . . . . . . . 155

5.5.1 Demand-driven approach . . . . . . . . . . . . . . . . . . . . . . 156

5.5.2 Supply-driven approach . . . . . . . . . . . . . . . . . . . . . . 168

5.5.3 Demand/supply-driven approach . . . . . . . . . . . . . . . . . 176

5.6 Logical design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

5.6.1 Logical representation of DW schemas . . . . . . . . . . . . . . 179

5.6.2 Defining ETL processes . . . . . . . . . . . . . . . . . . . . . . . 183

5.7 Physical design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

5.7.1 DW schema implementation . . . . . . . . . . . . . . . . . . . . 184

5.7.2 ETL processes and staging area implementation . . . . . . . . . 189

5.8 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

5.8.1 Overall process . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

5.8.2 Requirements specifications . . . . . . . . . . . . . . . . . . . . 192

5.8.3 Conceptual, logical, and physical level design . . . . . . . . . . . 195

5.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

6 Spatial and temporal DW design 197

6.1 Spatial and temporal database design . . . . . . . . . . . . . . . . . . . 198

6.2 A methodology for spatial DW design . . . . . . . . . . . . . . . . . . . 198

6.2.1 Requirements specification and conceptual design . . . . . . . . 198

6.2.2 Logical and physical design . . . . . . . . . . . . . . . . . . . . 203

6.3 Methodological framework for temporal DW design . . . . . . . . . . . 203

6.3.1 Requirements specification and conceptual design . . . . . . . . 204

6.3.2 Logical and physical design . . . . . . . . . . . . . . . . . . . . 208

6.4 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

7 Conclusions and future work 211

7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

7.2.1 Conventional data warehouses . . . . . . . . . . . . . . . . . . . 214

7.2.2 Spatial data warehouses . . . . . . . . . . . . . . . . . . . . . . 215

7.2.3 Temporal and spatio-temporal data warehouses . . . . . . . . . 216

7.2.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

viii CONTENTS

A Formalization of the MultiDimER model 219

A.1 Notations and assumptions . . . . . . . . . . . . . . . . . . . . . . . . . 219

A.2 Meta variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

A.3 Abstract syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

A.4 Examples using the abstract syntax . . . . . . . . . . . . . . . . . . . . 222

A.4.1 Level definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 222

A.4.2 Child-parent relationship definitions . . . . . . . . . . . . . . . . 222

A.4.3 Dimension definitions . . . . . . . . . . . . . . . . . . . . . . . . 222

A.4.4 Fact relationship definitions . . . . . . . . . . . . . . . . . . . . 223

A.4.5 Constraint definitions . . . . . . . . . . . . . . . . . . . . . . . . 223

A.5 Auxiliary functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

A.6 Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

A.6.1 Semantics of predefined data types . . . . . . . . . . . . . . . . 224

A.6.2 Semantic domains . . . . . . . . . . . . . . . . . . . . . . . . . . 224

A.6.3 Semantic functions . . . . . . . . . . . . . . . . . . . . . . . . . 224

B Formalization of the spatial MultiDimER model 229

B.1 Meta variables for the spatial extension . . . . . . . . . . . . . . . . . . 229

B.2 Abstract syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

B.3 Examples using the abstract syntax . . . . . . . . . . . . . . . . . . . . 230

B.3.1 Level definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

B.3.2 Child-parent relationship definitions . . . . . . . . . . . . . . . . 232

B.3.3 Dimension definitions . . . . . . . . . . . . . . . . . . . . . . . . 232

B.3.4 Fact relationship definitions . . . . . . . . . . . . . . . . . . . . 232

B.3.5 Constraint definitions . . . . . . . . . . . . . . . . . . . . . . . . 232

B.4 Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

B.4.1 The space model . . . . . . . . . . . . . . . . . . . . . . . . . . 233

B.4.2 Semantic domains . . . . . . . . . . . . . . . . . . . . . . . . . . 235

B.4.3 Semantic functions . . . . . . . . . . . . . . . . . . . . . . . . . 235

C Formalization of the temporal MultiDimER model 241

C.1 Meta variables for the temporal extension . . . . . . . . . . . . . . . . 241

C.2 Abstract syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241

C.3 Examples using the abstract syntax . . . . . . . . . . . . . . . . . . . . 242

C.3.1 Level definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 242

C.3.2 Child-parent relationship definitions . . . . . . . . . . . . . . . . 244

C.3.3 Dimension definitions . . . . . . . . . . . . . . . . . . . . . . . . 245

C.3.4 Fact relationship definitions . . . . . . . . . . . . . . . . . . . . 245

C.3.5 Constraint definitions . . . . . . . . . . . . . . . . . . . . . . . . 245

C.4 Auxiliary functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

C.5 Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

C.5.1 The time model . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

C.5.2 Semantic domains . . . . . . . . . . . . . . . . . . . . . . . . . . 247

C.5.3 Semantic functions . . . . . . . . . . . . . . . . . . . . . . . . . 247

CONTENTS ix

D Graphical notations 255D.1 Notations for the ER model . . . . . . . . . . . . . . . . . . . . . . . . 255D.2 Notations for relational and object-relational databases . . . . . . . . . 257D.3 Notations for conventional data warehouses . . . . . . . . . . . . . . . . 258D.4 Notations for spatial data warehouses . . . . . . . . . . . . . . . . . . . 260D.5 Notations for temporal data warehouses . . . . . . . . . . . . . . . . . 261

Bibliography 263

Acknowledgements

First of all, I would like to express my gratitude to the Universe for the opportunityand challenging events that allow me to grow both professionally and personally.

I am also very thankful for my family’s support. This work would be impossible tofinish without the constant, daily encouragement from my husband. Thanks, Yamil, forbelieving in me more than I could believe in myself. I am also grateful to you for helpingme balance my life between different everyday activities. Thanks, Bartosz, for beingsuch a self-sufficient son during these four years. You are a strong guy who knows howto face challenges with mom constantly making you move to a different country, learnnew languages, and shape your new life. Thanks, Carito, my unbelievable daughter,for taking all the responsibilities in Costa Rica. Without your help I would not havebeen able to study in Belgium.

I would also like to express my gratitude to the Director of this thesis, EstebanZimanyi. His critical observations helped me improve the analytical skills required forthe research process as well as the writing skills required for disseminating the results.I also appreciate the freedom that Esteban gave me in choosing research directionsand methods. I believe that the ability that I acquired during my doctoral studies oflooking for new problems and finding their solutions is an important skill to achievefuture professional goals. I also had a privilege to have Esteban’s friendship for fouryears and to witness the birth of his first daughter, Elenita. I will take with me thememory of these moments of hard work and fun.

Special thanks goes to my research committee, Dr. Jef Wijsen and Dr. RoelWuyts. You cannot imagine how much your encouragement and appreciation of myacademic effort meant to me during the presentation sessions over these four years.I am also particularly grateful for the valuable comments from Professor Wijsen whomade me analyze my proposal more in-depth to improve its quality. I have also receivedimportant advice from professor Wuyts who contributed to the accuracy and readinessof this thesis. Thanks also both of you for unbelievable support in different occasionsduring the last period of writing my dissertation.

I would also like to show my appreciation to Dr. Marco Dorigo and Professor Jean-Luc Hainaut for the help in the final phase of writing my dissertation. The detailedrevision of professor Dorigo and his positive feedback motivated me to keep on workingto improve my thesis. I would like to acknowledge that, in spite of time constraintsand your busy schedule, you were able to read this thesis and give me your valuablecomments. Special thanks goes also to Professor Hainaut. I was fortunate to haveyou in my committee and to benefit from your expertise. Thank you for dedicatingyour valuable time to revise my work and to give me additional recommendations forincreasing its quality.

xi

xii ACKNOWLEDGEMENTS

I would also like to express my gratitude to many anonymous reviewers who eval-uated my articles, either accepting or rejecting them, for conferences, workshops, andjournals. The feedback I received from them helped to improve the quality of thisthesis and to expand my research scope. Thanks you for this enormous effort, which Iknow was time-consuming and did not have a direct appreciation gesture.

