Fundamentals of business intelligence 2015

Data-Centric Systems and Applications

Wilfried Grossmann
Stefanie Rinderle-Ma
Fundamentals of

Business
Intelligence


Data-Centric Systems and Applications

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Wilfried Grossmann • Stefanie Rinderle-Ma

Fundamentals of
Business Intelligence

123


Stefanie Rinderle-Ma
University of Vienna
Vienna
Austria

Wilfried Grossmann
University of Vienna
Vienna
Austria

ISSN 2197-9723
ISSN 2197-974X (electronic)
Data-Centric Systems and Applications
ISBN 978-3-662-46530-1
ISBN 978-3-662-46531-8 (eBook)
DOI 10.1007/978-3-662-46531-8
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Foreword

Intelligent businesses need Business Intelligence (BI). They need it for recognizing,
analyzing, modeling, structuring, and optimizing business processes. They need it,
moreover, for making sense of massive amounts of unstructured data in order to
support and improve highly sensible—if not highly critical—business decisions.
The term “intelligent businesses” does not merely refer to commercial companies
but also to (hopefully) intelligent governments, intelligently managed educational
institutions, efficient hospitals, and so on. Every complex business activity can profit
from BI.
BI has become a mainstream technology and is—according to most information
technology analysts—looking forward to a more brilliant and prosperous future.
Almost all medium and large-sized enterprises and organizations are either already
using BI software or plan to make use of it in the next few years. There is thus a
rapidly growing need of BI specialists. The need of experts in machine learning
and data analytics is notorious. Because these disciplines are central to the Big
Data hype, and because Google, Facebook, and other companies seem to offer an
infinite number of jobs in these areas, students resolutely require more courses in
machine learning and data analytics. Many Computer Science Departments have
consequently strengthened their curricula with respect to these areas.
However, machine learning, including data analytics, is only one part of BI
technology. Before a “machine” can learn from data, one actually needs to collect
the data and present them in a unified form, a process that is often referred to as data
provisioning. This, in turn, requires extracting the data from the relevant business
processes and possibly also from Web sources such as social networks, cleaning,
transforming, and integrating them, and loading them into a data warehouse or other
type of database. To make humans efficiently interact with various stages of these
activities, methods and tools for data visualization are necessary. BI goes, moreover,

much beyond plain data and aims to identify, model, and optimize the business
processes of an enterprise. All these BI activities have been thoroughly investigated,
and each has given rise to a number of monographs and textbooks. What was sorely
missing, however, was a book that ties it all together and that gives a unified view
of the various facets of Business Intelligence.
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vi

Foreword

The present book by Wilfried Grossmann and Stefanie Rinderle-Ma brilliantly
fills this gap. This book is a thoughtful introduction to the major relevant aspects
of BI. The book is, however, not merely an entry point to the field. It develops
the various subdisciplines of BI with the appropriate depth and covers the major
methods and techniques in sufficient detail so as to enable the reader to apply
them in a real-world business context. The book focuses, in particular, on the four
major areas related to BI: (1) data modeling and data provisioning including data
extraction, integration, and warehousing; (2) data and process visualization; (3)
machine learning, data and text mining, and data analytics; and (4) process analysis,
mining, and management. The book does not only cover the standard aspects of BI
but also topics of more recent relevance such as social network analytics and topics
of more specialized interest such as text mining. The authors have done an excellent
job in selecting and combining all topics relevant to a modern approach to Business
Intelligence and to present the corresponding concepts and methods within a unified
framework. To the best of my knowledge, this is the first book that presents BI at
this level of breadth, depth, and coherence.
The authors, Wilfried Grossmann and Stefanie Rinderle-Ma, joined to form
an ideal team towards writing such a useful and comprehensive book about

BI. They are both professors at the University of Vienna but have in addition
gained substantial experience with corporate and institutional BI projects: Stefanie
Rinderle-Ma more in the process management area and Wilfried Grossmann more
in the field of data analytics. To the profit of the reader, they put their knowledge and
experience together to develop a common language and a unified approach to BI.
They are, moreover, experts in presenting material to students and have at the same
time the real-life background necessary for selecting the truly relevant material.
They were able to come up with appropriate and meaningful examples to illustrate
the main concepts and methods. In fact, the four running examples in this book are
grounded in both authors’ rich project experience.
This book is suitable for graduate courses in a Computer Science or Information
Systems curriculum. At the same time, it will be most valuable to data or software
engineers who aim at learning about BI, in order to gain the ability to successfully
deploy BI techniques in an enterprise or other business environment. I congratulate
the authors on this well-written, timely, and very useful book, and I hope the reader
enjoys it and profits from it as much as possible.

