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Courses

2IHI10/2IIC0 - Business information systems

Process-aware information systems (e.g., workflow management systems, ERP systems, CRM systems, PDM systems) are generic information systems that are configured on the basis of process models. In some systems the process models are explicit and can be adapted (e.g., the control-flow in a workflow system) while in other systems they are implicit (e.g., the reference models in the context of SAP). Some systems implement monolithic processes in isolation while in other systems various (web-)services are composed to complex processes. In some systems they are hard-coded and in other systems truly configurable. Regardless of the implementation, it is clear that in any enterprise, business processes and information systems are strongly intertwined. Therefore, it is important that students understand the relationship between systems and processes and are able to model complex systems involving processes, (web-)services, humans, and organizations.

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2IIE0 - Business process intelligence

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.

The course covers the three main types of process mining. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the a-algorithm that takes an event log and produces a Petri net explaining the behavior recorded in the log. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between “business” and “IT”. Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.

The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.

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2IMI00 - Seminar architecture of information systems

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, for decision making, it is of the utmost importance to have a thorough understanding of the operational processes as they take place in the organization.

Typically, such operational processes are supported by information systems that record events as they take place in real life in so-called event logs. Process Mining techniques allow for extracting information from event logs. For example, the audit trails of a workflow management system or the transaction logs of an enterprise resource planning system can be used to discover models describing processes, organizations, and products. Moreover, it is possible to use process mining to monitor deviations (e.g., comparing the observed events with predefined models or business rules in the context of SOX).

Process mining is closely related to BAM (Business Activity Monitoring), BOM (Business Operations Management), BPI (Business Process Intelligence), and data/workflow mining. Unlike classical data mining techniques the focus is on processes and questions that transcend the simple performance-related queries supported by tools such as Business Objects, Cognos BI, and Hyperion.

In this seminar, students are introduced to the research being conducted in the Architecture of Information Systems group of this university. Specifically, this seminar focuses on the state-of-the-art research on Process Mining. To emphasize that Process Mining is not only a research area, but has gained increasing interest from Industry, practical application will be considered.

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2IMI05 - Capita selecta architecture of information systems

People interested in the 'process side' of information systems can take the course 'Capita selecta architecture of information systems'. This course will be organized in an ad-hoc manner taking into account the interests of the student. The focus will always be on a particular 'hot topic' in the information systems domain. The course can, in principle, be taken at any point in time. Send an e-mail to wsinfsys@tue.nl describing your interest and the list of courses you already took (with marks).

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2IMI10 - Business process management systems

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. The mark will be based on an assignment (3 points) and a written exam (7 points).

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2IMI15 - Metamodeling and interoperability

Independently developed applications based on different models and implemented on different platforms need to use each others services and share each others data. Interoperability is therefore one of the buzz-words of the last years in Computer Science. Web services-driven Service-Oriented Architectures (SOA) have arisen as a solution to the interoperability problem. In this context, metamodeling became more important than ever, since the key to successful integration of inter-organizational enterprise information systems and interoperability lies in the intelligent use and management of metadata and metaprocesses. In this course we will consider and compare a number of industrial and academic modeling and metamodeling frameworks (such as UML, BPMN, WS BPEL, EPC, Petri nets, Yawl, temporal logics) and their place within the SOA approach and learn to develop data and process (meta-) models for services. We will also study a number of analysis techniques to check the compatibility of (communicating to each other) services.

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2IMI20 - Advanced process mining

Process mining provides a new means to improve processes in a variety of application domains. There are two main drivers for this new technology. On the one hand, more and more events are being recorded thus providing detailed information about the history of processes. On the other hand, in most organizations there is a need to improve process performance (e.g., reduce costs and flow time) and compliance (e.g., avoid deviations or risks). This advanced course on process mining teaches students the theoretical foundations of process mining and exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension.

The course will cover various advanced process discovery techniques, i.e., techniques based on region theory and genetic algorithms. One needs to be able to understand such techniques, apply them, and know their strengths and weaknesses.

The course will also cover conformance checking techniques covering all four conformance dimensions: replay fitness, precision, generalization, and simplicity. A key element is the notion of alignments linking observed to modeled behavior.

Process mining techniques will not be limited to control-flow and will also include other perspectives such as time (bottleneck analysis), resources (social network analysis), and data (decision mining).

Besides learning theoretical concepts, students will be exposed to event data from a variety of domains, including hospitals, insurance companies, governments, high-tech systems, etc. The assignment will either focus on the analysis of such data sets or on focusing on a particular process mining problem.

Note that the bachelor course Business Process Intelligence (2IIE0) introduces process mining at an introductory level. This course is not required as prior knowledge, i.e., 2IMI20 does not depend on 2IIE0. However, students can benefit from 2IIE0 to already have an initial understanding of process mining and being familiar with some of the simpler process mining algorithms.

