Determine the dependency graph (3 points) Determine the log relations (follows, causality, parallelism, no-relation) and the footprint matrix

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Determine the dependency graph (3 points) Determine the log relations (follows, causality, parallelism, no-relation) and the footprint matrix

Part A: Consider the following event log: Case ID Events 1 A B G C D H I 2 A B C E F G H I 3 A C B G E F H J 4 A B C G D H J 5 A C B E F H G I 6 A C D H B G I 7 A B G C E F J 8 A C E F B G H J Determine the dependency graph (3 points) Determine the log relations (follows, causality, parallelism, no-relation) and the footprint matrix (3 points) Show step by step how the alpha algorithm works on this log, and draw the resulting process model (Tip: you can use process mining software to check your work) (6 points)
Part B: Attached is a real-life log, taken from a Dutch Financial Institute that has automated the process using the workflow management system. The process represented in the event log is an application process for a personal loan or overdraft within a global financing organization. The amount requested by the customer is indicated in the case attribute AMOUNT_REQ, which is global, i.e. every case contains this attribute. The event log is a merger of three intertwined sub processes. The first letter of each task name identifies from which sub process (source) it originated from (A = Applications, O = Offers, W = manual work items). Feel free to run analyses on the process as a whole, on selections of the whole process and/or the individual sub processes. The company is particularly interested in answers to the following questions: What does the process model look like? Which resources generate the highest activation rate of applications? What are the throughput times per part of the process, in particular the difference between the time spent in the company’s systems waiting for processing by a user and the time spent waiting on input from the applicant as this is currently unclear, What is the influence on the frequency of incompleteness to the final outcome. The hypothesis here is that if applicants are confronted with more requests for completion, they are more likely to not accept the final offer, How many customers ask for more than one offer (where it matters if these offers are asked for in a single conversation or in multiple conversations)? How does the conversion compare between applicants for whom a single offer is made and applicants for whom multiple offers are made? Any other interesting trends, dependencies etc. For these exercise, you can use any of the methods and techniques and software tools described in chapter 11 or in the tutorials/videos found on Brightspace. You should illustrate your results using screenshots from your process mining tool. You should expect to write approx. 6 to 10 pages or more. However, your submission will be evaluated based on interesting and/or actionable insights and findings provided. (28 points)