IGI global selected readings on database technologies and applications aug 2008 ISBN 1605660981 pdf

95 Efrem Mallach, University of Massachusetts Dartmouth, USA Chapter VII Conceptual Modeling for XML: A Myth or a Reality ...112 Sriram Mohan, Indiana University, USA Arijit Sengupta, Wr

Selected Readings on

Database Technologies and Applications

Terry Halpin

Neumont University, USA

Hershey • New York

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Selected readings on database technologies and applications / Terry Halpin, editor.

p cm.

Summary: "This book offers research articles focused on key issues concerning the development, design, and analysis of Provided by publisher.

Includes bibliographical references and index.

ISBN 978-1-60566-098-1 (hbk.) ISBN 978-1-60566-099-8 (ebook)

1 Databases 2 Database design I Halpin, T A

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005.74 dc22

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Table of Contents

Prologue xviii About the Editor xxvii

Section I Fundamental Concepts and Theories Chapter I

Argiris Tzikopoulos, Agricultural University of Athens, Greece

Nikos Manouselis, Agricultural University of Athens, Greece

Riina Vuorikari, European Schoolnet, Belgium

Chapter IV

Discovering.Quality.Knowledge.from.Relational.Databases 65

M Mehdi Owrang O., American University, USA

Section II Development and Design Methodologies

Chapter V

Business Data Warehouse: The Case of Wal-Mart 85

Indranil Bose, The University of Hong Kong, Hong Kong

Lam Albert Kar Chun, The University of Hong Kong, Hong Kong

Leung Vivien Wai Yue, The University of Hong Kong, Hong Kong

Li Hoi Wan Ines, The University of Hong Kong, Hong Kong

Wong Oi Ling Helen, The University of Hong Kong, Hong Kong

Chapter VI

A Database Project in a Small Company (or How the Real World Doesn’t Always

Follow the Book) 95

Efrem Mallach, University of Massachusetts Dartmouth, USA

Chapter VII

Conceptual Modeling for XML: A Myth or a Reality 112

Sriram Mohan, Indiana University, USA

Arijit Sengupta, Wright State University, USA

Chapter VIII

Designing Secure Data Warehouses 134

Rodolfo Villarroel, Universidad Católica del Maule, Chile

Eduardo Fernández-Medina, Universidad de Castilla-La Mancha, Spain

Juan Trujillo, Universidad de Alicante, Spain

Mario Piattini, Universidad de Castilla-La Mancha, Spain

Chapter IX

Web Data Warehousing Convergence: From Schematic to Systematic 148

D Xuan Le, La Trobe University, Australia

J Wenny Rahayu, La Trobe University, Australia

David Taniar, Monash University, Australia

Section III Tools and Technologies Chapter X

Visual Query Languages, Representation Techniques, and Data Models 174

Maria Chiara Caschera, IRPPS-CNR, Italy

Arianna D’Ulizia, IRPPS-CNR, Italy

Leonardo Tininini, IASI-CNR, Italy

Chapter XI

Application of Decision Tree as a Data Mining Tool in a Manufacturing System 190

S A Oke, University of Lagos, Nigeria

Chapter XII

A Scalable Middleware for Web Databases 206

Athman Bouguettaya, Virginia Tech, USA

Zaki Malik, Virginia Tech, USA

Abdelmounaam Rezgui, Virginia Tech, USA

Lori Korff, Virginia Tech, USA

Chapter XIII

A Formal Verification and Validation Approach for Real-Time Databases 234

Pedro Fernandes Ribeiro Neto, Universidade do Estado–do Rio Grande do Norte, Brazil Maria Lígia Barbosa Perkusich, Universidade Católica de Pernambuco, Brazil

Hyggo Oliveira de Almeida, Federal University of Campina Grande, Brazil

Angelo Perkusich, Federal University of Campina Grande, Brazil

Chapter XIV

A Generalized Comparison of Open Source and Commercial Database Management Systems 252

Theodoros Evdoridis, University of the Aegean, Greece

Theodoros Tzouramanis, University of the Aegean, Greece

Section IV Application and Utilization Chapter XV

An Approach to Mining Crime Patterns 268

Sikha Bagui, The University of West Florida, USA

Chapter XVI

Bioinformatics Web Portals 296

Mario Cannataro, Università “Magna Græcia” di Catanzaro, Italy

Pierangelo Veltri, Università “Magna Græcia” di Catanzaro, Italy

Chapter XVII

An XML-Based Database for Knowledge Discovery: Definition and Implementation 305

Rosa Meo, Università di Torino, Italy

Giuseppe Psaila, Università di Bergamo, Italy

Chapter XVIII

Enhancing UML Models: A Domain Analysis Approach 330

Iris Reinhartz-Berger, University of Haifa, Israel

Arnon Sturm, Ben-Gurion University of the Negev, Israel

Chapter XIX

Seismological Data Warehousing and Mining: A Survey 352

Gerasimos Marketos,University of Piraeus, Greece

Yannis Theodoridis, University of Piraeus, Greece

Ioannis S Kalogeras, National Observatory of Athens, Greece

Section V Critical Issues

Chapter XX

Business Information Integration from XML and Relational Databases Sources 369

Ana María Fermoso Garcia, Pontifical University of Salamanca, Spain

Roberto Berjón Gallinas, Pontifical University of Salamanca, Spain

Chapter XXI

Security Threats in Web-Powered Databases and Web Portals 395

Theodoros Evdoridis, University of the Aegean, Greece

Theodoros Tzouramanis, University of the Aegean, Greece

Chapter XXII

Empowering the OLAP Technology to Support Complex Dimension Hierarchies 403

Svetlana Mansmann, University of Konstanz, Germany

Marc H Scholl, University of Konstanz, Germany

Chapter XXIII

NetCube: Fast, Approximate Database Queries Using Bayesian Networks 424

Dimitris Margaritis, Iowa State University, USA

Christos Faloutsos, Carnegie Mellon University, USA

Sebastian Thrun, Stanford University, USA

Chapter XXIV

Node Partitioned Data Warehouses: Experimental Evidence and Improvements 450

Pedro Furtado, University of Coimbra, Portugal

Section VI Emerging Trends Chapter XXV

Rule Discovery from Textual Data 471

Shigeaki Sakurai, Toshiba Corporation, Japan

Chapter XXVI

Action Research with Internet Database Tools 490

Bruce L Mann, Memorial University, Canada

Chapter XXVII

Database High Availability: An Extended Survey 499

Moh’d A Radaideh, Abu Dhab Police – Ministry of Interior, United Arab Emirates

Hayder Al-Ameed, United Arab Emirates University, United Arab Emirates

Index 528

Detailed Table of Contents

Prologue xviii About the Editor xxvii

Section I Fundamental Concepts and Theories Chapter I

Conceptual.Modeling.Solutions.for.the.Data.Warehouse 1

Stefano Rizzi, DEIS - University of Bologna, Italy

This.opening.chapter.provides.an.overview.of.the.fundamental.role.that.conceptual.modeling.plays.in.data.warehouse design Specifically, research focuses on a conceptual model called the DFM (Dimensional Fact Model), which suits the variety of modeling situations that may be encountered in real projects of.small.to.large.complexity The.aim.of.the.chapter.is.to.propose.a.comprehensive.set.of.solutions.for.conceptual modeling according to the DFM and to give the designer a practical guide for applying them in.the.context.of.a.design.methodology Other.issues.discussed.include.descriptive.and.cross-dimension.attributes; convergences; shared, incomplete, recursive, and dynamic hierarchies; multiple and optional arcs; and additivity

Chapter II

Databases Modeling of Engineering Information 21

Z M Ma, Northeastern University, China

As information systems have become the nerve center of current computer-based engineering, the need for engineering information modeling has become imminent Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling It should be noted that, however, the current mainstream databases are mainly used for business applications Some new engineering requirements challenge today’s database technologies and promote their evolution Database modeling can be clas-sified into two levels: conceptual data modeling and logical database modeling In this chapter, the author tries to identify the requirements for engineering information modeling and then investigates the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models

Chapter III

An Overview of Learning Object Repositories 44

Argiris Tzikopoulos, Agricultural University of Athens, Greece

Nikos Manouselis, Agricultural University of Athens, Greece

Riina Vuorikari, European Schoolnet, Belgium

Learning objects are systematically organized and classified in online databases, which are termed ing object repositories (LORs) Currently, a rich variety of LORs is operating online, offering access

learn-to wide collections of learning objects These LORs cover various educational levels and learn-topics, slearn-tore learning objects and/or their associated metadata descriptions, and offer a range of services that may vary from advanced search and retrieval of learning objects to intellectual property rights (IPR) manage-ment Until now, there has not been a comprehensive study of existing LORs that will give an outline of their overall characteristics For this purpose, this chapter presents the initial results from a survey of 59 well-known repositories with learning resources The most important characteristics of surveyed LORs are examined and useful conclusions about their current status of development are made