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2IMI25 - Constraint programming

Constraint Programming (CP) is a widely used paradigm to model and solve combinatorial optimization problems. Being able to model and solve combinatorial optimization problems is a skill that is broadly valued, by big corporations like Google, IBM, SAP, Oracle, Microsoft, but also by a large number of smaller companies and start-ups. CP is widely used both in consulting, where an optimization consultant solves a specific problem for one customer, and in software development where it is used in products that cater to a broad set of customers. Being able to model and solve problems through CP is a very valuable addition to being able to model and solve problems by way of Mathematical Programming. As such combining this course with course 2WO09 - Modelleren van LP-problemen, gives students a great background in modeling and solving combinatorial optimization problems. The course will start by presenting the foundations of Constraint Programming in detail. Students will learn about variables, constraints, domains, constraint propagation, arc-consistency, global propagation and much more. They will then get hands-on experience by modeling a variety of CP problems. The tool used for that is IBM ILOG CPLEX Optimization Studio (COS) together with the OPL modeling language. As such students will learn how to use the COS IDE and the OPL language. The second part of the course will pay attention to Constraint-Based Scheduling, which is the discipline that studies how to solve scheduling problems by using CP. Scheduling has been a main area where CP has been successful, and CP is a leading technology to solve scheduling problems. The course will pay extensive attention to modeling and solving real-life scheduling problems, with special attention to the way Constraint-Based Scheduling is used in leading Advanced Planning and Scheduling products. The third part of the course is dedicated to studying how CP problems can be solved. Topics like tree search, local search, and large neighborhood search are discussed and detailed examples are given. We will also go into the relationship between Constraint Programming and Mathematical Programming and discuss in what way scientific and industrial experience shows that many of the most challenging combinatorial problems are best tackled by using a combination of techniques, where Mathematical Programming, Constraint Programming, tree search, and neighborhood search are extensively used.

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2IMI30 - Business process simulation

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.

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2IMI35 - Introduction to process mining

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as “data science in action”.

The course covers the three main types of process mining. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the a-algorithm that takes an event log and produces a Petri net explaining the behavior recorded in the log. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between “business” and “IT”. Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the data science field.

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2IOC0 - DBL Information systems

Information systems are the central software of today’s information driven economy. Their design and improvement is vital to the success of an organization like a company or a public administration. Modern information system are typically “process aware” meaning that the order in which information is processed or shown to a user is expressed in models. However, the actual challenge for the success of such a system lies in two other areas: it has to serve its users the necessary information in the right form to achieve a particular task, and it has to access and show this information within a technical environment that one cannot influence. In this course, we focus on the aspect of designing information retrieval, information processing, and information visualization within a given technical environment and user context. We use techniques from “design thinking” to analyze limitations in existing information sources (databases, web-services, etc.), to understand user interests for data in these data sources, and to design a processes and an information system to overcome these limitations. System implementation requires integrating an existing system with a database and optionally a web-based frontend through programming

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JBG010 Perspectives on Data Science

This course familiarizes students from a multidisciplinary perspective with the characteristics of data, the use and visualization of data as well as the role of computing and analytics in data science developments.

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JBG030 - Data Challenge 1

Data Challenge 1 is part of a four course series in the JADS Bachelor Data Science program offered by TU Eindhoven and University of Tilburg.

The objective of the Data Challenge courses is to teach students how to perform large-scale data-driven analyses themselves, combining the technical skills acquired earlier in the Data Science program with insights gained in methodological courses. Data Challenge 1 is the first course in this series and shall familiarize students with the skills of conducting/executing a large scale analysis on their own. The focus is on learning how to acquire and use tools and libraries for doing data collection, data enrichment, and data analysis in an independent, reproducible manner.

In the first Data Challenge 1, students will get the possibility to apply the methods and techniques acquired during the first year of the program on a large, complex dataset. The students will be given a large, structured dataset, several specific analysis questions about this dataset, and a proposed analysis approach for each question (i.e., particular analysis techniques to apply). The task for the students is to technically realize these analyses by identifying and familiarizing themselves with the right software tools for this analysis, implementing the analysis in a repeatable form, and reflecting on the validity of their results and the suitability of their analysis approach. An important element in this course will be the actual handling of large data being stored in various formats (files, relational databases, object databases, etc.), the pre-processing of data to be usable for the analysis, and the storage of analysis results in a suitable data format.

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Previous years

Courses 2015 - 2016

Code Old codes Name Quartile Oase OwInfo
2IHI10 2IIC7 Business information systems A1 B3 Oase OwInfo
2IIC0 Business information systems A1 B3 Oase OwInfo
2IIE0 2IIF0 Business process intelligence B3 Oase OwInfo
2IMC92 2IM33 Kick-off meeting master BIS A1 Oase OwInfo
2IMC93 Kick-off meeting master EIT data science A1 Oase OwInfo
2IMI00 Seminar architecture of information systems A2 Oase OwInfo
2IMI05 2II99 Capita selecta architecture of information systems Oase OwInfo
2IMI10 2II55 Business process management systems B3 Oase OwInfo
2IMI15 2II65 Metamodeling and interoperability A1 Oase OwInfo
2IMI20 2II66 Advanced process mining B4 Oase OwInfo
2IMI25 Constraint programming A1 Oase OwInfo
2IMI30 2II75 Business process simulation B3 Oase OwInfo
2IMI35 Introduction to process mining A1 Oase OwInfo
2IOC0 DBL information systems B4 Oase OwInfo