Oxford, UK
March 2015

Georg Gottlob


Preface

The main task of business intelligence (BI) is providing decision support for
business activities based on empirical information. The term business is understood
in a rather broad sense covering activities in different domain applications, for
example, an enterprise, a university, or a hospital. In the context of the business
under consideration, decision support can be at different levels ranging from the

operational support for a specific business activity up to strategic support at the top
level of an organization. Consequently, the term BI summarizes a huge set of models
and analytical methods such as reporting, data warehousing, data mining, process
mining, predictive analytics, organizational mining, or text mining.
In this book, we present fundamental ideas for a unified approach towards BI
activities with an emphasis on analytical methods developed in the areas of process
analysis and business analytics.
The general framework is developed in Chap. 1, which also gives an overview on
the structure of the book. One underlying idea is that all kinds of business activities
are understood as a process in time and the analysis of this process can emphasize
different perspectives of the process. Three perspectives are distinguished: (1)
the production perspective, which relates to the supplier of the business; (2) the
customer perspective, which relates to users/consumers of the offered business; and
(3) the organizational perspective, which considers issues such as operations in the
production perspective or social networks in the customer perspective.
Core elements of BI are data about the business, which refer either to the
description of the process or to instances of the process. These data may take
different views on the process defined by the following structural characteristics:
(1) an event view, which records detailed documentation of certain events; (2) a
state view, which monitors the development of certain attributes of process instances
over time; and (3) a cross-sectional view, which gives summary information of
characteristic attributes for process instances recorded within a certain period of
time.
The issues for which decision support is needed are often related to so-called
key performance indicators (KPIs) and to the understanding of how they depend on
certain influential factors, i.e., specificities of the business. For analytical purposes,
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Preface

it is necessary to reformulate a KPI in a number of analytical goals. These goals
correspond to well-known methods of analysis and can be summarized under
the headings business description goals, business prediction goals, and business
understanding goals. Typical business description goals are reporting, segmentation
(unsupervised learning), and the identification of interesting behavior. Business prediction goals encompass estimation and classification and are known as supervised
learning in the context of machine learning. Business understanding goals support
stakeholders in understanding their business processes and may consist in process
identification and process analysis.
Based on this framework, we develop a method format for BI activities oriented
towards ideas of the L format for process mining and CRISP for business analytics.
The main tasks of the format are the business and data understanding task, the data
task, the modeling task, the analysis task, and the evaluation and reporting task.
These tasks define the structure of the following chapters.
Chapter 2 deals with questions of modeling. A broad range of models occur
in BI corresponding to the different business perspectives, a number of possible
views on the processes, and manifold analysis goals. Starting from possible ways
of understanding the term model, the most frequently used model structures in BI
are identified, such as logic-algebraic structures, graph structures, and probabilistic/statistical structures. Each structure is described in terms of its basic properties
and notation as well as algorithmic techniques for solving questions within these
structures. Background knowledge is assumed about these structures at the level of
introductory courses in programs for applied computer science. Additionally, basic
considerations about data generation, data quality, and handling temporal aspects
are presented.
Chapter 3 elaborates on the data provisioning process, ranging from data collection and extraction to a solid description of concepts and methods for transforming
data into analytical data formats necessary for using the data as input for the models
in the analysis. The analytical data formats also cover temporal data as used in
process analysis.