Chapter IV

Discovering Quality Knowledge from Relational Databases 65

M Mehdi Owrang O., American University, USA

Current database technology involves processing a large volume of data in order to discover new edge However, knowledge discovery on just the most detailed and recent data does not reveal the long-term trends Relational databases create new types of problems for knowledge discovery since they are normalized to avoid redundancies and update anomalies, which make them unsuitable for knowledge discovery A key issue in any discovery system is to ensure the consistency, accuracy, and completeness

knowl-of the discovered knowledge This selection describes the aforementioned problems associated with the quality of the discovered knowledge and provides solutions to avoid them

Section II Development and Design Methodologies

Chapter V

Business Data Warehouse: The Case of Wal-Mart 85

Indranil Bose, The University of Hong Kong, Hong Kong

Lam Albert Kar Chun, The University of Hong Kong, Hong Kong

Leung Vivien Wai Yue, The University of Hong Kong, Hong Kong

Li Hoi Wan Ines, The University of Hong Kong, Hong Kong

Wong Oi Ling Helen, The University of Hong Kong, Hong Kong

The retailing giant Wal-Mart owes its success to the efficient use of information technology in its erations One of the noteworthy advances made by Wal-Mart is the development of a data warehouse, which gives the company a strategic advantage over its competitors In this chapter, the planning and implementation of the Wal-Mart data warehouse is described and its integration with the operational systems is discussed The chapter also highlights some of the problems encountered in the developmental

op-process of the data warehouse The implications of the recent advances in technologies such as RFID, which is likely to play an important role in the Wal-Mart data warehouse in future, are also detailed in this chapter.

Chapter VI

A Database Project in a Small Company (or How the Real World Doesn’t Always

Follow the Book) 95

Efrem Mallach, University of Massachusetts Dartmouth, USA

The selection presents a small consulting company’s experience in the design and implementation of a database and associated information retrieval system The company’s choices are explained within the context of the firm’s needs and constraints Issues associated with development methods are discussed, along with problems that arose from not following proper development disciplines Ultimately, the author asserts that while the system provided real value to its users, the use of proper development disciplines could have reduced some problems while not reducing that value

Chapter VII

Conceptual Modeling for XML: A Myth or a Reality 112

Sriram Mohan, Indiana University, USA

Arijit Sengupta, Wright State University, USA

Conceptual design is independent of the final platform and the medium of implementation, and is ally in a form that is understandable to managers and other personnel who may not be familiar with the low-level implementation details, but have a major influence in the development process Although a strong design phase is involved in most current application development processes, conceptual design for XML has not been explored significantly in literature or in practice In this chapter, the reader is introduced to existing methodologies for modeling XML A discussion is then presented comparing and contrasting their capabilities and deficiencies, and delineating the future trend in conceptual design for XML applications

usu-Chapter VIII

Designing Secure Data Warehouses 134

Rodolfo Villarroel, Universidad Católica del Maule, Chile

Eduardo Fernández-Medina, Universidad de Castilla-La Mancha, Spain

Juan Trujillo, Universidad de Alicante, Spain

Mario Piattini, Universidad de Castilla-La Mancha, Spain

As an organization’s reliance on information systems governed by databases and data warehouses (DWs) increases, so does the need for quality and security within these systems Since organizations generally deal with sensitive information such as patient diagnoses or even personal beliefs, a final DW solution should restrict the users that can have access to certain specific information This chapter presents a comparison of six design methodologies for secure systems Also presented are a proposal for the design

of secure DWs and an explanation of how the conceptual model can be implemented with Oracle Label Security (OLS10g)

Chapter IX

Web Data Warehousing Convergence: From Schematic to Systematic 148

D Xuan Le, La Trobe University, Australia

J Wenny Rahayu, La Trobe University, Australia

David Taniar, Monash University, Australia

This chapter proposes a data warehouse integration technique that combines data and documents from different underlying documents and database design approaches Well-defined and structured data, semi-structured data, and unstructured data are integrated into a Web data warehouse system and user specified requirements and data sources are combined to assist with the definitions of the hierarchical structures A conceptual integrated data warehouse model is specified based on a combination of user requirements and data source structure, which necessitates the creation of a logical integrated data ware-house model A case study is then developed into a prototype in a Web-based environment that enables the evaluation The evaluation of the proposed integration Web data warehouse methodology includes the verification of correctness of the integrated data, and the overall benefits of utilizing this proposed integration technique

Section III Tools and Technologies Chapter X

Visual Query Languages, Representation Techniques, and Data Models 174

Maria Chiara Caschera, IRPPS-CNR, Italy

Arianna D’Ulizia, IRPPS-CNR, Italy

Leonardo Tininini, IASI-CNR, Italy

An easy, efficient, and effective way to retrieve stored data is obviously one of the key issues of any information system In the last few years, considerable effort has been devoted to the definition of more intuitive, visual-based querying paradigms, attempting to offer a good trade-off between expressive-ness and intuitiveness In this chapter, the authors analyze the main characteristics of visual languages specifically designed for querying information systems, concentrating on conventional relational data-bases, but also considering information systems with a less rigid structure such as Web resources storing XML documents Two fundamental aspects of visual query languages are considered: the adopted visual representation technique and the underlying data model, possibly specialized to specific application contexts

Chapter XI

Application of Decision Tree as a Data Mining Tool in a Manufacturing System 190

S A Oke, University of Lagos, Nigeria

This selection demonstrates the application of decision tree, a data mining tool, in the manufacturing system Data mining has the capability for classification, prediction, estimation, and pattern recognition

by using manufacturing databases Databases of manufacturing systems contain significant information

for decision making, which could be properly revealed with the application of appropriate data mining techniques Decision trees are employed for identifying valuable information in manufacturing databases Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost The work shows that it is valuable for managers to mine data for better and more effective decision making

Chapter XII

A Scalable Middleware for Web Databases 206

Athman Bouguettaya, Virginia Tech, USA

Zaki Malik, Virginia Tech, USA

Abdelmounaam Rezgui, Virginia Tech, USA

Lori Korff, Virginia Tech, USA

The emergence of Web databases has introduced new challenges related to their organization, access, integration, and interoperability New approaches and techniques are needed to provide across-the-board transparency for accessing and manipulating Web databases irrespective of their data models, platforms, locations, or systems In meeting these needs, it is necessary to build a middleware infrastructure to sup-port flexible tools for information space organization communication facilities, information discovery, content description, and assembly of data from heterogeneous sources This chapter describes a scalable middleware for efficient data and application access built using available technologies The resulting system, WebFINDIT, is a scalable and uniform infrastructure for locating and accessing heterogeneous and autonomous databases and applications

Chapter XIII

A Formal Verification and Validation Approach for Real-Time Databases 234

Pedro Fernandes Ribeiro Neto, Universidade do Estado–do Rio Grande do Norte, Brazil Maria Lígia Barbosa Perkusich, Universidade Católica de Pernambuco, Brazil

Hyggo Oliveira de Almeida, Federal University of Campina Grande, Brazil

Angelo Perkusich, Federal University of Campina Grande, Brazil

Real-time database-management systems provide efficient support for applications with data and actions that have temporal constraints, such as industrial automation, aviation, and sensor networks, among others Many issues in real-time databases have brought interest to research in this area, such as: concurrence control mechanisms, scheduling policy, and quality of services management However, considering the complexity of these applications, it is of fundamental importance to conceive formal verification and validation techniques for real-time database systems This chapter presents a formal verification and validation method for real-time databases Such a method can be applied to database systems developed for computer integrated manufacturing, stock exchange, network-management, and command-and-control applications and multimedia systems

trans-Chapter XIV

A Generalized Comparison of Open Source and Commercial Database Management Systems 252

Theodoros Evdoridis, University of the Aegean, Greece

Theodoros Tzouramanis, University of the Aegean, Greece

This chapter attempts to bring to light the field of one of the less popular branches of the open source software family, which is the open source database management systems branch In view of the objec-tive, the background of these systems is first briefly described followed by presentation of a fair generic database model Subsequently and in order to present these systems under all their possible features, the main system representatives of both open source and commercial origins will be compared in relation

to this model, and evaluated appropriately By adopting such an approach, the chapter’s initial concern

is to ensure that the nature of database management systems in general can be apprehended The overall orientation leads to an understanding that the gap between open and closed source database management systems has been significantly narrowed, thus demystifying the respective commercial products

