Master | Information Systems, Computer Engineering

Business Information Systems

Information Systems, Computer Engineering


Information systems form the basis of the design, improvement and maintenance of business processes. They have grown into the largest and most complex technical products and form a highly integrated part of the operational processes within companies. 

An organization's information system is an essential component and if the system does not function as it should, it brings all other processes to a standstill. Whether multinationals, banks, insurance companies, government ministries, hospitals, travel companies or online shops, none of these can operate without a perfectly functioning information system. 

The integration of IT systems in business processes requires experts who possess knowledge of both computer science and business administration. Our Master's program in Business Information Systems trains those experts. 

The Master's degree program in Business Information Systems (BIS) combines computer science subjects with subjects related to business management and industrial engineering and management sciences. The program covers various aspects:

- The use of models in the design of information systems 

- The implementation of models in information systems 

- The management, specification and analysis of company information systems

Many of the compulsory courses are in the field of computer science and information systems. In the compulsory courses in the field of industrial engineering and management sciences however, the emphasis lies in logistics and operational management.



  • Business Process Management
  • Business Intelligence and Analytics
  • ICT Services
  • ICT in Healthcare


Schedule Full-time
Duration 2 Years
Presence of students On-campus
Scholarships available No



The course consists of four parts, which address respectively an introduction to information system architecture, the architecture of concrete information systems, the role of information technology in the design and implementation of architectures, and the conceptual and methodological aspects of architecture design and analysis. Details are as follows:

Part 1 (3 Lectures): Introduction to Information System Architecture

  • the nature of information systems architecture
  • the role of architecture and architect in the design of complex information systems
  • the levels at and aspects in which architecture can be described
  • architecture styles, architecture patterns and reference architectures

Part 2 (2 Lectures): Architecture of Concrete Systems

  • architecture of data-centric systems
  • architecture of process-centric systems
  • architecture of business function-centric systems


Part 3 (2 Lectures): Architecture Embodiment

  • component technology, service technology
  • middleware technology, internet technology
  • data management technology
  • process management technology


Part 4 (8 Instructions): Architectures Design and Analysis

  • the aspects from which architecture can be described (data, process, function, communication, platform, organization) and their relationships
  • an architecture specification technique (UML)
  • a practical architecture design approach (method)

In modern businesses, the extraction of information from the data in the information systems has become a competitive advantage. The availability of large amounts of data of different modalities (i.e. big data) often requires the use of advanced analytics, computational models and intelligent methods for discovering the relevant information that a business needs. The theories, methods and the architectures for achieving this goal is termed business intelligence. In this course, students are first introduced to the principles of business intelligence. Afterwards, advanced data analysis methods by using computational intelligence techniques are discussed to deal with business problems. Since significant uncertainty will be present in a business environment, non-probabilistic uncertainty modeling is considered, with a focus on fuzzy systems. Many models must be adaptive to the data and the underlying processes that generate the data. Adaptive and learning models based on neural networks are discussed for exploiting the past information in order to improve the design of information systems for business processes by providing them with knowledge about past business experience and, consequently, to improve their decision support capability. Finally, (multi-objective) optimization of operational processes through nature-inspired meta-heuristics (evolutionary computation techniques such as genetic/memetic algorithms, particle swarm optimization, ant-colony optimization) is considered.


Business Process Management (BPM) is a radical shift from traditional management thinking. Instead of taking an organization's functional decomposition as a starting point to manage the efficiency and effectiveness of operations, BPM focuses on the business processes that cut right through different departments (or even organizations). A business process can be seen as a structured set of activities designed to produce a specified output for a particular customer. As such, an effective and efficient business process is a particularly valuable asset for companies to attract and please its customers. Nowadays, Information Technology must be considered as the key enabler of high-performing business processes.

Managing business processes is an inherently cyclic endeavor, which passes through different phases. The course on Business Process Management considers each of the following phases:

  • Identification: the problem to distinguish which processes in organizations require priority to be managed.
  • Discovery: the elicitation and specification of the way that operational processes are carried out.
  • Diagnosis: the understanding of a process structural ability to fulfill the requirements it must meet
  • Design: the planned action to increase the performance and/or conformance of business processes by changing its elements
  • Execution: the execution of business processes using advanced IT, such as workflow management systems
  • Control: the day-to-day monitoring of a business process to detect operational problems and violations of regulations
  • The course offers students various methods, techniques and tools to carry out these phases, which include: process modeling with high-level Petri nets, process mining, process simulation, redesign best practices, product-based design theory, the use of reference models, industrial modeling tools, and the BPM maturity framework.

This course focuses on enterprise information systems that are driven by models, i.e., instead of constructing code these systems are assembled, configured or generated using a model-driven approach. Of particular interest are so-called "process-aware" information systems. Typical examples are workflow management systems and the process engines of ERP, CRM, PDM and other enterprise information systems. Starting point for the course are the process modeling techniques taught in the Bachelor phase. In particular it is assumed that the students are able to model in terms of (high-level) Petri nets and are able to make object models. Reading materials to refresh main concepts will be provided. 