I am also thankful for the continuous administrative support from Natasha VanderHeyden. I know very few people who help the way you do without asking recognitionfor your job.

Last, but not least, I am dedicating my appreciation to two universities: the Uni-versite Libre de Bruxelles, specially to Service de Cooperation, and the Universidadde Costa Rica. Thanks to the former I received the scholarship that allowed me toaccomplish the dream of my professional life: a PhD degree. This financial and humansupport gave me the necessary comfort to focus on my research. I would like to expressmy gratitude to Mme. Dominique Mertens and Larondelle Arlette from the Service deCooperation for helping me with all administrative issues. Thanks also to Dominiquefor being such a special person and sharing her friendship with me. I strongly believethat the time and effort you invested in me and financial support you gave me willreplicate enormously when I share my knowledge with my students at the Universidadde Costa Rica.

Special thanks to the Universidad de Costa Rica that provided financial supportto my family. I believe this university is one of a kind, as it strongly supports thehuman side of scientists and encourages them to develop their professional growth bysimultaneously promoting family union. I would like to particularly express my appre-ciation to the former and current directors of the Oficina de Asuntos Internacionales(International Affairs Bureau) of the Universidad de Costa Rica, Dr. Manuel Murilloy Dra Ana Sittenfeld, respectively. Thank you for providing very efficient and rapidadministrative procedures and for offering all the necessary conditions to completemy doctoral studies. Special thanks goes also to Patricia Alfaro, Fatima Acosta, andYamileth Damazio, from the same bureau. You were very supportive and patient withall the questions I had during these four years of study.

Bibliography

[1] A. Abello and C. Martın. A bitemporal storage structure for a corporate datawarehouse. In Proc. of the 5th Int. Conference on Enterprise Information Sys-tems, ICEIS’03, pages 177–183, Angers, France, April 2003.

[2] A. Abello, J. Samos, and F. Saltor. Understanding analysis dimensions in amultidimensional object-oriented model. In Theodoratos et al. [216], page 4.

[3] A. Abello, J. Samos, and F. Saltor. On relationships offering new drill-acrossposibilities. In Theodoratos [215], pages 7–13.

[4] A. Abello, J. Samos, and F. Saltor. Y AM2 (yet another multidimensional model):An extension of UML. In M. Nascimento, M. Ozsu, and O. Zaıane, editors, Proc.of the Int. Database Engineering and Applications Symposium, IDEAS’02, pages172–181, Edmonton, Alberta, Canada, July 2002. IEEE Computer Society Press.

[5] S. Agarwal, R. Agrawal, P. Deshpande, A. Gupta, J. Naughton, R. Ramakrish-nan, and S. Sarawagi. On the computation of multidimensional aggregates. InAtkinson et al. [9], pages 506–521.

[6] J. Akoka, I. Comyn-Wattiau, and N. Prat. Dimension hierarchies design fromUML generalizations and aggregations. In Kunii et al. [120], pages 442–445.

[7] J. Allen. Maintaining knowledge about temporal intervals. Communications ofthe ACM, 26(11):832–843, 1983.

[8] H. Arisawa, K. Moriya, and T. Miura. Operations and the properties on non-first-normal-form relational databases. In M. Schkolnick and C. Thanos, editors, Proc.of the 9th Int. Conference on Very Large Data Bases, VLDB’83, pages 197–204,Florence, Italy, October - November 1983. Morgan Kaufmann Publishers.

[9] M. Atkinson, M. Orlowska, P. Valduriez, S. Zdonik, and M. Brodie, editors. Proc.of the 25th Int. Conference on Very Large Data Bases, VLDB’99, Edinburgh,Scotland, September 1999. Morgan Kaufmann Publishers.

[10] V. Basili, G. Caldiera, and D. Rombach. Goal/question/metric paradigm.In J. Marciniak, editor, Encyclopedia of Software Engineering, pages 528–532.Wiley-Interscience Publishers, 1994.

[11] C. Batini, S. Ceri, and S. Navathe. Conceptual Database Design: An Entity-Relationship Approach. The Benjamin/Cummings Publishing Company, 1991.

263

264 BIBLIOGRAPHY

[12] A. Bauer, W. Hummer, and W. Lehner. An alternative relational OLAP modelingapproach. In Kambayashi et al. [107], pages 189–198.

[13] Y. Bedard, T. Merrett, and J. Han. Fundaments of spatial data warehousing forgeographic knowledge discovery. In H. Miller and J. Han, editors, GeographicData Mining and Knowledge Discovery, pages 53–73. Taylor & Francis Publish-ers, 2001.

[14] Y. Bedard, M. Proulx, S. Larrivee, and E. Bernier. Modeling multiple represen-tations into spatial data warehouses: a UML-based approach. In Proc. of JointInt. Symposium International Society for Photogrammetry and Remote Sensing,ISPRS, Commission IV, Ottawa, Canada, July 2002.

[15] T. Behr and M. Schneider. Topological relationships of complex points andcomplex regions. In Kunii et al. [120], pages 56–69.

[16] C. Bettini, C. Dyrsen, W. Evans, R. Snodgrass, and X. Wang. A glossary of timegranularity concepts. In Etzion et al. [56], pages 406–413.

[17] C. Bettini, S. Jajodia, and X. Wang. Time Granularities in Databases, DataMining, and Temporal Reasoning. Springer-Verlag, 2000.

[18] R. Bliujute, S. Slatenis, G. Slivinskas, and C. Jensen. Systematic change mange-ment in dimensional data warehousing. Technical report, Time Center, TR-23,1998.

[19] M. Body, M. Miquel, Y. Bedard, and A. Tchounikine. A multidimensional andmultiversion structure for OLAP applications. In Theodoratos [215], pages 1–6.

[20] M. Body, M. Miquel, Y. Bedard, and A. Tchounikine. Handling evolution in mul-tidimensional structures. In U. Dayal, K. Ramamritham, and T. Vijayaraman,editors, Proc. of the 19th Int. Conference on Data Engineering, ICDE’03, pages581–592, Bangalore, India, March 2003. IEEE Computer Society Press.

[21] M. Boehnlein and A. U. vom Ende. Deriving initial data warehouses struc-tures from the conceptual data models of the underlying operational informationsystems. In Proc. of the 2nd ACM Int. Workshop on Data Warehousing andOLAP, DOLAP’99, pages 15–21, Kansas City, Missouri, USA, November 1999.ACM Press.

[22] M. Boehnlein and A. U. vom Ende. Business process oriented development ofdata warehouse structures. In R. Jung and R. Winter, editors, Proc. of DataWarehousing 2000, pages 3–21, Heidelberg, Germany, 2000. Physica-Verlag.

[23] A. Bonifati, F. Cattaneo, S. Ceri, A. Fuggetta, and S. Paraboschi. Designingdata marts for data warehouses. ACM Transactions on Software Engineeringand Methodology, 10(4):452–483, 2001.

[24] G. Booch, I. Jacobson, and J. Rumbaugh. The Unified Modeling Language: UserGuide. Addison-Wesley, 1998.

BIBLIOGRAPHY 265

[25] K. Borges, A. Laender, and C. Davis. Spatial data integrity constraints in object-oriented geographic data modeling. In Medeiros [145], pages 1–6.

[26] R. Bruckner, B. List, and J. Schiefer. Developing requirements for data warehousesystems with use cases. In Proc. of the 7th Americas Conference On InformationSystems, AMCIS’01, pages 329–335, Boston, Massachusetts, USA, August 2001.

[27] R. Bruckner and A. Tjoa. Capturing delays and valid times in data warehouses –towards timely consistent analyses. Journal of Intelligent Information Systems,19(2):169–190, 2002.