Section IV Application and Utilization Chapter XV

An Approach to Mining Crime Patterns 268

Sikha Bagui, The University of West Florida, USA

This selection presents a knowledge discovery effort to retrieve meaningful information about crime from

a U.S state database The raw data were preprocessed, and data cubes were created using Structured Query Language (SQL) The data cubes then were used in deriving quantitative generalizations and for further analysis of the data An entropy-based attribute relevance study was undertaken to determine the relevant attributes A machine learning software called WEKA was used for mining association rules, developing a decision tree, and clustering SOM was used to view multidimensional clusters on

a regular two-dimensional grid

Chapter XVI

Bioinformatics Web Portals 296

Mario Cannataro, Università “Magna Græcia” di Catanzaro, Italy

Pierangelo Veltri, Università “Magna Græcia” di Catanzaro, Italy

Bioinformatics involves the design and development of advanced algorithms and computational platforms

to solve problems in biomedicine (Jones & Pevzner, 2004) It also deals with methods for acquiring, storing, retrieving and analysing biological data obtained by querying biological databases or provided

by experiments Bioinformatics applications involve different datasets as well as different software tools and algorithms Such applications need semantic models for basic software components and need advanced scientific portal services able to aggregate such different components and to hide their details and complexity from the final user For instance, proteomics applications involve datasets, either pro-duced by experiments or available as public databases, as well as a huge number of different software tools and algorithms To use such applications, it is required to know both biological issues related to data generation and results interpretation and informatics requirements related to data analysis

Chapter XVII

An XML-Based Database for Knowledge Discovery: Definition and Implementation 305

Rosa Meo, Università di Torino, Italy

Giuseppe Psaila, Università di Bergamo, Italy

Inductive databases have been proposed as general purpose databases to support the KDD process Unfortunately, the heterogeneity of the discovered patterns and of the different conceptual tools used

to extract them from source data make integration in a unique framework difficult In this chapter, ing XML as the unifying framework for inductive databases is explored, and a new model, XML for data mining (XDM), is proposed The basic features of the model are presented, based on the concepts

us-of data item (source data and patterns) and statement (used to manage data and derive patterns) This model uses XML namespaces (to allow the effective coexistence and extensibility of data mining opera-tors) and XML schema, by means of which the schema, state and integrity constraints of an inductive database are defined

Chapter XVIII

Enhancing UML Models: A Domain Analysis Approach 330

Iris Reinhartz-Berger, University of Haifa, Israel

Arnon Sturm, Ben-Gurion University of the Negev, Israel

UML has been largely adopted as a standard modeling language The emergence of UML from different modeling languages has caused a wide variety of completeness and correctness problems in UML mod-els Several methods have been proposed for dealing with correctness issues, mainly providing internal consistency rules, but ignoring correctness and completeness with respect to the system requirements and the domain constraints This chapter proposes the adoption of a domain analysis approach called application-based domain modeling (ADOM) to address the completeness and correction problems of UML models Experimental results from a study which checks the quality of application models when utilizing ADOM on UML suggest that the proposed domain helps in creating more complete models without compromising comprehension

Chapter XIX

Seismological Data Warehousing and Mining: A Survey 352

Gerasimos Marketos,University of Piraeus, Greece

Yannis Theodoridis, University of Piraeus, Greece

Ioannis S Kalogeras, National Observatory of Athens, Greece

Earthquake data is comprised of an ever increasing collection of earth science information for processing analysis Earth scientists, as well as local and national administration officers, use these data collections for scientific and planning purposes In this chapter, the authors discuss the architecture of a seismic data management and mining system (SDMMS) for quick and easy data collection, processing, and visualization The SDMMS architecture includes a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining Template schemes are provided for these two components and examples of how these components support decision making are given A comparative survey of existing operational or prototype SDMMS is also offered

post-Section V Critical Issues Chapter XX

Business Information Integration from XML and Relational Databases Sources 369

Ana María Fermoso Garcia, Pontifical University of Salamanca, Spain

Roberto Berjón Gallinas, Pontifical University of Salamanca, Spain

Roberto Berjón Gallinas, Pontifical University of Salamanca, Spain

This chapter introduces different alternatives to store and manage jointly relational and eXtensible Markup Language (XML) data sources Nowadays, businesses are transformed into e-business and have to manage large data volumes and from heterogeneous sources To manage large amounts of in-formation, Database Management Systems (DBMS) continue to be one of the most used tools, and the most extended model is the relational one On the other side, XML has reached the de facto standard to present and exchange information between businesses on the Web Therefore, it could be necessary to use tools as mediators to integrate these two different data to a common format like XML, since it is the main data format on the Web First, a classification of the main tools and systems where this problem

is handled is made, with their advantages and disadvantages The objective will be to propose a new system to solve the integration business information problem

Chapter XXI

Security Threats in Web-Powered Databases and Web Portals 395

Theodoros Evdoridis, University of the Aegean, Greece

Theodoros Tzouramanis, University of the Aegean, Greece

It is a strongly held view that the scientific branch of computer security that deals with Web-powered databases (Rahayu & Taniar, 2002) that can be accessed through Web portals (Tatnall, 2005) is both complex and challenging This is mainly due to the fact that there are numerous avenues available for

a potential intruder to follow in order to break into the Web portal and compromise its assets and functionality This is of vital importance when the assets that might be jeopardized belong to a legally sensitive Web database such as that of an enterprise or government portal, containing sensitive and confidential information It is obvious that the aim of not only protecting against, but mostly preventing from potential malicious or accidental activity that could set a Web portal’s asset in danger, requires an attentive examination of all possible threats that may endanger the Web-based system

Chapter XXII

Empowering the OLAP Technology to Support Complex Dimension Hierarchies 403

Svetlana Mansmann, University of Konstanz, Germany

Marc H Scholl, University of Konstanz, Germany

Comprehensive data analysis has become indispensable in a variety of domains OLAP (On-Line lytical Processing) systems tend to perform poorly or even fail when applied to complex data scenarios The restriction of the underlying multidimensional data model to admit only homogeneous and balanced dimension hierarchies is too rigid for many real-world applications and, therefore, has to be overcome in order to provide adequate OLAP support The authors of this chapter present a framework for classifying

Ana-and modeling complex multidimensional data, with the major effort at the conceptual level of transforming irregular hierarchies to make them navigable in a uniform manner The properties of various hierarchy types are formalized and a two-phase normalization approach is proposed: heterogeneous dimensions are reshaped into a set of well-behaved homogeneous subdimensions, followed by the enforcement of summarizability in each dimension’s data hierarchy The power of the current approach is exemplified using a real-world study from the domain of academic administration.

Chapter XXIII

NetCube: Fast, Approximate Database Queries Using Bayesian Networks 424

Dimitris Margaritis, Iowa State University, USA

Christos Faloutsos, Carnegie Mellon University, USA

Sebastian Thrun, Stanford University, USA

This chapter presents a novel method for answering count queries from a large database approximately and quickly This method implements an approximate DataCube of the application domain, which can be used to answer any conjunctive count query that can be formed by the user The DataCube is a conceptual device that in principle stores the number of matching records for all possible such queries However, because its size and generation time are inherently exponential, the current approach uses one or more Bayesian networks to implement it approximately By means of such a network, the proposed method, called NetCube, exploits correlations and independencies among attributes to answer a count query quickly without accessing the database Experimental results show that NetCubes have fast generation and use, achieve excellent compression and have low reconstruction error while also naturally allowing for visualization and data mining

Chapter XXIV

Node Partitioned Data Warehouses: Experimental Evidence and Improvements 450

Pedro Furtado, University of Coimbra, Portugal

Data Warehouses (DWs) with large quantities of data present major performance and scalability lenges, and parallelism can be used for major performance improvement in such context However, instead of costly specialized parallel hardware and interconnections, the authors of this selection focus

chal-on low-cost standard computing nodes, possibly in a nchal-on-dedicated local network In this envirchal-onment, special care must be taken with partitioning and processing Experimental evidence is used to analyze the shortcomings of a basic horizontal partitioning strategy designed for that environment, and then im-provements to allow efficient placement for the low-cost Node Partitioned Data Warehouse are proposed and tested A simple, easy-to-apply partitioning and placement decision that achieves good performance improvement results is analyzed This chapter’s experiments and discussion provides important insight into partitioning and processing issues for data warehouses in shared-nothing environments

Section VI Emerging Trends Chapter XXV

Rule Discovery from Textual Data 471

Shigeaki Sakurai, Toshiba Corporation, Japan

This chapter introduces knowledge discovery methods based on a fuzzy decision tree from textual data The author argues that the methods extract features of the textual data based on a key concept dictionary, which is a hierarchical thesaurus, and a key phrase pattern dictionary, which stores characteristic rows

of both words and parts of speech, and generate knowledge in the format of a fuzzy decision tree The author also discusses two application tasks One is an analysis system for daily business reports and the other is an e-mail analysis system The author hopes that the methods will provide new knowledge for researchers engaged in text mining studies, facilitating their understanding of the importance of the fuzzy decision tree in processing textual data

Chapter XXVI

Action Research with Internet Database Tools 490

Bruce L Mann, Memorial University, Canada

This chapter discusses and presents examples of Internet database tools, typical instructional methods used with these tools, and implications for Internet-supported action research as a progressively deeper examination of teaching and learning First, the author defines and critically explains the use of arti-facts in an educational setting and then differentiates between the different types of artifacts created by both students and teachers Learning objects and learning resources are also defined and, as the chapter concludes, three different types of instructional devices – equipment, physical conditions, and social mechanisms or arrangements – are analyzed and an exercise is offered for both differentiating between and understanding differences in instruction and learning