In Chap. 4, we present basic methods for data description and data visualization
that are used in the business and data understanding task as well as in the evaluation
and reporting task. Methods for process-oriented data and cross-sectional data are
considered. Based on these fundamental techniques, we sketch aspects of interactive
and dynamic visualization and reporting.
Chapters 5–8 explain different analytical techniques used for the main analysis
goals of supervised learning (prediction and classification), unsupervised learning
(clustering), as well as process identification and process analysis. Each chapter
is organized in such a way that we first present first an overview of the used
terminology and general methodological considerations. Thereafter, frequently used
analytical techniques are discussed.
Chapter 5 is devoted to analysis techniques for cross-sectional data, basically
traditional data mining techniques. For prediction, different regression techniques
are presented. For classification, we consider techniques based on statistical principles, techniques based on trees, and support vector machines. For unsupervised


Preface

ix

learning, we consider hierarchical clustering, partitioning methods, and modelbased clustering.
Chapter 6 focuses on analysis techniques for data with temporal structure. We
start with probabilistic-oriented models in particular, Markov chains and regressionbased techniques (event history analysis). The remainder of the chapter considers
analysis techniques useful for detecting interesting behavior in processes such as
association analysis, sequence mining, and episode mining.
Chapter 7 treats methods for process identification, process performance management, process mining, and process compliance. In Chap. 8, various analysis
techniques for problems are elaborated, which look at a business process from
different perspectives. The basics of social network analysis, organizational mining,
decision point analysis, and text mining are presented. The analysis of these
problems combines techniques from the previous chapters.

For explanation of a method, we use demonstration examples on the one hand
and more realistic examples based on use cases on the other hand. The latter include
the areas of medical applications, higher education, and customer relationship
management. These use cases are introduced in Chap. 1. For software solutions,
we focus on open source software, mainly R for cross-sectional analysis and ProM
for process analysis. A detailed code for the solutions together with instructions on
how to install the software can be found on the accompanying website:
www.businessintelligence-fundamentals.com
The presentation tries to avoid too much mathematical formalism. For the
derivation of properties of various algorithms, we refer to the corresponding
literature. Throughout the text, you will find different types of boxes. Light grey
boxes are used for the presentation of the use cases, dark grey boxes for templates
that outline the main activities in the different tasks, and white boxes for overview
summaries of important facts and basic structures of procedures.
The material presented in the book was used by the authors in a 4-h course on
Business Intelligence running for two semesters. In case of shorter courses, one
could start with Chaps. 1 and 2, followed by selected topics of Chaps. 3, 5, and 7.
Vienna, Austria
Vienna, Austria

Wilfried Grossmann
Stefanie Rinderle-Ma



Acknowledgements

We thank the following persons for their support and contributions to the book:
Reinhold Dunkl for providing details on the EBMC2 project, Simone Kriglstein for
the support on the example presented in Fig. 4.3, Hans-Georg Fill for the discussions

and support on ontologies, Jürgen Mangler for his help with the HEP data set,
Fengchuan Fan for support on dynamic visualization, Karl-Anton Fröschl for the
inspiring discussions, and Manuel Gatterer for checking the language.
Our greatest gratitude goes to our families for their unconditional support.

xi



Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.1 Definition of Business Intelligence . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.2 Putting Business Intelligence into Context .. . . . . . .. . . . . . . . . . . . . . . . . . . .
1.2.1 Business Intelligence Scenarios . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.2.2 Perspectives in Business Intelligence . . . . .. . . . . . . . . . . . . . . . . . . .
1.2.3 Business Intelligence Views on Business Processes.. . . . . . . . .
1.2.4 Goals of Business Intelligence . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.2.5 Summary: Putting Business Intelligence in Context . . . . . . . . .
1.3 Business Intelligence: Tasks and Analysis Formats . . . . . . . . . . . . . . . . . .
1.3.1 Data Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.3.2 Business and Data Understanding Task . . .. . . . . . . . . . . . . . . . . . . .
1.3.3 Modeling Task. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.3.4 Analysis Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.3.5 Evaluation and Reporting Task . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.3.6 Analysis Formats .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.3.7 Summary: Tasks and Analysis Formats .. .. . . . . . . . . . . . . . . . . . . .
1.4 Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.4.1 Application in Patient Treatment .. . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.4.2 Application in Higher Education . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