[28] D. Bulos and S. Foresman. Getting started with ADAPTTM OLAP databasedesign. http://www.symcorp.com/download/ADAPT\%20white\%20paper.pdf,1998. White Paper.

[29] L. Cabbibo and R. Torlone. The design and development of a logical system forOLAP. In Kambayashi et al. [107], pages 1–10.

[30] L. Cabibbo and R. Torlone. A logical approach to multidimensional databases.In Schek et al. [205], pages 183–197.

[31] E. Camossi, M. Bertolotto, E. Bertino, and G. Guerrini. A multigranular spa-tiotemporal data model. In Proc. of the 11th ACM Symposium on Advancesin Geographic Information Systems, ACM-GIS’03, pages 94–101, New Orleans,Louisiana, USA, November 2003. ACM Press.

[32] L. Carneiro and A. Brayner. X-META: A methodology for data warehouse designwith metadata management. In Lakshmanan [122], pages 13–22.

[33] J. Castro, M. Kolp, and J. Mylopoulos. Towards requirements-driven informationsystems engineering: The Tropos project. Information Systems, 27(6):365–389,2002.

[34] C. Chan and Y. Ioannidis. Bitmap index designed evaluation. In L. Haas andA. Tiwary, editors, Proc. of the ACM SIGMOD Int. Conference on Managementof Data, SIGMOD’98, pages 355–366, Seattle, Washington, USA, June 1998.ACM Press.

[35] P. Chen. The Entity-Relationship model - towards a unified view of data. ACMTransaction on Database Systems, 1(1):9–36, 1976.

[36] T. Chenoweth, D. Schuff, and R. Louis. A method for developing dimensionaldata marts. Communications of the ACM, 46(12):93–98, 2003.

[37] G. Chin. Agil Project Management. AMACOM Div American Mgmt Assn, 2003.

[38] J. Clifford, A. Croker, and A. Tuzhilin. On completeness of historical relationalquery languages. ACM Transactions on Database Systems, 19(1):64–116, 1994.

[39] C. Combi, M. Franceschet, and A. Peron. Representing and reasoning abouttemporal granularities. Journal of Logic and Computation, 4(1):52–77, 2004.

266 BIBLIOGRAPHY

[40] C. Date. An Introduction to Database Systems. Addison-Wesley, 2000.

[41] C. Date, H. Darwen, and N. Lorentzos, editors. Temporal Data and the RelationalModel. Morgan Kaufmann Publishers, 2003.

[42] C. Davis and A. Laender. Multiple representations in GIS: Materializationthrough map generalization, geometric, and spatial analysis operations. InMedeiros [145], pages 60–65.

[43] V. Detienne and J. Hainaut. CASE tool support for temporal database design.In Kunii et al. [120], pages 208–224.

[44] Proc. of the 2nd ACM Int. Workshop on Data Warehousing and OLAP,DOLAP’99, Kansas City, Missouri, USA, November 1999. ACM Press.

[45] C. Dyrsen. Valid-Time Indeterminacy. PhD thesis, University of Arizona, 1994.

[46] J. Eder, C. Koncilia, and T. Morzy. The COMET metamodel for temporal datawarehouses. In A. Banks Pidduck, J. Mylopoulos, C. Woo, and M. Ozsu, editors,Proc. of the 14th Int. Conference on Advanced Information Systems Engineering,CAiSE’02, pages 83–99, Toronto, Canada, May 2002. Springer-Verlag.

[47] M. Egenhofer. A model for detailed binary topological relationships. Geomatica,47(3&4):261–273, 1993.

[48] M. Egenhofer, A. Frank, and J. Jackson. A topological data model for spatialdatabases. In A. Buchman, O. Gunter, T. Smith, and Y. Wang, editors, Proc.of the 1st Symposium on Design and Implementation of Large Spatial Databases,SSD’89, pages 271–286, Santa Barbara, California, USA, July 1989. Springer-Verlag.

[49] M. Egenhofer and R. Franzosa. Point-set topological spatial relations. Int. Jour-nal of Geographic Information Systems, 5(2):161–174, 1991.

[50] M. Egenhofer and R. Golledge, editors. Spatial and Temporal Reasoning in Ge-ographic Information Systems. Oxford University Press, 1998.

[51] R. Elmasri and S. Navathe. Fundamentals of Database Systems. Addison-Wesley,fourth edition, 2003.

[52] R. Elmasri and G. Wuu. A temporal model and query language for ER databases.In Proc. of the 6th Int. Conference on Data Engineering, ICDE’90, pages 76–83,Los Angeles, California, February 1990. IEEE Computer Society Press.

[53] M. Erwig and M. Schneider. Formalization of advanced map operations. In Proc.of the 9th Int. Symposium on Spatial Data Handling, SDH’00, pages 8a.3–17,Beijing, China, August 2000.

BIBLIOGRAPHY 267

[54] M. Erwig, M. Schneider, and R. Guting. Temporal objects for spatio-temporaldata models and a comparison of their representations. In Y. Kambayashi, D. Lee,E. Lim, M. Mohania, and Y. Masunaga, editors, Proc. of the ER’98 Int. Work-shops on Advances in Database Technologies, pages 454–465, Singapore, Novem-ber 1998. Springer-Verlag.

[55] ESRI, Inc. ArcGIS data models. http://www.esri.com/software/

arcgisdatamodels/index.html, 2004.

[56] O. Etzion, S. Jajodia, and S. Sripada, editors. Temporal Databases: Researchand Practice. Springer-Verlag, 1998.

[57] F. Ferri, E. Pourabbas, M. Rafanelli, and F. Ricci. Extending geographic data-bases for a query language to support queries involving statistical data. InO. Gunter and H. Lenz, editors, Proc. of the 12th Int. Conference on Scien-tific and Statistical Database Management, SSDBM’00, pages 220–230, Berlin,Germany, July 2000. IEEE Computer Society Press.

[58] R. Fidalgo, V. Times, J. Silva, and F. Souza. GeoDWFrame: A frameworkfor guiding the design of geographical dimensional schemes. In Y. Kambayashi,M. Mohania, and W. Wos, editors, Proc. of the 6th Int. Conference on DataWarehousing and Knowledge Discovery, DaWaK’04, pages 26–37, Zaragoza,Spain, September 2004. Springer-Verlag.

[59] W. Filho, L. Figueiredo, M. Gattas, and P. Carvalho. A topological data structurefor hierarchical planar subdivisions. Technical report, University Waterloo, 1995.

[60] E. Franconi and U. Sattler. A data warehouse conceptual data model for multi-dimensional aggregation. In S. Gatziu, M. Jeusfeld, M. Staudt, and Y. Vassiliou,editors, Proc. of the Int. Workshop on Design and Management of Data Ware-houses, DMDW’99, page 13, Heidelberg, Germany, June 1999. CEUR WorkshopProceedings.

[61] G. Freitas, A. Laender, and M. Campos. MD2 - getting users involved in thedevelopment of data warehouse application. In Lakshmanan [122], pages 3–12.

[62] P. Giorgini, S. Rizzi, and M. Garzetti. Goal-oriented requirements analysis fordata warehouse design. In I. Song and J. Trujillo, editors, Proc. of the 8thACM Int. Workshop on Data Warehousing and OLAP, DOLAP’05, pages 47–56, Bremen, Germany, November 2005. ACM Press.

[63] M. Golfarelli, D. Maio, and S. Rizzi. Conceptual design of data warehouses fromE/R schemes. In Proc. of the 31st Hawaii Int. Conference on System Sciences,HICSS-31, page 334, Big Island of Hawaii, January 1998.

[64] M. Golfarelli and S. Rizzi. A methodological framework for data warehousedesign. In Proc. of the 1st ACM Int. Workshop on Data Warehousing and OLAP,DOLAP’98, pages 3–9, Bethesda, Maryland, USA, November 1998. ACM Press.

268 BIBLIOGRAPHY

[65] M. Golfarelli and S. Rizzi. Designing the data warehouse: Key steps and crucialissues. Journal of Computer Science and Information Mangement, 2(3):1–14,1999.