Chapter XXVII

Database High Availability: An Extended Survey 499

Moh’d A Radaideh, Abu Dhab Police – Ministry of Interior, United Arab Emirates

Hayder Al-Ameed, United Arab Emirates University, United Arab Emirates

With the advancement of computer technologies and the World Wide Web, there has been an sion in the amount of available e-services, most of which represent database processing Efficient and effective database performance tuning and high availability techniques should be employed to ensure that all e-services remain reliable and available all times To avoid the impacts of database downtime, many corporations have taken interest in database availability The goal for some is to have continuous availability such that a database server never fails Other companies require their content to be highly available In such cases, short and planned downtimes would be allowed for maintenance purposes This chapter is meant to present the definition, the background, and the typical measurement factors of high availability It also demonstrates some approaches to minimize a database server’s shutdown time

explo-Index 528

xviii

Prologue

historical oVErViEw of databasE tEchnology

This prologue provides a brief historical perspective of developments in database technology, and then reviews and contrasts three current approaches to elevate the initial design of database systems to a conceptual level

Beginning in the late 1970s, the old network and hierarchic database management systems (DBMSs) began to be replaced by relational DBMSs, and by the late 1980s relational systems performed sufficiently well that the recognized benefits of their simple bag-oriented data structure and query language (SQL) made relational DBMSs the obvious choice for new database applications In particular, the simplicity of

Codd’s relational model of data where all facts are stored in relations (sets of ordered n-tupes) facilitated

data access and optimization for a wide range of application domains (Codd, 1970) Although Codd’s data model was purely set-oriented, industrial relational DBMSs and SQL itself are bag-oriented, since SQL allows keyless tables, and SQL queries queries may return multisets (Melton & Simon, 2002).Unlike relational databases, network and hierarchic databases store facts in not only record types but also navigation paths between record types For example, in a hierarchic database the fact that employee

101 works for the Sales department would be stored as a parent-child link from a department record (an instance of the Department record type where the deptName attribute has the value ‘Sales’) to an em-ployee record (an instance of the Employee record type where the empNr attribute has the value 101).Although relational systems do support foreign key “relationships” between relations, these relation-ships are not navigation paths; instead they simply encode constraints (e.g each deptName in an Employee table must also occur in the primary key of the Department table) rather than ground facts For example, the ground fact that employee 101 works for the Sales department is stored by entering the values 101,

‘Sales’ in the empNr and deptName columns on the same row of the Employee table

In 1989, a group of researchers published “The Object-Oriented Database System Manifesto” in which they argued that object-oriented databases should replace relational databases (Atkinson et al 1989) Influenced by object-oriented programming languages, they felt that databases should support not only core databases features such as persistence, concurrency, recovery, and an ad hoc query facility, but also object-oriented features such as complex objects, object identity, encapsulation of behavior with data, types or classes, inheritance (subtyping), overriding and late binding, computational completeness, and extensibility Databases conforming to this approach are called object-oriented databases (OODBs) or simply object databases (ODBs)

Partly in response to the OODB manifesto, one year later a group of academic and industrial searchers proposed an alternative “3rd generation DBMS manifesto” (Stonebraker et al., 1990) They considered network and hierarchic databases to be first generation, and relational databases to be second generation, and argued that third generation databases should retain the capabilities of relational systems while extending them with object-oriented features Databases conforming to this approach are called object-relational databases (ORDBs)

xix

While other kinds of databases (e.g deductive, temporal, and spatial) were also developed to address specific needs, none of these has gained a wide following in industry Deductive databases typically provide a declarative query language such as a logic programming language (e.g Prolog), giving them powerful rule enforcement mechanisms with built-in backtracking and strong support for recursive rules (e.g computing the transitive closure of an ancestor relation)

Spatial databases provide efficient management of spatial data, such as maps (e.g for cal applications), 2-D visualizations (e.g for circuit designs), and 3-D visualizations (e.g for medical imaging) Built-in support for spatial data types (e.g points, lines, polygons) and spatial operators (e.g intersect, overlap, contains) facilitates queries of a spatial nature (e.g how many residences lie within

geographi-3 miles of the proposed shopping center?)

Temporal databases provide built-in support for temporal data types (e.g instant, duration, period) and temporal operators (e.g before, after, during, contains, overlaps, precedes, starts, minus), facilitating queries of a temporal nature (e.g which conferences overlap in time?)

A more recent proposal for database technology employs XML (eXtensible Markup Language) XML databases store data in XML (eXtensible Markup Language), with their structure conforming either to the old DTD (Document Type Definition) or the newer XSD (XML Schema Definition) format Like the old hierarchic databases, XML is hierarchic in nature However XML is presented as readable text, using tags to provide the structure For example, the facts that employees 101 and 102 work for the Sales department could be stored (along with their names and birth dates) in XML as follows

<department name = “Sales”>

OWL includes three versions OWL Lite provides a decidable, efficient mechanism for simple tologies composed mainly of classification hierarchies and relationships with simple constraints OWL

on-DL (the “on-DL” refers to Description Logic) is based on a stronger SHOIN(D) description logic that is

still decidable OWL Full is more expressive but is undecidable, and even goes beyond even first order logic

xx

All of the above database technologies are still in use, to varying degrees While some legacy systems still use the old network and hierarchic DBMSs, new database applications are not built on these obso-lete technologies Object databases, deductive databases, and temporal databases provide advantages for niche markets However the industrial database world is still dominated by relational and object-relational DBMSs In practice, ORDBs have become the dominant DBMS, since virtually all the major industrial relational DBMSs (e.g Oracle, IBM DB2, and Microsoft SQL Server) extended their systems with object-oriented features, and also expanded their support for data types including XML The SQL standard now includes support for collection types (e.g arrays, row types and multisets, recursive queries and XML) Some ORDBMSs (e.g Oracle) include support for RDF While SQL is still often used for data exchange, XML is being increasingly used for exchanging data between applications

In practice, most applications use an object model for transient (in-memory) storage, while using

an RDB or ORDB for persistent storage This has led to extensive efforts to facilitate transformation between these differently structured data stores (known as Object-Relational mapping) One interesting initiative in this regard is Microsoft’s Language Integrated Query (LINQ) technology, which allows users

to interact with relational data by using an SQL-like syntax in their object-oriented program code Recently there has been a growing recognition that the best way to develop database systems is by transformation from a high level, conceptual schema that specifies the structure of the data in a way that can be easily understood and hence validated by the (often nontechnical) subject matter experts, who are the only ones who can reliably determine whether the proposed models accurately reflect their business domains

While this notion of model driven development was forcefully and clearly proposed over a quarter century ago in an ISO standard (van Griethuysen, 1982), only in the last decade has it begun to be widely accepted by major commercial interests Though called differently by different bodies (e.g the Object management Group calls it “Model Driven Architecture” and Microsoft promotes model driven development based on Domain Specific Languages) the basic idea is to clearly specify the business domain model at a conceptual level, and then transform it as automatically as possible to application code, thereby minimizing the need for human programming In the next section we review and contrast three of the most popular approaches to specifying high level data models for subsequent transformation into database schemas

concEptual databasE modEling approachEs

In industry, most database designers either use a variant of Entity Relationship (ER) modeling or simply

design directly at the relational level The basic ER approach was first proposed by Chen (1976), and structures facts in terms of entities (e.g Person, Car) that have attributes (e.g gender, birthdate) and participate in relationships (e.g Person drives Car) The most popular industrial versions of ER are the Barker ER notation (Barker, 1990), Information Engineering (IE) (Finkelstein, 1998), and IDEF1X (IEEE, 1999) IDEF1X is actually a hybrid of ER and relational, explicitly using relational concepts such as foreign keys Barker ER is currently the best and most expressive of the industrial ER notations,

so we focus our ER discussion on it

1997 as a language for object-oriented (OO) analysis and design After several minor revisions, a major overhaul resulted in UML version 2.0 (OMG, 2003), and the language is still being refined Although suitable for object-oriented code design, UML is less suitable for information analysis (e.g even UML

2 does not include a graphic way to declare that an attribute is unique), and its textual Object Constraint

xxi

Language (OCL) is too technical for most business people to understand (Warmer & Kleppe, 2003) For such reasons, although UML is widely used for documenting object-oriented programming applications,

it is far less popular than ER for database design

Despite their strengths, both ER and UML are fairly weak at capturing the kinds of business rules found in data-intensive applications, and their graphical language does not lend itself readily to verbal-ization and multiple instantiation for validating data models with domain experts

These problems can be remedied by using a fact-oriented approach for information analysis, where

communication takes place in simple sentences, each sentence type can easily be populated with multiple instances, attributes are avoided in the base model, and far more business rules can be captured graphi-cally At design time, a fact-oriented model can be used to derive an ER model, a UML class model, or

a logical database model

Object Role Modeling (ORM), the main exemplar of the fact-oriented approach, originated in