1.4.3 Application in Logistics . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.4.4 Application in Customer Relationship Management . . . . . . . . .
1.5 Structure and Outline of the Book .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
1.6 Recommended Reading (Selection) . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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2 Modeling in Business Intelligence . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.1 Models and Modeling in Business Intelligence .. .. . . . . . . . . . . . . . . . . . . .
2.1.1 The Representation Function of Models . .. . . . . . . . . . . . . . . . . . . .
2.1.2 Model Presentation.. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.1.3 Model Building.. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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2.1.4 Model Assessment and Quality of Models.. . . . . . . . . . . . . . . . . . .
2.1.5 Models and Patterns .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.1.6 Summary: Models and Modeling in Business
Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2 Logical and Algebraic Structures .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2.1 Logical Structures .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2.2 Modeling Using Logical Structures . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2.3 Summary: Logical Structures.. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.3 Graph Structures .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.3.1 Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

2.3.2 Modeling with Graph Structures . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.3.3 Summary: Graph Structures . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4 Analytical Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4.1 Calculus.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4.2 Probabilistic Structures . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4.3 Statistical Structures . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4.4 Modeling Methods Using Analytical Structures .. . . . . . . . . . . .
2.4.5 Summary: Analytical Structures.. . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.5 Models and Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.5.1 Data Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.5.2 The Role of Time.. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.5.3 Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.5.4 Summary: Models and Data . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.6 Conclusion and Lessons Learned .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.7 Recommended Reading (Selection) . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3 Data Provisioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.1 Introduction and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.2 Data Collection and Description .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.3 Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.3.1 Extraction-Transformation-Load (ETL) Process . . . . . . . . . . . . .
3.3.2 Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.3.3 Summary on Data Extraction .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.4 From Transactional Data Towards Analytical Data .. . . . . . . . . . . . . . . . . .
3.4.1 Table Formats and Online Analytical Processing (OLAP) .. .
3.4.2 Log Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.4.3 Summary: From Transactional Towards
Analytical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.5 Schema and Data Integration . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.5.1 Schema Integration .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

3.5.2 Data Integration and Data Quality.. . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.5.3 Linked Data and Data Mashups . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.5.4 Summary: Schema and Data Integration ... . . . . . . . . . . . . . . . . . . .
3.6 Conclusion and Lessons Learned .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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3.7 Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 115
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 115
4 Data Description and Visualization . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.1 Introduction .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.2 Description and Visualization of Business Processes.. . . . . . . . . . . . . . . .
4.2.1 Process Modeling and Layout . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.2.2 The BPM Tools’ Perspective . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

4.2.3 Process Runtime Visualization . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.2.4 Visualization of Further Aspects . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.2.5 Challenges in Visualizing Process-Related Information . . . . .
4.2.6 Summary: Description and Visualization
of Business Processes .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.3 Description and Visualization of Data in the Customer
Perspective .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.3.1 Principles for Description and Visualization
of Collections of Process Instances . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.3.2 Interactive and Dynamic Visualization .. . .. . . . . . . . . . . . . . . . . . . .
4.3.3 Summary: Visualization of Process Instances . . . . . . . . . . . . . . . .
4.4 Basic Visualization Techniques .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.1 Description and Visualization of Qualitative
Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.2 Description and Visualization of Quantitative Variables . . . . .
4.4.3 Description and Visualization of Relationships.. . . . . . . . . . . . . .
4.4.4 Description and Visualization of Temporal Data . . . . . . . . . . . . .
4.4.5 Interactive and Dynamic Visualization .. . .. . . . . . . . . . . . . . . . . . . .
4.4.6 Summary: Basic Visualization Techniques . . . . . . . . . . . . . . . . . . .
4.5 Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.5.1 Description and Visualization of Metadata . . . . . . . . . . . . . . . . . . .
4.5.2 High-Level Reporting .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.5.3 Infographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.5.4 Summary: Reporting .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.6 Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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5 Data Mining for Cross-Sectional Data . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.1 Introduction to Supervised Learning . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2 Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.1 Model Formulation and Terminology . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.2 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.4 Kernel Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.5 Smoothing Splines . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.6 Summary: Regression Models .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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5.3