[66] J. Gray, S. Chaudhuri, A. Basworth, A. Layman, D. Reichart, M. Venkatrao,F. Pellow, and P. H. Data cube: A relational aggregation operator generaliz-ing group-by, cross-tab, and sub-totals. Data Mining and Knowledge Discovery,1(1):29–53, 1997.

[67] H. Gregersen and C. Jensen. Conceptual modeling of time-varying information.Technical report, Time Center, TR-35, 1998.

[68] H. Gregersen and C. Jensen. Temporal entity-relationship models - a survey.IEEE Transactions on Knowledge and Data Engineering, 11(3):464–497, 1999.

[69] H. Gregersen, L. Mark, and C. Jensen. Mapping temporal ER diagrams torelational schemas. Technical report, Time Center, TR-39, 1998.

[70] G. Groger, M. Reuter, and L. Plumer. Representation of a 3-D city model inspatial object-relational databases. In Proc. of the 20th Congress of InternationalSociety for Photogrammetry and Remote Sensing, page 13, Istanbul, Turkey, July2004.

[71] A. Gupta and I. Mumick. Maintenance of materialized views: Problems, tech-niques, and applications. IEEE Data Engineering Bulletin, 18(2):3–18, 1995.

[72] R. Guting. An introduction to spatial database systems. The VLDB Journal,3(4):357–399, 1994.

[73] R. Guting, T. de Ridder, and M. Schneider. Implementation of the ROSE algebra:Efficient algorithms for realm-based spatial data types. In Proc. of the 4th Int.Symposium on Advances in Spatial Databases, SSD’95, pages 216–239, Portland,Maine, USA, August 1995. Springer-Verlag.

[74] R. Guting and M. Schneider. Realms: A foundation for spatial data types indatabase systems. In D. Abel and B. Ooi, editors, Proc. of the 3rd Int. Symposiumon Advances in Spatial Databases, SSD’93, pages 14–35, Singapore, June 1993.Springer-Verlag.

[75] R. Guting and M. Schneider. Real-based spatial data types: the ROSE algebra.The VLDB Journal, 4(2):243–286, 1995.

[76] R. Guting and M. Schneider. Moving Objects Databases. Elsevier Science Pub-lisher, 2005.

[77] A. Guttman. R-trees: a dynamic index structure for spatial searching. In B. Yor-mark, editor, Proc. of the ACM SIGMOD Int. Conference on Management ofData, SIGMOD’84, pages 46–57, Boston, Massachusetts, USA, June 1984. ACMPress.

BIBLIOGRAPHY 269

[78] K. Hahn, C. Sapia, and M. Blaschka. Automatically generating OLAP schematafrom conceptual graphical models. In Proc. of the 3rd ACM Int. Workshop onData Warehousing and OLAP, DOLAP’00, pages 9–16, Washington, DC, USA,November 2000. ACM Press.

[79] J. Hainaut, V. Englebert, J. Henrard, J. Hick, and D. Roland. Database reverseengineering: from requirements to CASE tools. Journal of Automated SoftwareEngineering, 3(1):9–45, 1996.

[80] J. Hainaut, J. Henrard, J. Hick, D. Roland, and V. Englebert. CASE tools fordatabase engineering. In L. R. amd J. Doorn and V. Ferraggine, editors, En-cyclopedia of Database Technologies and Applications, pages 59–65. Idea GroupPublishing, 2005.

[81] J. Hainaut, J. Hick, J. Henrard, D. Roland, and V. Englebert. Knowledge trans-fer in database reverse engineering: A supporting case study. In D. Baxter,A. Quilici, and C. Verhoef, editors, Proc. of the 4th Working Conference on Re-verse Engineering, WCRE’97, pages 194–205, Amsterdam, Netherlands, October1997. IEEE Computer Society Press.

[82] M. Halper, J. Galler, and Y. Perl. An OODB part-whole model: Semantics,notation, and implementation. Data & Knowledge Engineering, DKE, 27:59–95,1998.

[83] M. Hammer and J. Champy. Reengineering the Corporation - A Manifiesto forBusiness Revolution. Harper Collins, 1993.

[84] J. Han, K. Koperski, and N. Stefanovic. GeoMiner: a system prototype for spatialdata mining. In J. Peckham, editor, Proc. of the ACM SIGMOD Int. Conferenceon Management of Data, SIGMOD’97, pages 553–556, Tucson, Arizona, USA,May 1997. ACM Press.

[85] J. Henrard, J. Hainaut, J. Hick, , D. Roland, and V. Englebert. Data struc-ture extraction in database reverse engineering. In P. Chen, D. Embley, andJ. Kouloumdjian, editors, Proc. of the ER’99 Workshops on Evolution andChange in Data Management, Reverse Engineering in Information Systems, andWorld Wide Web and Conceptual Modeling, pages 149–160, Paris, France, No-vember 1999. Springer-Verlag.

[86] J. Henrard, J. Hick, P. Thiran, and J. Hainaut. Strategies for data reengineering.In A. van Deursen and E. Burd, editors, Proc. of the 9th Working Conference onReverse Engineering, WCRE’02, pages 211–220, Richmond, VA, USA, October- November 2002. IEEE Computer Society Press.

[87] W. Hummer, W. Lehner, A. Bauer, and L. Schlesinger. A decathlon in mul-tidimensional modeling: Open issues and some solutions. In Y. Kambayashi,W. Winiwater, and M. Arikawa, editors, Proc. of the 4th Int. Conference onData Warehousing and Knowledge Discovery, DaWaK’02, pages 275–285, Aix-en-Provence, France, September 2002. Springer-Verlag.

270 BIBLIOGRAPHY

[88] C. Hurtado and A. Mendelzon. Reasoning about summarizabiliy in heterogeneousmultidimensional schemas. In J. Van den Bussche and V. Vianu, editors, Proc. ofthe 8th Int. Conference on Database Theory, ICDT’01, pages 375–389, London,UK, January 2001. Springer-Verlag.

[89] C. Hurtado, A. Mendelzon, and A. Vaisman. Updating OLAP dimensions.In Proc. of the 2nd ACM Int. Workshop on Data Warehousing and OLAP,DOLAP’99, pages 60–66, Kansas City, Missouri, USA, November 1999. ACMPress.

[90] B. Husemann, J. Lechtenborger, and G. Vossen. Conceptual data warehousedesign. In Jeusfeld et al. [105], page 6.

[91] IBM. Red brick warehouse. www-3.ibm.com/software/data/Informix/

redbrick, 2003.

[92] M. Ibrahim, J. Kung, and N. Revell, editors. Proc. of the 11th Int. Conference onDatabase and Expert Systems Applications, DEXA’00, London, UK, September2000. Springer-Verlag.

[93] C. Imhoff, N. Galemmo, and J. Geiger. Mastering Data Warehouse Design. JohnWiley & Sons Publishers, 2003.

[94] M. Indulska and M. Orlowska. On aggregation issues in spatial data management.In X. Shou, editor, Proc. of the 13th Australasian Database Conference, ADC’02,pages 75–84, Melbourne, Australia, January-February 2002. CRIPT AustralianComputer Society.

[95] W. Inmon. Building the Data Warehouse. John Wiley & Sons Publishers, 2002.

[96] ISO. SQL multimedia and application packages - part 3: Spatial. Technicalreport, ISO/IEC FCD 13249-3:2003, 2002.

[97] H. Jagadish, L. Lakshmanan, and D. Srivastava. What can hierarchies do fordata warehouses. In Atkinson et al. [9], pages 530–541.

[98] M. Jarke, M. Lenzerini, Y.Vassiluiou, and P. Vassiliadis, editors. Fundamentalsof Data Warehouse. Springer-Verlag, 2003.

[99] C. Jensen. Temporal database management. http://www.cs.aau.dk/∼csj/Thesis/, 2003. Dr. techn. thesis, University of Aalborg.