Eu-rope in the mid-1970s (Falkenberg, 1976), and been extensively revised and extended since, along with commercial tool support (e.g Halpin, Evans, Hallock & MacLean, 2003) Recently, a major upgrade to the methodology resulted in ORM 2, a second generation ORM (Halpin 2005; Halpin & Morgan 2008) Neumont ORM Architect (NORMA), an open source tool accessible online at www.ORMFoundation.org, is under development to provide deep support for ORM 2 (Curland & Halpin, 2007)

ORM pictures the world simply in terms of objects (entities or values) that play roles (parts in tionships) For example, you are now playing the role of reading, and this prologue is playing the role

rela-of being read Wherever ER or UML uses an attribute, ORM uses a relationship For example, the Person.

in this relationship may be given the rolename “birthdate”

ORM is less popular than either ER or UML, and its diagrams typically consume more space because

of their attribute-free nature However, ORM arguably offers many advantages for conceptual analysis,

as illustrated by the following example, which presents the same data model using the three different notations

In terms of expressibility for data modeling, ORM supports relationships of any arity (unary, binary, ternary or longer), identification schemes of arbitrary complexity, asserted, derived, and semiderived facts and types, objectified associations, mandatory and uniqueness constraints that go well beyond ER and UML in dealing with n-ary relationships, inclusive-or constraints, set comparison (subset, equality, exclusion) constraints of arbitrary complexity, join path constraints, frequency constraints, object and role cardinality constraints, value and value comparison constraints, subtyping (asserted, derived and semiderived), ring constraints (e.g asymmetry, acyclicity), and two rule modalities (alethic and deontic (Halpin, 2007a)) For some comparisons between ORM 1 and ER and UML see Halpin (2002, 2004)

As well as its rich notation, ORM includes detailed procedures for constructing ORM models and transforming them to other kinds of models (ER, UML, Relational, XSD etc.) on the way to implementa-tion For a general discussion of such procedures, see Halpin & Morgan (2008) For a detailed discussion

of using ORM to develop the data model example discussed below, see Halpin (2007b)

Figure 1 shows an ORM schema for a fragment of a book publisher application Entity types appear

as named, soft rectangles, with simple identification schemes parenthesized (e.g Books are identified by their ISBN) Value types (e.g character strings) appear as named, dashed, soft rectangles (e.g BookTitle) Predicates are depicted as a sequence of one or more role boxes, with at least one predicate reading By default, predicates are ready left-right or top-down Arrow tips indicate other predicate reading direc-tions An asterisk after a predicate reading indicates the fact type is derived (e.g best sellers are derived using the derivation rule shown) Role names may be displayed in square brackets next to the role (e.g totalCopiesSold)

xxii

A bar over a sequence of one or more roles depicts a uniqueness constraint (e.g each book has at most one booktitle, but a book may be authored by many persons and vice versa) The external unique-ness constraint (circled bar) reflects the publisher’s policy of publishing at most one book of any given title in any given year A dot on a role connector indicates that role is mandatory (e.g each book has a booktitle)

Subtyping is depicted by an arrow from subtype to supertype In this case, the PublishedBook subtype

is derived (indicated by an asterisk), so a derivation rule for it is supplied Value constraints are placed

in braces (e.g the possible codes for Gender are ‘M’ and ‘F’)

The ring constraint on the book translation fact type indicates that relationship is acyclic The clusion constraint (circled X) ensures that no person may review a book that he or she authors The frequency constraint (≥ 2) ensures that any book assigned for review has at least two reviewers The subset constraint (circled ⊆) ensures that if a person has a title that is restricted to a specific gender (e.g

ex-‘Mrs’ is restricted to females), then that person must be of that gender—an example of a constraint on a conceptual join path The textual declarations provide a subtype definition and two derivation rules, one

in attribute style (using role names) and one in relational style ORM schemas can also be automatically verbalized in natural languages sentences, enabling validation by domain experts without requiring them

to understand the notation (Curland & Halpin, 2007)

Figure 2 depicts the same model in Barker ER notation, supplemented by textual rules (6 numbered constraints, plus 3 derivations) that cannot be captured in this notation

Barker ER depicts entity types as named, soft rectangles Mandatory attributes are preceded by an asterisk and optional attributes by “o” An attribute that is part of the primary identifier is preceded by

“#”, and a role that is part of an identifier has a stroke “|” through it

All relationships must be binary, with each half of a relationship line depicting a role A crowsfoot indicates a maximum cardinality of many A line end with no crowsfoot indicates a maximum cardinal-ity of one A solid line end indicates the role is mandatory, and a dashed line end indicates the role is optional Subtyping is depicted by Euler diagrams with the subtype inside the supertype Unlike ORM and UML, Barker ER supports only single inheritance, and requires that the subtyping always forms a partition

Figure 1 Book publisher schema in ORM

Book (ISBN)

is authored by

Person (.nr)

is assigned for review by

“ReviewAssignment !”

PersonName has/is of

Gender (.code)

is of

{‘M’, ‘F’}

has PersonTitle

is restricted to resulted in

Grade (.nr) {1 5}

BookTitle

has

Year (CE) was published in

Published Book*

is translated from

… in … sold

NrCopies

sold total- * is a best seller*

Each PublishedBook is a Book that was published in some Year.

* For each PublishedBook, totalCopiesSold= sum(copiesSoldInYear).

* PublishedBook is a best seller iff PublishedBook sold total NrCopies >= 10000.

[copiesSoldInYear]

[totalCopiesSold]

≥ 2

xxiii

Figure 3 shows the same model as a class diagram in UML, supplemented by several textual rules captured either as informal notes (e.g acyclic) or as formal constraints in OCL (e.g yearPublished -> notEmpty()) or as nonstandard notations in braces (e.g., the {P} for preferred identifier and {Un} for uniqueness are not standard UML) Derived attributes are preceded by a slash Attribute multiplicities are assumed to be 1 (i.e exactly one) unless otherwise specified (e.g restrictedGender has a multiplicity

of [0 1], i.e at most one) A “*” for maximum multiplicity indicates “many”

Figure 3 Book publisher schema in UML, supplemented by extra rules

Figure 2 Book publisher schema in Barker ER, supplemented by extra rules

REVIEW ASSIGNMENT

o grade

with for

assigned

by allocated

has that gender.

Person

reviewer

bookAuthored bookReviewed

«enumeration»

gendercode

m f

title.restrictedGender -> isEmpty()

Each (yrSold, publishedBook)

combination applies to at most

one SalesFigure

xxiv

Part of the problem with the UML and ER models is that in these approaches personTitle and gender would normally be treated as attributes, but for this application we need to talk about them to capture a relevant business rule The ORM model arguably provides a more natural representation of the business domain, while also formally capturing much more semantics with its built-in constructs, facilitating transformation to executable code This result is typical for industrial business domains

Figure 4 shows the relational database schema obtained by mapping these data schemas via ORM’s Rmap algorithm (Halpin & Morgan, 2008), using absorption as the default mapping for subtyping Here square brackets indicate optional, dotted arrows indicate subset constraints, and a circled “X” depicts

an exclusion constraint Additional constraints are depicted as numbered textual rules in a high level relational notation For implementation, these rules are transformed further into SQL code (e.g check clauses, triggers, stored procedures, views)

conclusion

While many kinds of database technology exist, RDBs and ORDBs currently dominate the market, with XML being increasingly used for data exchange While ER is still the main conceptual modeling approach for designing databases, UML is gaining a following for this task, and is already widely used for object oriented code design Though less popular than ER or UML, the fact-oriented approach exemplified by ORM has many advantages for conceptual data analysis, providing richer coverage of business rules, easier validation by business domain experts, and semantic stability (ORM models and queries are un-impacted by changes that require one to talk about an attribute) Because ORM models may be used to generate ER and UML models, it may also be used in conjunction with these if desired

Figure 4 Book publisher relational schema

Book ( isbn, title, [yearPublished], [translationSource] ) SalesFigure ( isbn, yearSold, copiesSold )

Authorship ( personNr, isbn ) ReviewAssignment ( personNr, isbn, [grade] ) Person ( personNr, personName, gender, personTitle ) TitleRestriction ( personTitle, gender )

View: SoldBook (isbn, totalCopiesSold, isaBestSeller )

6 not exists(Person join TitleRestriction on personTitle

where Person.gender <> TitleRestriction.gender).

xxv

With a view to providing better support at the conceptual level, the OMG recently adopted the mantics of Business Vocabulary and Business Rules (SBVR) specification (OMG, 2007) Like ORM, the SBVR approach is fact oriented instead of attribute-based, and includes deontic as well as alethic rules Many companies are now looking to model-driven development as a way to dramatically increase the productivity, reliability, and adaptability of software engineering approaches It seems likely that both object-oriented and fact-oriented approaches will be increasingly utilized in the future to increase the proportion of application code that can be generated from higher level models

Se-rEfErEncEs

Atkinson, M., Bancilhon, F., DeWitt, D., Dittrick, K., Maier, D & Zdonik, S (1989) The

Object-Ori-ented Database System Manifesto In W Kim, J-M Nicolas & S Nishio (Eds), Proc DOOD-89: First

Int Conf on Deductive and Object-Oriented Databases (pp 40–57) Elsevier.