Classification Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.3.1 Model Formulation and Terminology . . . . .. . . . . . . . . . . . . . . . . . . .
5.3.2 Classification Based on Probabilistic Structures.. . . . . . . . . . . . .
5.3.3 Methods Using Trees . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.3.4 K-Nearest-Neighbor Classification . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.3.5 Support Vector Machines . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.3.6 Combination Methods . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

5.3.7 Application of Classification Methods . . . .. . . . . . . . . . . . . . . . . . . .
5.3.8 Summary: Classification Models . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4 Unsupervised Learning.. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.1 Introduction and Terminology .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.3 Partitioning Methods .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.4 Model-Based Clustering . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.5 Summary: Unsupervised Learning . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.5 Conclusion and Lessons Learned .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.6 Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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6 Data Mining for Temporal Data . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.1 Terminology and Approaches Towards Temporal Data Mining.. . . . .
6.2 Classification and Clustering of Time Sequences .. . . . . . . . . . . . . . . . . . . .
6.2.1 Segmentation and Classification Using Time
Warping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.2.2 Segmentation and Classification Using Response
Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.2.3 Summary: Classification and Clustering of Time
Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.3 Time-to-Event Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.4 Analysis of Markov Chains . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.4.1 Structural Analysis of Markov Chains . . . .. . . . . . . . . . . . . . . . . . . .
6.4.2 Cluster Analysis for Markov Chains . . . . . .. . . . . . . . . . . . . . . . . . . .
6.4.3 Generalization of the Basic Model . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.4.4 Summary: Analysis of Markov Chains . . .. . . . . . . . . . . . . . . . . . . .
6.5 Association Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.6 Sequence Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.7 Episode Mining .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.8 Conclusion and Lessons Learned .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.9 Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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7 Process Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.1 Introduction and Terminology . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2 Business Process Analysis and Simulation.. . . . . . .. . . . . . . . . . . . . . . . . . . .

7.2.1 Static Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2.2 Dynamic Analysis and Simulation . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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7.2.3 Optimization.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2.4 Summary: Process Analysis and Simulation.. . . . . . . . . . . . . . . . .
7.3 Process Performance Management and Warehousing . . . . . . . . . . . . . . . .
7.3.1 Performance Management .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.3.2 Process Warehousing . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.3.3 Summary: Process Performance Management
and Warehousing .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.4 Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.4.1 Process Discovery .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.4.2 Change Mining .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.4.3 Conformance Checking .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.4.4 Summary: Process Mining .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.5 Business Process Compliance . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.5.1 Compliance Along the Process Life Cycle.. . . . . . . . . . . . . . . . . . .
7.5.2 Summary: Compliance Checking . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.6 Evaluation and Assessment . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.6.1 Process Mining .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.6.2 Compliance Checking.. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.7 Conclusion and Lessons Learned .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.8 Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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8 Analysis of Multiple Business Perspectives . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.1 Introduction and Terminology . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

8.2 Social Network Analysis and Organizational Mining . . . . . . . . . . . . . . . .
8.2.1 Social Network Analysis. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.2.2 Organizational Aspect in Business Processes.. . . . . . . . . . . . . . . .
8.2.3 Organizational Mining Techniques for Business
Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.2.4 Summary: Social Network Analysis
and Organizational Mining . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.3 Decision Point Analysis .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.4 Text Mining .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.4.1 Introduction and Terminology .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.4.2 Data Preparation and Modeling . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.4.3 Descriptive Analysis for the Document Term Matrix . . . . . . . .
8.4.4 Analysis Techniques for a Corpus .. . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.4.5 Further Aspects of Text Mining . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.4.6 Summary: Text Mining . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.5 Conclusion and Lessons Learned .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
8.6 Recommended Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 319


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A Survey on Business Intelligence Tools . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
A.1 Data Modeling and ETL Support .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
A.2 Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
A.3 Visualization, Visual Mining, and Reporting . . . . .. . . . . . . . . . . . . . . . . . . .
A.4 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
A.5 Process Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
A.6 Text Mining .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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Index . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 343


Chapter 1

Introduction

Abstract In this chapter, we provide definitions of Business Intelligence (BI)
and outline the development of BI over time, particularly carving out current
questions of BI. Different scenarios of BI applications are considered and business

perspectives and views of BI on the business process are identified. Further, the goals
and tasks of BI are discussed from a management and analysis point of view and a
method format for BI applications is proposed. This format also gives an outline of
the book’s contents. Finally, examples from different domain areas are introduced
which are used for demonstration in later chapters of the book.