[100] C. Jensen, J. Clifford, R. Elmasri, S. Gadia, P. Hayes, and S. Jajodia. A consensusglossary of temporal database concepts. SIGMOD Record, 23(1):52–64, 1994.

[101] C. Jensen, A. Klygis, T. Pedersen, and I. Timko. Multidimensional data modelingfor location-based services. VLDB Journal, 13(1):1–21, 2004.

[102] C. Jensen, M. Schneider, B. Seeger, and V. Tsotras, editors. Proc. of the 7th Int.Symposium on Advances in Spatial and Temporal Databases, SSTD’01, RedondoBeach, California, USA, July 2001. Springer-Verlag.

BIBLIOGRAPHY 271

[103] C. Jensen and R. Snodgrass. Temporally enhanced database design. In M. Papa-zoglou, S. Spaccapietra, and Z. Tari, editors, Advances in Object-Oriented DataModeling, pages 163–193. MIT Press, 2000.

[104] C. Jensen, R. Snodgrass, and M. Soo. Extending normal forms to temporalrelations. Technical report, Time Center, TR 92-17, 1992.

[105] M. Jeusfeld, H. Shu, M. Staudt, and G. Vossen, editors. Proc. of the 2nd Int.Workshop on Design and Management of Data Warehouses, DMDW’00, Stock-holm, Sweden, June 2000. CEUR Workshop Proceedings.

[106] H. Jullens. Spatial customer intelligence: The map shows the future. GeoInfor-matics, 3(12), 2000.

[107] Y. Kambayashi, M. Mohania, and A. Min Tjoa, editors. Proc. of the 2nd Int.Conference on Data Warehousing and Knowledge Discovery, DaWaK’00, Lon-don, UK, September 2000. Springer-Verlag.

[108] M. Kang, F. Pinet, M. Schneider, J. Chanet, and F. Vigier. How to designgeographic databases? Specific UML profile and spatial OCL applied to wirelessad hoc networks. In Proc. of the 7th AGILE Conference on GIScience, pages197–207, Heraklion, Greece, May 2004.

[109] N. Karayannidis, A. Tsois, T. Sellis, R. Pieringer, V. Markl, F. Ramsak, R. Fenk,K. Elhardt, and R. Bayer. Processing star queries on hierarchically-clustered facttables. In Proc. of the 28th Int. Conference on Very Large Data Bases, VLDB’02,pages 730–741, Hong Kong, China, August 2002. Morgan Kaufmann Publishers.

[110] H. Kerzner. Project Management: A Systems Approach to Planning, Scheduling,and Controlling. John Wiley & Sons Publishers, 2005.

[111] KHEOPS, Technologies. JMap spatial OLAP. http://www.kheops-tech.com/

en/jmap/solap.jsp, 2004.

[112] R. Kimball, L. Reeves, M. Ross, and W. Thornthwaite. The Data WarehouseLifecycle Toolkit. John Wiley & Sons Publishers, 1998.

[113] R. Kimball, M. Ross, and R. Merz. The Data Warehouse Toolkit: The CompleteGuide to Dimensional Modeling. John Wiley & Sons Publishers, 2002.

[114] P. King. The database design process. In S. Spaccapietra, editor, Entity-Relationship Approach. Elsevier Science Publisher, 1987.

[115] T. Kolbe and R. Gruger. Towards unified 3D city models. In J. Schiewe,M. Hahn, M. Madden, and M. Sester, editors, Proc. of ISPRS Commision IVJoin Workshop on Challenges in Geospatial Analysis, Integration and Visualiza-tion II, Stuttgart, Germany, September 2003.

272 BIBLIOGRAPHY

[116] C. Koncilia. A bi-temporal data warehouse model. In J. Eder and M. Missikoff,editors, Proc. of the 15th Int. Conference on Advanced Information Systems En-gineering, CAiSE’03, pages 77–80, Klagenfurt, Austria, June 2003. Springer-Verlag.

[117] Z. Kouba, K. Matousek, and P. Miksovsky. On data warehouse and GIS integra-tion. In Ibrahim et al. [92], pages 604–613.

[118] M. Koubarakis, T. Sellis, A. Frank, R. Guting, C. Jensen, A. Lorentzos,Y. Manolopoulos, E. Nardelli, B. Pernici, H.-J. Schek, M. Scholl, B. Theodoulidis,and N. Tryfona. Spatio-Temporal Databases: The Chorochronos Approach.Springer-Verlag, 2003.

[119] H. Kriegel, P. Kunath, M. Pfeifle, and M. Renz. Object-relational managementof complex geographical objects. In Pfoser et al. [182], pages 109–117.

[120] H. Kunii, S. Jajodia, and S. Solvberg, editors. Proc. of the 20th Int. Conferenceon Conceptual Modeling, ER’01, Yokohama, Japan, November 2001. Springer-Verlag.

[121] T. Lachev. Applied Microsoft Analysis Services 2005. Prologica Press, 2005.

[122] L. Lakshmanan, editor. Proc. of the 4th Int. Workshop on Design and Man-agement of Data Warehouses, DMDW’02, Toronto, Ontario, Canada, May 2002.CEUR Workshop Proceedings.

[123] S. Larrivee, Y. Bedard, and J. Pouliot. How to enrich the semantics of geospatialdatabases by properly expressing 3D objects in a conceptual model. In R. Meers-man, Z. Tari, P. Herrero, and G. Mendez, editors, Proc. of On the Move toMeaningful Internet Systems Workshops, pages 999–1008, Agia Napa, Cyprus,October - November 2005. Springer-Verlag.

[124] W. Lehner, J. Albrecht, and H. Wedekind. Normal forms for multidimensionaldatabases. In Rafanelli and Jarke [192], pages 63–72.

[125] H. Lenz and A. Shoshani. Summarizability in OLAP and statistical databases. InY. Ioannidis and D. Hansen, editors, Proc. of the 9th Int. Conference on Scien-tific and Statistical Database Management, SSDBM’97, pages 132–143, Olympia,Washington, USA, August 1997. IEEE Computer Society Press.

[126] H. Lenz, P. Vassiliadis, M. Jeusfeld, and M. Staudt, editors. Proc. of the 5th Int.Workshop on Design and Management of Data Warehouses, DMDW’03, Berlin,Germany, September 2003. CEUR Workshop Proceedings.

[127] B. List, R. Bruckner, K. Machaczek, and J. Shiefer. Comparison of data ware-house development methodologies. Case study of the process warehouse. InA. Hameurlain, R. Cicchetti, and R. Traunmuller, editors, Proc. of the 13thInt. Conference on Database and Expert Systems Applications, DEXA’02, pages6–1–6–11, Aix-en-Provence, France, September 2002. Springer-Verlag.

BIBLIOGRAPHY 273

[128] B. List, J. Shiefer, and A. Tjoa. Process-oriented requirement analysis supportingthe data warehouse design process - a use case driven approach. In Ibrahim et al.[92], pages 593–603.

[129] S. Lujan-Mora and J. Trujillo. A comprehensive method for data warehousedesign. In Lenz et al. [126].

[130] S. Lujan-Mora, J. Trujillo, and I. Song. Multidimensional modeling with UMLpackage diagrams. In S. Spaccapietra, S. March, and Y. Kambayashi, editors,Proc. of the 21st Int. Conference on Conceptual Modeling, ER’02, pages 199–213,Tampere, Finland, October 2002. Springer-Verlag.

[131] E. Malinowski and E. Zimanyi. OLAP hierarchies: A conceptual perspective. InA. Persson and J. Stirna, editors, Proc. of the 16th Int. Conference on AdvancedInformation Systems Engineering, CAiSE’04, pages 477–491, Riga, Latvia, June2004. Springer-Verlag.

[132] E. Malinowski and E. Zimanyi. Representing spatiality in a conceptual multidi-mensional model. In Pfoser et al. [182], pages 12–21.