Barker, R (1990) CASE*Method: Entity Relationship Modelling, Addison-Wesley, Wokingham.

Berners-Lee, T., Hendler, J & Lassila, O (2001) ‘The Semantic Web’, Scientific American, May

2001

Bloesch, A & Halpin, T (1997) Conceptual queries using ConQuer-II In D Embley & R Goldstein

(Eds.), Proc 16th Int Conf on Conceptual Modeling ER’97 (pp 113-126) Berlin: Springer

Booch, G., Rumbaugh, J & Jacobson, I (1999) The Unified Modeling Language User Guide Reading:

Addison-Wesley

Chen, P (1976) ‘The Entity-Relationship Model—Toward a Unified View of Data’, ACM Transactions

Codd, E (1970) A Relational Model of Data for Large Shared Data Banks CACM, vol 13, no 6, pp

377−87

Curland, M & Halpin, T (2007) Model Driven Development with NORMA In: Proc HICSS-40,

CD-ROM, IEEE Computer Society

Falkenberg, E (1976) Concepts for modelling information In G Nijssen (Ed.), Modelling in Data Base

Management Systems (pp 95-109) Amsterdam: North-Holland

Finkelstein, C (1998) ‘Information Engineering Methodology’, Handbook on Architectures of Information

Systems, eds P Bernus, K Mertins & G Schmidt, Springer-Verlag, Berlin, Germany, pp 405–27.

Halpin, T (2002) Information Analysis in UML and ORM: a Comparison Advanced Topics in Database

Research, vol 1, K Siau (Ed.), Hershey PA: Idea Publishing Group, Ch XVI (pp 307-323).

Halpin, T (2004) Comparing Metamodels for ER, ORM and UML Data Models In: Siau K (ed)

Ad-vanced Topics in Database Research, vol 3, Idea Pub Group, Hershey, pp 23–44.

Halpin, T (2005) ORM 2 In: Meersman R et al (eds) On the Move to Meaningful Internet Systems

2005: OTM 2005 Workshops, LNCS vol 3762 Springer, Berlin Heidelberg New York, pp 676–687.

Halpin, T (2006) Object-Role Modeling (ORM/NIAM) In: Handbook on Architectures of Information

xxvi

Halpin, T (2007a) Modality of Business Rules In: Research Issues in Systems Analysis and Design,

Databases and Software Development, ed K Siau, IGI Publishing, Hershey, pp 206-226.

Halpin, T (2007b) Fact-Oriented Modeling: Past, Present and Future In: Krogstie J, Opdahl A,

Brinkkem-per S (eds) Conceptual Modelling in Information Systems Engineering Springer, Berlin, pp 19-38 Halpin, T & Bloesch, A (1999) Data modeling in UML and ORM: a comparison Journal of Database

Management, 10(4), 4-13.

Halpin, T., Evans, K, Hallock, P & MacLean, W (2003) Database Modeling with Microsoft® Visio for

Enterprise Architects, San Francisco: Morgan Kaufmann.

Halpin, T & Morgan, T (2008) Information Modeling and Relational Databases 2 nd Edn San

Fran-cisco: Morgan Kaufmann

IEEE (1999) IEEE standard for conceptual modeling language syntax and semantics for IDEF1X 97

ter Hofstede, A., Proper, H & van der Weide, T (1993) Formal definition of a conceptual language for

the description and manipulation of information models Information Systems 18(7), 489-523.

Jacobson, I., Booch, G & Rumbaugh, J (1999) The Unified Software Development Process Reading:

Stonebraker, M., Rowe, L., Lindsay, B., Gray, J., Carey, M., Brodie, M., Bernstein, P & Beech, D (1990)

‘Third Generation Database System Manifesto’, ACM SIGMOD Record, vol 19, no 3.

van Griethuysen, J (ed.) (1982) Concepts and Terminology for the Conceptual Schema and the

Infor-mation Base, ISO TC97/SC5/WG3, Eindhoven.

Warmer, J & Kleppe, A (2003) The Object Constraint Language: Getting Your Models Ready for MDA,

Second Edition Reading: Addison-Wesley.

xxvii

About the Editor

Terry Halpin, BSc, DipEd, BA, MLitStud, PhD, is distinguished professor and vice president (Conceptual

Modeling) at Neumont University His industry experience includes several years in data modeling technology

at Asymetrix Corporation, InfoModelers Inc., Visio Corporation, and Microsoft Corporation His doctoral thesis formalized Object-Role Modeling (ORM/NIAM), and his current research focuses on conceptual modeling and conceptual query technology He has authored over 150 technical publications and five books, including Informa-tion Modeling and Relational Databases and has co-edited four books on information systems modeling research

He is a member of IFIP WG 8.1 (Information Systems) and several academic program committees, is an editor or

reviewer for several academic journals, is a regular columnist for the Business Rules Journal, and has presented

seminars and tutorials at dozens of international conferences Dr Halpin is the recipient of the DAMA International Achievement Award for Education (2002) and the IFIP Outstanding Service Award (2006)

Section I

Fundamental Concepts

and Theories



Chapter I

Conceptual Modeling Solutions

for the Data Warehouse

Stefano Rizzi

DEIS - University of Bologna, Italy

abstract

In the context of data warehouse design, a basic

role is played by conceptual modeling, that

pro-vides a higher level of abstraction in describing

the warehousing process and architecture in all

its aspects, aimed at achieving independence of

implementation issues This chapter focuses on

a conceptual model called the DFM that suits

the variety of modeling situations that may be

encountered in real projects of small to large

complexity The aim of the chapter is to propose

a comprehensive set of solutions for conceptual

modeling according to the DFM and to give the

designer a practical guide for applying them in

the context of a design methodology Besides the basic concepts of multidimensional modeling, the other issues discussed are descriptive and cross-dimension attributes; convergences; shared, incomplete, recursive, and dynamic hierarchies; multiple and optional arcs; and additivity

introduction

Operational databases are focused on recording transactions, thus they are prevalently character-ized by an OLTP (online transaction processing) workload Conversely, data warehouses (DWs) allow complex analysis of data aimed at decision



Conceptual Modeling Solutions for the Data Warehouse

support; the workload they support has

com-pletely different characteristics, and is widely

known as OLAP (online analytical processing)

Traditionally, OLAP applications are based on

multidimensional modeling that intuitively

rep-resents data under the metaphor of a cube whose

cells correspond to events that occurred in the

business domain (Figure 1) Each event is

quanti-fied by a set of measures; each edge of the cube

corresponds to a relevant dimension for analysis,

typically associated to a hierarchy of attributes

that further describe it The multidimensional

model has a twofold benefit On the one hand,

it is close to the way of thinking of data

analyz-ers, who are used to the spreadsheet metaphor;

therefore it helps users understand data On the

other hand, it supports performance improvement

as its simple structure allows designers to predict

the user intentions

Multidimensional modeling and OLAP

work-loads require specialized design techniques In

the context of design, a basic role is played by

conceptual modeling that provides a higher level

of abstraction in describing the warehousing

pro-cess and architecture in all its aspects, aimed at

achieving independence of implementation issues

Conceptual modeling is widely recognized to be the necessary foundation for building a database that is well-documented and fully satisfies the user requirements; usually, it relies on a graphical notation that facilitates writing, understanding, and managing conceptual schemata by both de-signers and users

Unfortunately, in the field of data warehousing there still is no consensus about a formalism for conceptual modeling (Sen & Sinha, 2005) The entity/relationship (E/R) model is widespread

in the enterprises as a conceptual formalism to provide standard documentation for relational information systems, and a great deal of effort has been made to use E/R schemata as the input for designing nonrelational databases as well (Fahrner

& Vossen, 1995); nevertheless, as E/R is oriented

to support queries that navigate associations tween data rather than synthesize them, it is not well suited for data warehousing (Kimball, 1996) Actually, the E/R model has enough expressivity

be-to represent most concepts necessary for modeling

a DW; on the other hand, in its basic form, it is not able to properly emphasize the key aspects of the multidimensional model, so that its usage for DWs is expensive from the point of view of the

Figure 1 The cube metaphor for multidimensional modeling

Conceptual Modeling Solutions for the Data Warehouse

graphical notation and not intuitive (Golfarelli,

Maio, & Rizzi, 1998)