1.1 Definition of Business Intelligence
If one looks for a definition of the term Business Intelligence (BI) one will find
the first reference already in 1958 in a paper of H.P. Luhn (cf. [14]). Starting
from the definition of the terms “Intelligence” as “the ability to apprehend the
interrelationships of presented facts in such a way as to guide action towards a
desired goal” and “Business” as “a collection of activities carried on for whatever
purpose, be it science, technology, commerce, industry, law, government, defense,
et cetera”, he specifies a business intelligence system as “[an] automatic system
[that] is being developed to disseminate information to the various sections of
any industrial, scientific or government organization.” The main task of Luhn’s
system was automatic abstracting of documents and delivering this information to
appropriate so-called action points.
This definition did not come into effect for 30 years, and in 1989 Howard Dresner
coined the term Business Intelligence (BI) again. He introduced it as an umbrella
term for a set of concepts and methods to improve business decision making,
using systems based on facts. Many similar definitions have been given since. In
Negash [18], important aspects of BI are emphasized by stating that “. . . business
intelligence systems provide actionable information delivered at the right time, at
the right location, and in the right form to assist decision makers.”
Today one can find many different definitions which show that at the top level
the intention of BI has not changed so much. For example, in [20] BI is defined as
“an integrated, company-specific, IT-based total approach for managerial decision

© Springer-Verlag Berlin Heidelberg 2015

W. Grossmann, S. Rinderle-Ma, Fundamentals of Business Intelligence,
Data-Centric Systems and Applications, DOI 10.1007/978-3-662-46531-8_1

1


2

1 Introduction

support” and Wikipedia coins the term BI as “a set of theories, methodologies,
processes, architectures, and technologies that transform raw data into meaningful
and useful information for business purposes.”
Summarizing the different definitions, BI can be characterized by the following
features:
Features of BI
• Task of BI: The main task of BI is providing decision support for specific goals
defined in the context of business activities in different domain areas taking
into account the organizational and institutional framework.
• Foundation of BI: BI decision support mainly relies on empirical information
based on data. Besides this empirical background, BI also uses different types
of knowledge and theories for information generation.
• Realization of BI: The decision support has to be realized as a system using the
actual capabilities in information and communication technologies (ICT).
• Delivery of BI: A BI system has to deliver information at the right time to the
right people in an appropriate form.
Corresponding to the development in ICT and availability of data, we can
distinguish different epochs in BI. The prehistory of BI mainly runs under the
heading decision support systems (DSS) and is documented, for example, in [19].
The review covers the era from the 1960s up to the beginning of the twentyfirst century and considers theory development in computer science, optimization,

and application domains, as well as systems development like model-driven DSS
(planning models or simulation), data-driven DSS (from data bases up to OLAP systems), communication-driven DSS (collaboration networks), document-driven DSS
(document retrieval and analysis), and knowledge-driven DSS (expert systems).
According to Howard Dresner’s definition in 1989, the term BI became popular
in the 1990s and was understood mostly as data-driven decision support closely
connected to the development of data warehouses, the usage of online analytical
processing (OLAP), and reporting tools. In parallel to the developments in the area
of data management, other analysis tools such as data mining or predictive analytics
became popular. Sometimes, these were summarized under the heading business
analytics, and one got the impression that BI is a collection of a loosely related
heterogeneous set of tools supporting different tasks within a business. Hence, it
was necessary to consolidate the different lines of development and to focus again
on the decision support perspective.
One influential approach putting the data warehouse into the center is the Kimball
methodology (cf. [12]). This methodology defines a life cycle for data warehouse
solutions with dimensional modeling as the core element. The design of appropriate
technical architectures supports the realization of a data warehouse. Applications
like reporting and analytical models provide decision makers with the necessary
information.