The first part of the course focuses on the modeling and implementation of workflows. Different languages and systems are presented. Using the so-called workflow patterns students need to compare and evaluate languages and systems. Moreover, students need to model and implement non-trivial workflows in a specific workflow management system (e.g., Bizagi and YAWL). It should be noted that although the focus is on pure workflow management systems, the knowledge and experience will also applicable to other process-aware information systems. 

The second part of the course focuses on the analysis of workflows using Petri net theory. One of the topics is workflow verification, i.e., How to automatically identify design errors and correct them? Here different tools are being used and, among others, the SAP reference model and its errors are used as examples. This requires an introduction to concepts such as WF-nets, various soundness notions, free-choice nets, reduction rules, etc.

The final part of the course considers how process-aware information systems interact with their “environment” of processes, systems, and applications. The topics include modeling and enacting service-oriented and data-driven process interactions in complex scenarios, and ensuring consistent process execution and data across different processes and applications in case of failures.


Organizations are constantly trying to improve the way their businesses perform. To this end, managers have to take decisions on changes to the operational processes. However, these changes are never without consequences and often high costs are involved. Therefore, it is of the utmost importance that these decisions are supported by a thorough analysis of all possible consequences on the organization.

To gain insights into the consequences of decisions on an operational process, one often resorts to simulation studies. In these studies, simulation models are made of the operational process under consideration, taking into account the necessary elements, such as costs, resources and activities. These simulation models are then executed with different parameters, to gain insights into the consequences of different decisions on the basis of which a final decision is made. 

It is clear that the construction of simulation models of an operational process is a far from trivial task. Deciding which elements of the operational process to take into account and which not is key to getting useful simulation results.

In this course, we use a discrete event simulation tool called Arena to execute simulations. This tool allows for the graphical definition of a simulation model, together with complex definitions of queue types, resource availability and so on.


We study models of contemporary data intensive systems and their practical use. These models are among: Graph databases, Data warehousing and online analytical processing (ROLAP, MOLAP, etc.), Document databases (NoSQL, JSON stores, etc.), Parallel and distributed data processing (MapReduce, etc.), and Deductive databases (Datalog).

We discuss why these models were introduced, their relative advantages and disadvantages, how to use them in practice, and, at a high level, how they are implemented. Unlike the subject Database Technology (2ID35) which focuses primarily on systems internals and their efficient implementation at a lower level, the primary goal of this subject is to develop the practical ability to engineer non-trivial data intensive applications based on a solid understanding of the underlying engineering principles. Towards this goal, hands-on practical assignment(s) using contemporary frameworks and technologies are a central component of the course.


The E-Business course addresses the architecture (structure) and systems (technology) for support of e-business. Business and organization aspects are also discussed. The focus is on inter-organizational business operation in which information and communication technology is an enabling factor. E-commerce, most significantly business-to-business e-commerce, is an important category of e-business in this respect. The first part of this course follows the BOAT framework to place e-business elements in perspective, distinguishing the business, organizational, architecture and technology aspects. These can be placed in a demand pull or technology push context. At the business and organization aspects, attention is paid to e-business paradigms like dynamic service outsourcing and highly dynamic supply chains. A pivotal point in this course is the architecture aspect of the framework, covering the mapping of organization architectures to information system and technology class architectures. Attention is paid to architecture and technology standards in the e-business field. Important technology classes are placed into perspective and essential characteristics are discussed. Emphasis is on the analysis of complex e-business structures. This is also reflected in the group assignment to be completed by the students enrolled in this course.

Recent developments will also be addressed, e.g., cloud computing, Service Oriented Architecture (SOA), Service Level Agreement (SLA), and interoperability.


The engineering history has taught us well that it is essential to have a clear understanding of the problem and the requirements of the solution system in order to successfully design and implement the right solution. This course focuses on the methods and practices that are used to identify the needs and to define information systems & technology (IST) solutions that will maximize the value delivered by an organization to its stakeholders. It provides an understanding of the organizational and managerial issues related to the elicitation, analysis, specification, verification, validation and management of IST requirements. It discusses methods and practices that are applied not only for the development of innovative commercial software systems but also for the implementation of enterprise information systems. These systems support functions and business processes in various domains, including healthcare, logistics, financial services, and other industries particularly in the service environment. 

The student groups will develop and verify requirements of a software (information) system or a specific part of it. This will significantly help in reinforcing the theoretical concepts and methods discussed during the lectures and in understanding the requirements engineering problems that can be encountered in the business environment.



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Oktay Turetken is an Associate Professor in the Department of Industrial Engineering & Innovation Sciences at Eindhoven University of Technology (TU/e). His research interest centers around the topic of ‘business model engineering’ in an information systems/business process management (BPM) context, with special focus on the operational design and implementation of service-dominant business models using BPM methods and tools, and the organizational capabilities related to the strategic, people and cultural factors (i.e. soft factors of BPM) that influence an organization’s ability to redesign and manage business and process models. Current research projects focus on Business Model Engineering, Maturity Models in Business Process Management, Digital Transformation/Innovation.