Some designers claim to use star schemata

for conceptual modeling A star schema is the

standard implementation of the multidimensional

model on relational platforms; it is just a

(denor-malized) relational schema, so it merely defines

a set of relations and integrity constraints Using

the star schema for conceptual modeling is like

starting to build a complex software by writing

the code, without the support of and static,

func-tional, or dynamic model, which typically leads

to very poor results from the points of view of

adherence to user requirements, of maintenance,

and of reuse

For all these reasons, in the last few years the

research literature has proposed several original

approaches for modeling a DW, some based on

extensions of E/R, some on extensions of UML

This chapter focuses on an ad hoc conceptual

model, the dimensional fact model (DFM), that

was first proposed in Golfarelli et al (1998) and

continuously enriched and refined during the

fol-lowing years in order to optimally suit the variety

of modeling situations that may be encountered in

real projects of small to large complexity The aim

of the chapter is to propose a comprehensive set

of solutions for conceptual modeling according to

the DFM and to give a practical guide for

apply-ing them in the context of a design methodology

Besides the basic concepts of multidimensional

modeling, namely facts, dimensions, measures,

and hierarchies, the other issues discussed are

descriptive and cross-dimension attributes; vergences; shared, incomplete, recursive, and dynamic hierarchies; multiple and optional arcs; and additivity

con-After reviewing the related literature in the next section, in the third and fourth sections,

we introduce the constructs of DFM for basic and advanced modeling, respectively Then, in the fifth section we briefly discuss the different methodological approaches to conceptual design Finally, in the sixth section we outline the open issues in conceptual modeling, and in the last section we draw the conclusions

rElatEd litEraturE

In the context of data warehousing, the literature proposed several approaches to multidimensional modeling Some of them have no graphical support and are aimed at establishing a formal foundation for representing cubes and hierarchies as well as

an algebra for querying them (Agrawal, Gupta, & Sarawagi, 1995; Cabibbo & Torlone, 1998; Datta

& Thomas, 1997; Franconi & Kamble, 2004a; Gyssens & Lakshmanan, 1997; Li & Wang, 1996; Pedersen & Jensen, 1999; Vassiliadis, 1998); since we believe that a distinguishing feature of conceptual models is that of providing a graphical support to be easily understood by both designers and users when discussing and validating require-ments, we will not discuss them

Table 1 Approaches to conceptual modeling

E/R extension object-oriented ad hoc



Conceptual Modeling Solutions for the Data Warehouse

The approaches to “strict” conceptual

model-ing for DWs devised so far are summarized in

Table 1 For each model, the table shows if it is

associated to some method for conceptual design

and if it is based on E/R, is object-oriented, or is

an ad hoc model

The discussion about whether E/R-based,

object-oriented, or ad hoc models are preferable

is controversial Some claim that E/R extensions

should be adopted since (1) E/R has been tested for

years; (2) designers are familiar with E/R; (3) E/R

has proven flexible and powerful enough to adapt

to a variety of application domains; and (4) several

important research results were obtained for the

E/R (Sapia, Blaschka, Hofling, & Dinter, 1998;

Tryfona, Busborg, & Borch Christiansen, 1999)

On the other hand, advocates of object-oriented models argue that (1) they are more expressive and better represent static and dynamic properties of information systems; (2) they provide powerful mechanisms for expressing requirements and constraints; (3) object-orientation is currently the dominant trend in data modeling; and (4) UML, in particular, is a standard and is naturally extensible (Abelló, Samos, & Saltor, 2002; Luján-Mora, Trujillo, & Song, 2002) Finally, we believe that ad hoc models compensate for the lack of familiarity from designers with the fact that (1) they achieve better notational economy; (2) they give proper emphasis to the peculiarities of the

(Luján-Mora et al., 2002), and a fact schema (Hüsemann, Lechtenbörger, & Vossen, 2000)

Conceptual Modeling Solutions for the Data Warehouse

multidimensional model, thus (3) they are more

intuitive and readable by nonexpert users In

par-ticular, they can model some constraints related

to functional dependencies (e.g., convergences

and cross-dimensional attributes) in a simpler

way than UML, that requires the use of formal

expressions written, for instance, in OCL

A comparison of the different models done

by Tsois, Karayannidis, and Sellis (2001) pointed

out that, abstracting from their graphical form,

the core expressivity is similar In confirmation

of this, we show in Figure 2 how the same simple

fact could be modeled through an E/R based, an

object-oriented, and an ad hoc approach

thE dimEnsional fact modEl:

basic modEling

In this chapter we focus on an ad hoc model

called the dimensional fact model The DFM is a

graphical conceptual model, specifically devised

for multidimensional modeling, aimed at:

• Effectively supporting conceptual design

• Providing an environment on which user

queries can be intuitively expressed

• Supporting the dialogue between the designer and the end users to refine the specification of requirements

• Creating a stable platform to ground logical design

• Providing an expressive and non-ambiguous design documentation

The representation of reality built using the

DFM consists of a set of fact schemata The basic

concepts modeled are facts, measures, sions, and hierarchies In the following we intui-tively define these concepts, referring the reader

dimen-to Figure 3 that depicts a simple fact schema for modeling invoices at line granularity; a formal definition of the same concepts can be found in Golfarelli et al (1998)

Definition 1: A fact is a focus of

interest for the decision-making cess; typically, it models a set of events occurring in the enterprise world A fact

pro-is graphically represented by a box with two sections, one for the fact name and one for the measures



Conceptual Modeling Solutions for the Data Warehouse

Examples of facts in the trade domain are sales,

shipments, purchases, claims; in the financial

domain: stock exchange transactions, contracts

for insurance policies, granting of loans, bank

statements, credit cards purchases It is essential

for a fact to have some dynamic aspects, that is,

to evolve somehow across time

Guideline 1: The concepts

repre-sented in the data source by

frequently-updated archives are good candidates

for facts; those represented by

almost-static archives are not

As a matter of fact, very few things are

com-pletely static; even the relationship between cities

and regions might change, if some border were

revised Thus, the choice of facts should be based

either on the average periodicity of changes, or

on the specific interests of analysis For instance,

assigning a new sales manager to a sales

depart-ment occurs less frequently than coupling a

promotion to a product; thus, while the

relation-ship between promotions and products is a good

candidate to be modeled as a fact, that between

sales managers and departments is not—except

for the personnel manager, who is interested in

analyzing the turnover!

Definition 2: A measure is a

numer-ical property of a fact, and describes one

of its quantitative aspects of interests

for analysis Measures are included in

the bottom section of the fact

For instance, each invoice line is measured by

the number of units sold, the price per unit, the net

amount, and so forth The reason why measures

should be numerical is that they are used for

computations A fact may also have no measures,

if the only interesting thing to be recorded is the

occurrence of events; in this case the fact scheme

is said to be empty and is typically queried to

count the events that occurred

Definition 3: A dimension is a

fact property with a finite domain and describes one of its analysis coordi-nates The set of dimensions of a fact determines its finest representation granularity Graphically, dimensions are represented as circles attached to the fact by straight lines

Typical dimensions for the invoice fact are product, customer, agent, and date

Guideline 2: At least one of the

dimensions of the fact should represent time, at any granularity

The relationship between measures and mensions is expressed, at the instance level, by the concept of event

di-Definition 4: A primary event is an

occurrence of a fact, and is identified by

a tuple of values, one for each sion Each primary event is described

dimen-by one value for each measure

Primary events are the elemental information which can be represented (in the cube metaphor, they correspond to the cube cells) In the invoice example they model the invoicing of one product

to one customer made by one agent on one day;

it is not possible to distinguish between invoices possibly made with different types (e.g., active, passive, returned, etc.) or in different hours of the day

Guideline 3: If the granularity of

primary events as determined by the set of dimensions is coarser than the granularity of tuples in the data source, measures should be defined as either ag-gregations of numerical attributes in the data source, or as counts of tuples

Conceptual Modeling Solutions for the Data Warehouse

Remarkably, some multidimensional models

in the literature focus on treating dimensions

and measures symmetrically (Agrawal et al.,

1995; Gyssens & Lakshmanan, 1997) This is

an important achievement from both the point

of view of the uniformity of the logical model

and that of the flexibility of OLAP operators

Nevertheless we claim that, at a conceptual level,

distinguishing between measures and dimensions

is important since it allows logical design to be

more specifically aimed at the efficiency required

by data warehousing applications

Aggregation is the basic OLAP operation,

since it allows significant information useful for

decision support to be summarized from large

amounts of data From a conceptual point of

view, aggregation is carried out on primary events

thanks to the definition of dimension attributes

and hierarchies

Definition 5: A dimension attribute

is a property, with a finite domain, of

a dimension Like dimensions, it is

represented by a circle

For instance, a product is described by its type,

category, and brand; a customer, by its city and

its nation The relationships between dimension

attributes are expressed by hierarchies

Definition 6: A hierarchy is a

directed tree, rooted in a dimension,

whose nodes are all the dimension

at-tributes that describe that dimension,

and whose arcs model many-to-one

associations between pairs of

dimen-sion attributes Arcs are graphically

represented by straight lines

Guideline 4: Hierarchies should

reproduce the pattern of interattribute

functional dependencies expressed by

the data source

Hierarchies determine how primary events can be aggregated into secondary events and selected significantly for the decision-making process The dimension in which a hierarchy is rooted defines its finest aggregation granular-ity, while the other dimension attributes define progressively coarser granularities For instance, thanks to the existence of a many-to-one associa-tion between products and their categories, the invoicing events may be grouped according to the category of the products