1.1 Definition of Business Intelligence

3

The software life-cycle model as a framework for integration of different aspects
of BI is used in [17]. Other approaches like CRISP [4] start from the analysis process
in knowledge discovery from databases. Besides such conceptual ideas, one can also
frequently find pragmatic definitions, for example, in [6] it is argued that BI should
be divided into querying, reporting, OLAP, alert tools, and business analytics. In

this definition; business analytics is a subset of BI based on statistics, prediction,
and optimization. In the book, we will follow this idea and understand BI in such a
broad sense.
In the last years, data availability and analysis capabilities have increased
tremendously, and new research areas for BI have emerged. In [22], a number
of topics are listed under the heading Business Intelligence 2.0. Looking at these
topics from the perspective of the four main BI characteristics stated above, one can
organize these new challenges as shown in the overview box.
Actual Challenges of BI
• Tasks of BI: Nowadays we can find a well-structured understanding of the business logic in almost all domain areas. This new understanding has also led to
a process-oriented conceptual view, which integrates workflow considerations
and process mining into BI [23]. Another aspect is that new organizational
structures like decentralized organizations want to apply decision support
within their environment, and, hence, ideas from collective intelligence or
crowd sourcing are applied in BI.
• Foundations of BI: Besides the traditional data warehouse, we also have to take
into account data on the Web. Such data is often not well-structured, but only
semistructured such as text data. The need to integrate different data useful for
decision support in a coherent way has led to models for linking data in BI. In
connection with such new data, the scope of analytical methods has broadened
and new tools such as visual mining, text mining, opinion mining, or social
network analysis have emerged.
• Realization of BI systems: Today’s software architectures allow interesting
new realizations of BI systems. From a user perspective, Software as a
Service (SaaS) constitutes an interesting development for BI systems. From
a computational point of view, we have to deal with large and complex data
sets nowadays. Moreover, cloud computing and distributed computing are
important concepts opening new opportunities for BI applications.
• Delivery of BI: Mobile devices offer a new dimension for delivering information to users in real-time. However, these developments have to take into
account that quality of real-time information is a new challenge for BI.

Obviously, many of the mentioned new developments cover more than one aspect
of the aforementioned BI characteristics, but this classification should support the
understanding that the basic definition and characteristics of BI are still valid.


4

1 Introduction

Due to the importance of BI for business applications, there is a big market,
and many companies offer BI solutions. These vendors create a lot of terms and
acronyms and propose integrated formats for BI applications, but precise and
generally accepted definitions of terms are frequently missing in the BI context.
For an overview on vendors and tools, we refer to [21].

1.2 Putting Business Intelligence into Context
In the previous section, we characterized BI and stated its goals in a rather general
way. In order to make this more precise, we want to discuss first the connection
between business and BI from a management point of view. An interesting reference
in this context that is worth reading is [13].
We understand the term business in a rather broad sense, i.e., as “any kind
of activities of an organization for delivering goods or services to consumers.”
These organizations may be active in different application domains, for example,
an enterprise, an administrative body, a hospital, or an educational institution such
as a university. Besides the different application domains, we have to be aware
that decision support is needed for businesses of different size and scope. By size
we understand a classification of the organization with respect to criteria such as
number of employees (e.g., SMEs or big enterprises), regional dispersion (from
local up to global players), number of customers, or revenues. Scope refers to the
number of activities of the organization for which we look for decision support.

For example in business administration, we may be interested in decision support at
the global level for the enterprise or at a specific functional level (e.g., production
or marketing). In medical applications, our focus may be decision support for
the treatment of a specific disease or for the management of a hospital. In the
administrative context, we can look for decision support for efficient organization
of services or for improving customer satisfaction with the services.