[133] E. Malinowski and E. Zimanyi. Spatial hierarchies and topological relationshipsin the Spatial MultiDimER model. In M. Jackson, D. Nelson, and S. Strik,editors, Proc. of the 22nd British Nat. Conference on Databases, BNCOD’05,pages 17–28, Sunderland, UK, July 2005. Springer-Verlag.

[134] E. Malinowski and E. Zimanyi. A conceptual solution for representing time indata warehouse dimensions. In M. Stumptner, S. Hartmann, and Y. Kiyoki,editors, Proc. of the 3rd Asia-Pacific Conference on Conceptual Modelling,APCCM’06, pages 45–54, Hobart, Australia, January 2006. CRIPT AustralianComputer Society.

[135] E. Malinowski and E. Zimanyi. Hierarchies in a multidimensional model: fromconceptual modeling to logical representation. Data & Knowledge Engineering,DKE, 2006. To appear.

[136] E. Malinowski and E. Zimanyi. Inclusion of time-varying measures in tempo-ral data warehouses. In Y. Manolopoulos, J. Filipe, P. Constantopoulos, andJ. Cordeiro, editors, Proc. of the 8th Int. Conference on Enterprise InformationSystems, ICEIS’06, pages 181–186, Paphos, Cyprus, May 2006.

[137] E. Malinowski and E. Zimanyi. Logical representation of a conceptual model forspatial data warehouses. GeoInformatica, 2006. Submitted for publication.

[138] E. Malinowski and E. Zimanyi. Object-relational representation of a concep-tual model for temporal data warehouses. In E. Dubois and K. Pohl, editors,Proc. of the 18th Int. Conference on Advanced Information Systems Engineering,CAiSE’06, pages 96–110, Luxembourg, Luxembourg, June 2006. Springer-Verlag.

274 BIBLIOGRAPHY

[139] E. Malinowski and E. Zimanyi. Requirements specification and conceptual mod-eling for spatial data warehouses. In Proc. of the 2nd Int. Workshop on Semantic-based Geographical Information Systems, 2006. To appear.

[140] P. Marchand. The Spatio-Temporal Topological Operator Dimension, a Hyper-structure for Multidimensional Spatio-temporal Explorations and Analysis. PhDthesis, Universite Laval Sainte-Foy, 2003.

[141] M. Mark and M. Egenhofer. Modeling spatial relations between lines and regions:Combining formal mathematical models and human subjects testing. Cartogra-phy and Geographical Information Systems, 21(3):195–212, 1994.

[142] C. Martın and A. Abello. A temporal study of data sources to load a cor-porate data warehouse. In Y. Kambayashi, M. Mohania, and W. Wos, editors,Proc. of the 5th Int. Conference on Data Warehousing and Knowledge Discovery,DaWaK’03, pages 109–118, Prague, Chech Republic, September 2003. Springer-Verlag.

[143] T. Martyn. Reconsidering multi-dimensional schemas. SIGMOD Record,33(1):83–88, 2004.

[144] J. Mazon, J. Trujillo, M. Serrano, and M. Piattini. Designing data warehouses:From business requirement analysis to multidimensional modeling. In Proc. ofthe 1th Int. Workshop on Requirements Engineering for Business Need and ITAlignment, REBN’05, pages 44–53, Paris, France, August 2005.

[145] C. Medeiros, editor. Proc. of the 7th ACM Symposium on Advances in GeographicInformation Systems, ACM-GIS’99, Kansas City, USA, November 1999. ACMPress.

[146] J. Melton. Advanced SQL: 1999. Understanding Object-Relational and OtherAdvanced Features. Morgan Kaufman Publishers, 2003.

[147] J. Melton. SQL:2003 has been published. SIGMOD Record, 33(1):119–125, 2003.

[148] A. Mendelzon and A. Vaisman. Temporal queries in OLAP. In A. E. Abbadi,M. Brodie, S. Chakravarthy, U. Dayal, N. Kamel, G. Schlageter, and K.-Y.Whang, editors, Proc. of the 26th Int. Conference on Very Large Data Bases,VLDB’00, pages 243–253, Cairo, Egypt, August 2000. Morgan Kaufmann Pub-lishers.

[149] A. Mendelzon and A. Vaisman. Time in multidimensional databases. In Rafanelli[191], pages 166–199.

[150] I. Merlo, E. Bertino, E. Ferrari, and G. Guerrini. A temporal object-orienteddata model with multiple granularities. In 6th Int. Workshop on Temporal Rep-resentation and Reasoning, TIME’99, pages 73–81, Orlando, Florida, USA, May1999. IEEE Computer Society Press.

BIBLIOGRAPHY 275

[151] Microsoft Corporation. SQL Server 2000. Books Online. http://www.

microsoft.com/sql/techinfo/productdoc/2000/books.asp, 2003.

[152] P. Miksovsky and Z. Kouba. GOLAP - geographical on-line analytical processing.In H. Mayr, F. Lazansky, G. Quirchmayr, and P. Vogel, editors, Proc. of the 12thInt. Conference on Database and Expert Systems Applications, DEXA’01, pages201–205, Munich, Germany, September 2001. Springer-Verlag.

[153] M. Miquel, A. Brisebois, Y. Bedard, and G. Edwards. Implementation andevaluation of hypercube-based method for spatio-temporal exploration and anly-sis. Journal of the Int. Society for Photogrammetry and Remote Sensing ISPRS,59(1-2):6–20, 2004.

[154] D. Moody and M. Kortink. From enterprise models to dimensional models: amethodology for data warehouse and data mart design. In Jeusfeld et al. [105],page 5.

[155] T. Morzy and R. Wrembel. On querying versions of multiversion data warehouse.In I.Y.Song and K. Davis, editors, Proc. of the 7th ACM Int. Workshop on DataWarehousing and OLAP, DOLAP’04, pages 92–101, Washington, D.C., USA,November 2004. ACM Press.

[156] H. Nemati, D. Steiger, L. Iyer, and R. Herschel. Knowledge warehouse: anarchitectural integration of knowledge management, decision support, artificialintelligence and data warehousing. Decision Support Systems, 33:143–161, 2002.

[157] R. Newton. Project Manager: Mastering the Art of Delivery in Project Mnan-agement. Financial Times Prentice Hall, 2005.

[158] T. Niemi, J. Nummenmaa, and P. Thanisch. Logical multidimensional databasedesign for ragged and unbalanced aggregation hierarchies. In Theodoratos et al.[216], page 7.

[159] Object Management Group. Common Warehouse Metamodel. http://www.omg.org/docs/formal/03-03-02.pdf, 2002.

[160] E. O’Neil and G. Graefe. Multi-table joins through bitmapped join indices.SIGMOD Record, 24(3):8–11, 1995.

[161] Open GIS Consortium. OpenGIS simple features specification for SQL. TechnicalReport Revision 1.1, 1999.

[162] Open GIS Consortium. OpenGIS spatial reference systems. Technical ReportRevision 1.1, 1999.

[163] Oracle Coorporation. Oracle 9i OLAP User’s Guide, Release 2 (9.2). http:

//otn.oracle.com/products/bi/pdf/Userguide.pdf, 2003.

[164] Oracle Coorporation. Oracle database data warehousing guide 10g re-lease 1. http://download-west.oracle.com/docs/cd/B14117\ 01/server.

101/b10736.pdf, 2003.

276 BIBLIOGRAPHY

[165] Oracle Coorporation. Oracle database application developer’s guide - object-relational features. 10g release 1. http://download-west.oracle.com/docs/

cd/B14117 01/appdev.101/b10799/toc.htm, 2005.

[166] T. Ott and F. Swiaczny, editors. Time-Integrative Geographic Information Sys-tems: Management and Analysis of Spatio-temporal Data. Springer-Verlag, 2001.

[167] M. Ozsu and P. Valduriez. Principles of Distributed Database Systems. PrenticeHall, 1991.