Definition 7: Given a set of

di-mension attributes, each tuple of their

values identifies a secondary event

that aggregates all the corresponding primary events Each secondary event

is described by a value for each measure that summarizes the values taken by the same measure in the corresponding primary events

We close this section by surveying some alternative terminology used either in the lit-erature or in the commercial tools There is

substantial agreement on using the term

dimen-sions to designate the “entry points” to classify

and identify events; while we refer in particular

to the attribute determining the minimum fact granularity, sometimes the whole hierarchies are named as dimensions (for instance, the term

“time dimension” often refers to the whole erarchy built on dimension date) Measures are

hi-sometimes called variables or metrics Finally, in some data warehousing tools, the term hierarchy

denotes each single branch of the tree rooted in



Conceptual Modeling Solutions for the Data Warehouse

cross-dimension attributes; convergences; shared,

incomplete, recursive, and dynamic hierarchies;

multiple and optional arcs; and additivity Though

some of them are not necessary in the simplest and

most common modeling situations, they are quite

useful in order to better express the multitude of

conceptual shades that characterize real-world

scenarios In particular we will see how,

follow-ing the introduction of some of this constructs,

hierarchies will no longer be defined as trees to

become, in the general case, directed graphs

descriptive attributes

In several cases it is useful to represent additional

information about a dimension attribute, though

it is not interesting to use such information for

aggregation For instance, the user may ask for

knowing the address of each store, but the user

will hardly be interested in aggregating sales

according to the address of the store

Definition 8: A descriptive

attri-bute specifies a property of a dimension

attribute, to which is related by an

x-to-one association Descriptive attributes

are not used for aggregation; they are

always leaves of their hierarchy and are

graphically represented by horizontal lines

There are two main reasons why a descriptive attribute should not be used for aggregation:

Guideline 5: A descriptive

attri-bute either has a continuously-valued domain (for instance, the weight of a product), or is related to a dimension at-tribute by a one-to-one association (for instance, the address of a customer)

cross-dimension attributes

Definition 9: A cross-dimension attribute is a

(either dimension or descriptive) attribute whose value is determined by the combination of two or more dimension attributes, possibly belonging to different hierarchies It is denoted by connecting through a curve line the arcs that determine it

For instance, if the VAT on a product depends

on both the product category and the state where the product is sold, it can be represented by a cross-dimension attribute as shown in Figure 4

Conceptual Modeling Solutions for the Data Warehouse

convergence

Consider the geographic hierarchy on dimension

customer (Figure 4): customers live in cities, which

are grouped into states belonging to nations

Suppose that customers are grouped into sales

districts as well, and that no inclusion relationships

exist between districts and cities/states; on the

other hand, sales districts never cross the nation

boundaries In this case, each customer belongs

to exactly one nation whichever of the two paths

is followed (customer → city → state → nation or

customer → sales district → nation)

Definition 10: A convergence takes

place when two dimension attributes

within a hierarchy are connected by

two or more alternative paths of

many-to-one associations Convergences are

represented by letting two or more

arcs converge on the same dimension

attribute

The existence of apparently equal attributes

does not always determine a convergence If in

the invoice fact we had a brand city attribute on

a brand is manufactured, there would be no

con-vergence with attribute (customer) city, since a

product manufactured in a city can obviously be

sold to customers of other cities as well

optional arcs

Definition 11: An optional arc models the fact

that an association represented within the fact

scheme is undefined for a subset of the events

An optional arc is graphically denoted by

mark-ing it with a dash

For instance, attribute diet takes a value only

for food products; for the other products, it is

undefined

In the presence of a set of optional arcs exiting

from the same dimension attribute, their coverage

can be denoted in order to pose a constraint on the optionalities involved Like for IS-A hierar-chies in the E/R model, the coverage of a set of optional arcs is characterized by two independent

coordinates Let a be a dimension attribute, and

b1, , b m be its children attributes connected by optional arcs:

• The coverage is total if each value of a always

corresponds to a value for at least one of its

children; conversely, if some values of a exist

for which all of its children are undefined,

the coverage is said to be partial.

• The coverage is disjoint if each value of a

corresponds to a value for, at most, one of its children; conversely, if some values of

a exist that correspond to values for two or

more children, the coverage is said to be

is partial and disjoint

multiple arcs

In most cases, as already said, hierarchies include attributes related by many-to-one associations On the other hand, in some situations it is necessary to include also attributes that, for a single value taken

by their father attribute, take several values

Definition 12: A multiple arc is

an arc, within a hierarchy, modeling a many-to-many association between the two dimension attributes it connects

0

Conceptual Modeling Solutions for the Data Warehouse

Graphically, it is denoted by doubling

the line that represents the arc

Consider the fact schema modeling the sales

of books in a library, represented in Figure 5,

whose dimensions are date and book Users will

probably be interested in analyzing sales for

each book author; on the other hand, since some

books have two or more authors, the relationship

between book and author must be modeled as a

multiple arc

Guideline 6: In presence of

many-to-many associations, summarizability

is no longer guaranteed, unless the

mul-tiple arc is properly weighted Mulmul-tiple

arcs should be used sparingly since, in

ROLAP logical design, they require

complex solutions

Summarizability is the property of correcting

summarizing measures along hierarchies (Lenz &

Shoshani, 1997) Weights restore summarizability,

but their introduction is artificial in several cases;

for instance, in the book sales fact, each author

of a multiauthored book should be assigned a

normalized weight expressing her “contribution”

to the book

shared hierarchies

Sometimes, large portions of hierarchies are

replicated twice or more in the same fact schema

A typical example is the temporal hierarchy: a fact frequently has more than one dimension of type date, with different semantics, and it may

be useful to define on each of them a temporal hierarchy month-week-year Another example are geographic hierarchies, that may be defined starting from any location attribute in the fact schema To avoid redundancy, the DFM provides

a graphical shorthand for denoting hierarchy sharing Figure 4 shows two examples of shared hierarchies Fact INVOICE LINE has two date di-mensions, with semantics invoice date and order date, respectively This is denoted by doubling the circle that represents attribute date and specifying

two roles invoice and order on the entering arcs

The second shared hierarchy is the one on agent, that may have two roles: the ordering agent, that

is a dimension, and the agent who is responsible for a customer (optional)

Guideline 8: Explicitly representing

shared hierarchies on the fact schema is important since, during ROLAP logi-cal design, it enables ad hoc solutions aimed at avoiding replication of data

in dimension tables

ragged hierarchies

Let a1, , a n be a sequence of dimension attributes that define a path within a hierarchy (such as city, state, nation) Up to now we assumed that,

for each value of a1, exactly one value for every

Conceptual Modeling Solutions for the Data Warehouse

other attribute on the path exists In the

previ-ous case, this is actually true for each city in the

U.S., while it is false for most European countries

where no decomposition in states is defined (see

Figure 6)

Definition 13: A ragged (or

incom-plete) hierarchy is a hierarchy where,

for some instances, the values of one

or more attributes are missing (since

undefined or unknown) A ragged

hier-archy is graphically denoted by marking

with a dash the attributes whose values

may be missing

As stated by Niemi (2001), within a ragged

hierarchy each aggregation level has precise and

consistent semantics, but the different hierarchy

instances may have different length since one or

more levels are missing, making the interlevel

relationships not uniform (the father of “San

Francisco” belongs to level state, the father of

“Rome” to level nation)

There is a noticeable difference between a

ragged hierarchy and an optional arc In the first

case we model the fact that, for some hierarchy

instances, there is no value for one or more

attri-butes in any position of the hierarchy Conversely,

through an optional arc we model the fact that

there is no value for an attribute and for all of

its descendents.

Guideline 9: Ragged hierarchies

may lead to summarizability problems

A way for avoiding them is to fragment

a fact into two or more facts, each including a subset of the hierarchies characterized by uniform interlevel relationships

Thus, in the invoice example, fragmenting

INVOICE LINE (the first with the state attribute, the second without state) restores the completeness

of the geographic hierarchy

unbalanced hierarchies

Definition 14: An unbalanced (or recursive)

hier-archy is a hierhier-archy where, though interattribute

relationships are consistent, the instances may have different length Graphically, it is represented

by introducing a cycle within the hierarchy

A typical example of unbalanced hierarchy is the one that models the dependence interrelation-ships between working persons Figure 4 includes

an unbalanced hierarchy on sale agents: there are

no fixed roles for the different agents, and the different “leaf” agents have a variable number

of supervisor agents above them

Guideline 10: Recursive

hierar-chies lead to complex solutions during ROLAP logical design and to poor

Figure 6 Ragged geographic hierarchies