1.2.1 Business Intelligence Scenarios
For development of a general framework of such diverse problems, we will follow
ideas as outlined in [13] which organize BI activities according to principles used
in business enterprises. A management level, an organizational level, a functional
analytical level, and levels for data organization and acquisition are distinguished,
and the role of BI in connection with business models is discussed. As in the
case of BI, there are many definitions of the term business model (cf. [1]), but for
our purpose the following rather naive understanding seems sufficient: A business


1.2 Putting Business Intelligence into Context

5

model reflects the strategy of an enterprise for creating value. There are four
different scenarios that link BI to the business context, ranging from rather simple
applications of decision support for a specific problem up to BI as an essential part
of strategic planning [13].
BI Scenarios
1. Business intelligence separated from strategic management: In this case BI is
mainly concerned with the achievement of short-term targets in a division of an
organization, for example, a department of an enterprise or a clinic in a hospital.
Typically, results of the BI application are more or less standardized reports for

a dedicated part of the business.
2. BI supports monitoring of strategy performance: Such a BI application is
motivated by overall strategic goals and formulated in accordance with these
goals. Monitoring of the performance is done by defining measurable targets.
A data warehouse allowing a unified view onto the business is usually a
prerequisite for such an application scenario.
3. BI feedback on strategy formulation: This application goes one step beyond
the previous strategy and aims at an evaluation of the performance using
analytical methods. In the best case, such an application can be used for the
optimization of a strategy. A typical end-product in this scenario may be a
balanced scorecard.
4. BI as strategic resource: This strategy uses the information generated by BI
not only for optimization but also as an essential input for the definition
of the strategy at the management level. Typical examples are customerbased marketing or development of standard operation procedures for patient
treatment.
Obviously, this classification depends on the size of the organization and the
scope of the business under consideration. For example, a BI application at a
university department may be used as feedback on strategy formulation at the level
of the department but also as a tool for monitoring the performance at the university
level.
At first glance, the third and fourth strategies seem to be favorable, but in general,
we have to take into account specificities of the application, how many resources
can be attributed to BI, and the availability of information. For large productionoriented enterprises, the third option may be a good choice, and in service-oriented
businesses the fourth strategy has yielded many success stories. But sometimes
decision problems occur ad hoc, are hard to formalize, and it is not clear whether
implementation of a high-level strategy is worth it in the long run. Moreover, results
of such ad hoc applications may lead to standardized new BI activities at a higher
strategic level.

6

1 Introduction

1.2.2 Perspectives in Business Intelligence
After the determination of the overall BI strategy, we have to think about the
structure of business activities. The description of the structure is frequently done
by formulating a business process. We understand the term business process as a
collection of related and structured activities necessary for delivering a certain good
or service to customers together with possible response activities of customers.
Note that most definitions of business processes such as [5] omit the last part
of the definition. However, we think that understanding the customer as an active
decision maker inside the business process is more suited for BI. In the book,
generally speaking, we will take the position that all kinds of business activities are
processes, which means that activities take place within a period of time and follow
some rules such as the partial ordering or the exclusion of an activity under certain
conditions. However, we have to be aware that, to some extent, the incorporation of
customer activities into the business process limits the application of the idea that
business activities resemble the structure of purely rule-based activities. Instead of
such a mechanistic consideration of business processes, BI is more concerned with
the empirical realization of business process defined by process instances. In order
to scrutinize these instances, we introduce the following three BI perspectives for
the business process.
Perspectives in BI
• Production perspective: This perspective considers decision support for
answering questions such as what kind of products should be offered to the
customers and how the production should be operated. This perspective plays
an important role for product development and for internal organization of the
business.
• Customer perspective: This perspective focuses on customer behavior and aims

at understanding how customers perceive products or services and how they
react to this offer. The customer perspective plays an essential role in serviceoriented businesses.
• Organizational perspective: This perspective examines the organizational background of the business process. It may refer to the organizational background
for the operations in connection with the production perspective or to the
influence of social networks on customer behavior.
Obviously, such perspectives depend on the application domain, the size, and
the scope of the business. Practical applications usually encompass all three perspectives, but for BI applications such a division is useful for choosing appropriate
information and analysis models. To some extent, this division also reflects the
historical development of models and analytical methods nowadays applied in BI.