[168] F. Paim, A. Carvalho, and J. Castro. Towards a methodology for requirementsanalysis of data warehouse systems. In Proc. of the 15th Brazilian Symposiumon Software Engineering, pages 1–16, Gramado, Brasil, 2002.

[169] F. Paim and J. Castro. DWARF: An approach for requirements definition andmanagement of data warehouse systems. In Proc. of the 11th IEEE Int. Require-ments Engineering Conference, RE’03, pages 75–84, Monterey Bay, California,USA, September 2003. IEEE Computer Society Press.

[170] D. Papadias and M. Egenhofer. Algorithms for hierarchical spatial reasoning.Geomatica, 1(3):251–273, 1997.

[171] D. Papadias, P. Kalnis, J. Zhang, and Y. Tao. Efficient OLAP operations inspatial data warehouses. In Jensen et al. [102], pages 443–459.

[172] D. Papadias, Y. Tao, P. Kalnis, and J. Zhang. Indexing spatio-temporal datawarehouses. IEEE Data Engineering Bulletin, 25(1):10–17, 2002.

[173] S. Paraboschi, G. Sindoni, E. Baralis, and E. Teniente. Materialized views inmultidimensional databases. In Rafanelli [191], pages 222–251.

[174] C. Parent, S. Spaccapietra, and E. Zimanyi. Modeling time from a conceptual per-spective. In G. Gardarin, J. French, N. Pissinou, and K. Makki, editors, Proc. ofthe 7th Int. Conference on Information and Knowledge Management, CIKM’98,pages 432–440, Bethesda, Maryland, USA, November 1998. ACM Press.

[175] C. Parent, S. Spaccapietra, and E. Zimanyi. Spatio-temporal conceptual models:Data structures + Space + Time. In Medeiros [145], pages 26–33.

[176] C. Parent, S. Spaccapietra, and E. Zimanyi. Conceptual Modeling for Traditionaland Spatio-Temporal Applications: The MADS Approach. Springer-Verlag, 2006.

[177] T. Pedersen, C. Jensen, and C. Dyreson. A foundation for capturing and queryingcomplex multidimensional data. Information Systems, 26(5):383–423, 2001.

[178] T. Pedersen and N. Tryfona. Pre-aggregation in spatial data warehouses. InJensen et al. [102], pages 460–478.

[179] V. Peralta and R. Ruggia. Using design guidelines to improve data warehouselogical design. In Lenz et al. [126].

BIBLIOGRAPHY 277

[180] W. Pereira and K. Becker. A methodology targeted at the insertion of datawarehouse technology in corporations. In K. Becker, A. Augusto de Souza, andD. Yluska de Souza Fernandes, editors, Proc. of the 15th Simposio Brasileiro deBanco de Dados, SBBD’00, pages 316–330, Joao Pessoa, Brasil, October 2000.CEFET-PB Publishers.

[181] G. Pestana, M. Mira da Silva, and Y. Bedard. Spatial OLAP modeling: Anoverview base on spatial objects changing over time. In Proc. of the 3rd Int.Conference on Computational Cybernetics, Mauritius, April 2005.

[182] D. Pfoser, I. Cruz, and M. Ronthaler, editors. Proc. of the 12th ACM Sympo-sium on Advances in Geographic Information Systems, ACM-GIS’04, Washing-ton, D.C., USA, November 2004. ACM Press.

[183] C. Phipps and K. Davis. Automating data warehouse conceptual schema designand evaluation. In Lakshmanan [122], pages 23–32.

[184] E. Pourabbas. Cooperation with geographic databases. In Rafanelli [191], pages393–432.

[185] E. Pourabbas and M. Rafanelli. A pictorial query language for querying ge-ographic databases using positional and OLAP operators. SIGMOD Record,31(2):22–27, 2002.

[186] E. Pourabbas and M. Rafanelli. Hierarchies. In Rafanelli [191], pages 91–115.

[187] N. Prakash and A. Gosain. Requirements driven data warehouse development.In J. Eder and T. Welzer, editors, Proc. of the 15th Int. Conference on AdvancedInformation Systems Engineering, CAiSE’03, pages 13–16, Klagenfurt, Austria,June 2003. Springer-Verlag.

[188] R. Pressman. Software Engineering. McGraw-Hill Education, 2000.

[189] R. Price, N. Tryfona, and C. Jensen. Modeling topological constraints in spatialpart-whole relationships. In Kunii et al. [120], pages 27–40.

[190] M. Rafanelli. Basic notions. In Multidimensional Databases: Problems and So-lutions [191], pages 1–45.

[191] M. Rafanelli, editor. Multidimensional Databases: Problems and Solutions. IdeaGroup Publishing, 2003.

[192] M. Rafanelli and M. Jarke, editors. Proc. of the 10th Int. Conference on Scientificand Statistical Database Management, SSDBM’98, Capri, Italy, July 1998. IEEEComputer Society Press.

[193] M. Rafanelli and A. Shoshani. STORM: A statistical object representation model.In Z. Michalewicz, editor, Proc. of the 9th Int. Conference on Scientific and Sta-tistical Database Management, SSDBM’90, pages 14–29, Charlotte, N.C., USA,April 1990. Springer-Verlag.

278 BIBLIOGRAPHY

[194] F. Rao, L. Zhang, X. Lan, Y. Li, and Y. Chen. Spatial hierarchy and OLAP-favored search in spatial data warehouse. In Proc. of the 6th ACM Int. Workshopon Data Warehousing and OLAP, DOLAP’03, pages 48–55, New Orleans, LA,USA, November 2003. ACM Press.

[195] F. Ravat and O. Teste. A temporal object-oriented data warehouse model. InIbrahim et al. [92], pages 583–592.

[196] M. Riedewals, D. Agrawal, and A. El Abbadi. Efficient integration and aggre-gation of historical information. In M. Franklin, B. Moon, and A. Ailamaki,editors, Proc. of the ACM SIGMOD Int. Conference on Management of Data,SIGMOD’02, pages 13–24, Madison, Wisconsin, USA, June 2002. ACM Press.

[197] P. Rigaux, M. Scholl, and A. Voisard. Spatial Databases with Application to GIS.Morgan Kaufmann Publishers, 2002.

[198] S. Rivest, Y. Bedard, and P. Marchand. Toward better suppport for spatial deci-sion making: Defining the characteristics of spatial on-line analytical processing(SOLAP). Geomatica, 55(4):539–555, 2001.

[199] S. Rivest, Y. Bedard, M. Proulx, M. Nadeau, F. Hubert, and J. Pastor. SO-LAP technology: Merging business intelligence with geospatial technology forinteractive spatio-temporal exploration and analysis of data. ISPRS Journal ofPhotogrammetry & Remote Sensing, 60(1):17–33, 2005.

[200] S. Rizzi. Open problems in data warehousing: 8 years later. In Lenz et al. [126].

[201] S. Robertson and J. Robertson. Mastering the Requirements Process. Addison-Wesley, 1999.

[202] K. Ross, D. Srivastava, and D. Chatziantoniou. Complex aggregation at multiplegranularities. In Schek et al. [205], pages 263–277.

[203] C. Sapia, M. Blaschka, G. Hofling, and B. Dinter. Extending the E/R model formultidimensional paradigm. In T. Ling, S. Ram, and M. Lee, editors, Proc. of the17th Int. Conference on Conceptual Modeling, ER’98, pages 105–116, Singapore,November 1998. Springer-Verlag.

[204] N. Sarda. Temporal issues in data warehouse systems. In Proc. of the Int. Sym-posium on Database Applications in Non-Traditional Environment, DANTE’99,pages 27–34, Kyoto, Japan, November 1999. IEEE Computer Society Press.

[205] H. Schek, F. Saltor, I. Ramos, and G. Alonso, editors. Proc. the 6th Int. Con-ference on Extending Database Technology, EDBT’98, Valencia, Spain, Maarch1998. Springer-Verlag.

