Multiple methods for modelling and analyzing information systems in their application contexts. Systematic techniques for representing and analyzing process and data, use cases and scenarios, stakeholder goals and relationships. Requirements exploration and definition. Operational and strategic perspectives.
The purpose of this course is to provide an introduction to databases by analyzing their structure, content and measurement and by applying principles governing data modeling, database design and production with an emphasis on modeling, design and representation of content, decisions and tradeoffs involved in modeling, design and creation, and issues of standardization, security and emerging trends.
Business processes are pervasive in our lives: in banks, telecommunication centers, webservices, and healthcare. Processes in organizations are there to make sure that the business goals are achieved in an efficient way with the highest quality of products and/or services. The field of Business Process Management (BPM) focuses on improving an organization’s performance by managing, analyzing and improving its processes.
The first part of the course comprises basic concepts of Business Process Management. We shall learn the BPM lifecycle: (Re)Design, Modeling, Executing, Monitoring and Optimizing business processes. Moreover, we shall cover the methodological aspects of BPM such as modeling languages, model discovery, qualitative and quantitative analysis of processes models.
In the second part of the course, the focus shifts to a Data Science methodology for BPM, namely Process Mining. The students will learn the three basic steps of Process Mining: discovery of models from data, conformance analysis of the resulting models with data, and performance analytics. The emphasis of the Process Mining part will be on performance analytics.
The course will cover state-of-the-art literature, and as part of the final grade will require the students to present real business case studies on applications of BPMM in industry.
Machine learning has recently become the dominant field in AI research and constitutes the main part of the tools applied in industry-based AI positions. Business analysts, data scientists and AI engineers are required to know machine learning at different levels. The course will give a broad high-level overview of state-of-the-art machine learning methodologies. We shall focus on the application of these techniques to real-world data using the most advanced tools available for Python. The techniques will include: linear regression, basic techniques for classification, advanced regression and classification methods, and unsupervised learning.
Integrated System Design is a capstone course that integrates the various perspectives of an integrated system taught in third year, including: Optimization, Quality, Management, Information, and Economics. The course approaches systems design from a Business Process perspective. Beginning with the Business Processes, it explores the concept of Business Process Re-engineering. It extends the concept of business processes to incorporate perspectives such as cost, quality, time, behaviour, etc. The second part of the course focuses on business process design tools. Namely, software tools to both design, simulate and analyse business processes. The third part of the course explores the application of process design to various domains. Guest speakers are used to provide domain background.
Title: "Capacity Planning and Scheduling in Cloud Services"
Projected completion: October 2021. Co-supervised with J. Christopher Beck.
Title: "Cutting Customer Journeys for Process Mining"
Projected start: October 2020. Co-supervised with Periklis Andritsos.
Title: On "Towards Higher Maturity for Machine Learning: A Conceptual Modelling Approach"
Completion date: TBD. Second reader.
Title: "Modeling and Learning Workload Models in Cloud Applications"
Completion date: August 2020. Co-supervised with J. Christopher Beck.
Title: "Solving Petri net Reachability Problems with Heuristic Search"
Projected completion: December 2018. Co-supervised with J. Christopher Beck.
Title: "Fusion-Based Process Discovery"
Completion date: August 2018. Co-supervised with Avigdor Gal.