[206] J. Schiefer, B. List, and R. Bruckner. A holistic approach for managing require-ments of data warehouse systems. In Proc. of the 8th Conference On InformationSystems, AMCIS’02, pages 77–87, Dallas, Texas, USA, 2002.

BIBLIOGRAPHY 279

[207] S. Shekhar and S. Chawla. Spatial Databases: A Tour. Prentice Hall, 2003.

[208] J. Shim, M. Warkentin, J. Courtney, D. Power, R. Sharda, and C. Carlsson. Past,present and future of decision support technology. Decision Support Systems,32(2):111–126, 2002.

[209] A. Shukla, P. Deshpande, J. Naughton, and K. Ramasamy. Storage estimationfor multidimensional aggregates in the presence of hierarchies. In Vijayaramanet al. [231], pages 522–543.

[210] R. Snodgrass, editor. The TSQL2 Temporal Query Language. Kluwer AcademicPublishers, 1995.

[211] R. Snodgrass, editor. Developing Time-Oriented Database Applications in SQL.Morgan Kaufmann Publishers, 2000.

[212] D. Srivastava, S. Dar, H. Jagadish, and A. Levy. Answering queries with aggre-gation using views. In Vijayaraman et al. [231], pages 318–329.

[213] Stanford University. WHIPS: Data warehousing at Stanford. http://infolab.stanford.edu/warehousing/warehouse.html, 1998.

[214] N. Stefanovic, J. Han, and K. Koperski. Object-based selective materializationfor efficient implementation of spatial data cubes. Transactions on Knowledgeand Data Engineering, TKDE, 12(6):938–958, 2000.

[215] D. Theodoratos, editor. Proc. of the 5th ACM Int. Workshop on Data Ware-housing and OLAP, DOLAP’02, McLean, Virginia, USA, November 2002. ACMPress.

[216] D. Theodoratos, J. Hammer, M. Jeusfeld, and M. Staudt, editors. Proc. of the3rd Int. Workshop on Design and Management of Data Warehouses, DMDW’01,Interlaken, Switzerland, June 2001. CEUR Workshop Proceedings.

[217] E. Thomsen. OLAP Solutions. Building Multidimensional Information Systems.John Wiley & Sons Publishers, 2002.

[218] I. Timko and T. Pedersen. Capturing complex multidimensional data in location-based data warehouses. In R. Laurini, K. Makki, and N. Pissinou, editors, Proc.of the 6th ACM Symposium on Advances in Geographic Information Systems,ACM-GIS’98, pages 147–156, Washington, D.C., USA, November 1998. ACMPress.

[219] D. Tomlin. Geographic Information Systems and Cartographic Modeling. PrenticeHall, 1990.

[220] R. Torlone. Conceptual multidimensional models. In Rafanelli [191], pages 69–90.

[221] J. Trujillo, M. Palomar, J. Gomez, and I. Song. Designing data warehouses withOO conceptual models. IEEE Computer, 34(12):66–75, 2001.

280 BIBLIOGRAPHY

[222] N. Tryfona, F. Busborg, and J. Borch. StarER: A conceptual model for datawarehouse design. In DOLAP’99 [44], pages 3–8.

[223] N. Tryfona and M. Egenhofer. Consistency among parts and aggregates: a com-putational model. Transactions in GIS, 4(3):189–206, 1997.

[224] A. Tsois, N. Karayannidis, and T. Sellis. MAC: Conceptual data modelling forOLAP. In Theodoratos et al. [216], page 5.

[225] U.S. Census Bureau. Standard Hierarchy of Census Geographic Entities andHierarchy of American Indian, Alaska Native, and Hawaiian Entities. http:

//www.census.gov/geo/www/geodiagram.pdf, 2004.

[226] P. van Oosterom, W. Quak, and T. Tijssen. Testing current DBMS products withreal spatial data. In Proc. of the 23rd Urban Data Management Symposium, pagesVII.1–VII.18, Prague, Chech Republic, October 2002.

[227] P. van Oosterom, J. Stoter, W. Quak, and S. Zlatanova. The balance betweengeometry and topology. In D. Richardson and P. van Oosterom, editors, Proc.of the 10th Int. Symposium on Spatial Data Handling, SDH’02, pages 209–224,Berlin, Germany, August 2002. Springer-Verlag.

[228] P. Vassiliadis. Modeling multidimesional databases, cubes and cube operations.In Rafanelli and Jarke [192], page 53.

[229] P. Vassiliadis and T. Sellis. A survey on logical models for OLAP databases.In A. Delis, C. Faloutsos, S. Ghandeharizadeh, and E. Panagos, editors, Proc.of the ACM SIGMOD Int. Conference on Management of Data, SIGMOD’99,pages 64–69, Philadelphia, Pennsylvania, USA, June 1999. ACM Press.

[230] I. Vega, R. Snodgrass, and B. Moon. Spatiotemporal aggregate computation: Asurvey. Technical report, Time Center, TR-77, 2004.

[231] T. Vijayaraman, A. Buchmann, C. Mohan, and N. Sarda, editors. Proc. ofthe 22th Int. Conference on Very Large Data Bases, VLDB’96, Bombay, India,September 1996. Morgan Kaufmann Publishers.

[232] J. Viqueira, N. Lorentzos, and N. Brisaboa. Survey on spatial data modeling ap-proaches. In Y.Manolopoulos, M.Papadopoulos, and M.Vassilakopoulos, editors,Spatial Databases: Technologies. Techniques and Trends. Idea Group Publishing,2005.

[233] X. Wang, C. Bettini, A. Brodsky, and S. Jajodia. Logical design for temporaldatabases with multiple granularities. ACM Transactions on Database Systems,22(2):115–170, 1997.

[234] J. Wijsen. Design of temporal relational databases based on dynamic and tempo-ral functional dependencies. In J. Clifford and A. Tuzhilin, editors, Proc. of theInt. Workshop on Temporal Databases, pages 61–76, Zurich, Switzerland, 1995.Springer-Verlag.

BIBLIOGRAPHY 281

[235] J. Wijsen. Temporal FDs on complex objects. ACM Transactions on DatabaseSystems, 24(1):127–176, 1999.

[236] J. Wijsen. A string-based model for infinite granularities. In Proc. of the AAAIWorkshop on Spatial and Temporal Granularities, pages 9–16, Austin, Texas,USA, July 2000. AAAI Press.

[237] R. Winter and B. Strauch. Demand-driven information requirements analysisin data warehousing. In Proc. of the 36th Hawai’i Int. Conference on SystemScience, HICSS-36, pages 1359–1365, Big Island of Hawaii, January 2003.

[238] M. Worbboys and M. Duckham. GIS A Computing Perspective. CRC Press,2004. Second edition.

[239] J. Yang and J. Widom. Mantaining temporal views over non-temporal informa-tion source for data warehousing. In Schek et al. [205], pages 389–403.

[240] E. Yu. Modeling Strategic Relationships for Process Engineering. PhD thesis,University of Toronto, 1995.

[241] C. Zaniolo, S. Ceri, C. Faloutsos, R. Snodgross, V. Subrahmanian, and R. Zicari.Advanced Database Systems. Morgan Kaufmann Publishers, 1997.

[242] E. Zimanyi and M. Minout. Preserving semantics when transforming conceptualspatio-temporal schemas. In R. Meersman, Z. Tari, and P. Herrero, editors,Proc. of the On the Move to Meaningful Internet Systems Workshops, page 2,Agia Napa, Cuprus, October 2005. Springer-Verlag.

[243] E. Zimanyi, C. Parent, and S. Spaccapietra. TERC+: a temporal conceptualmodel. In Proc. of the Int. Symposium on Digital Media Information, Nara,Japan, November 1997.

[244] S. Zlatanova. On 3D topological relationships. In Proc. of the 11th Int. Work-shop on Database and Expert Systems Applications, DEXA’00, pages 913–924,London/Greenwich, UK, September 2000. IEEE Computer Society Press.