By Wil M. P. van der Aalst
This is the second one variation of Wil van der Aalst’s seminal booklet on procedure mining, which now discusses the sphere additionally within the broader context of knowledge technology and massive info ways. It comprises a number of additions and updates, e.g. on inductive mining strategies, the idea of alignments, a significantly increased part on software program instruments and a totally new bankruptcy of method mining within the huge. it really is self-contained, whereas while masking the complete process-mining spectrum from strategy discovery to predictive analytics.
After a normal advent to info technology and method mining partially I, half II presents the fundamentals of commercial technique modeling and knowledge mining essential to comprehend the rest of the ebook. subsequent, half III makes a speciality of strategy discovery because the most crucial procedure mining activity, whereas half IV strikes past learning the keep watch over movement of methods, highlighting conformance checking, and organizational and time views. half V deals a advisor to effectively using strategy mining in perform, together with an creation to the generally used open-source software promenade and a number of other advertisement items. finally, half VI takes a step again, reflecting at the fabric provided and the major open demanding situations.
Overall, this booklet offers a accomplished review of the state-of-the-art in method mining. it really is meant for company strategy analysts, enterprise experts, strategy managers, graduate scholars, and BPM researchers.
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Extra resources for Process Mining: Data Science in Action
Consider questions like: • What are the most frequent paths in my process? Do they change over time? • What do the cases that take longer than 3 months have in common? Where are the bottlenecks causing these delays? • Which cases deviate from the reference process? Do these deviations also cause delays? Obviously, these questions cannot be answered using spreadsheets because the process perspective is completely absent in spreadsheets. Processes cannot be captured in numerical data and operations like summation.
This model only allows for two traces and these are exactly the ones in the small event log. b and d are modeled as being concurrent because they can be executed in any order. For larger and more complex models it is important to discover concurrency. 1 The α-algorithm is just one of many possible process discovery algorithms. , logs containing 1 See, for example, Figs. 10 to understand why we use the term “Spaghetti” to refer to models that are difficult to comprehend. 3 Another event log: cases 7, 8, and 10 are not possible according to Fig.
The behavior of a process or system is modeled using random variables in order to allow for analysis. Well-known approaches include Markov models, queueing networks/systems, and simulation. These can be used to analyze waiting times, reliability, utilization, etc. in the context stochastic processes. 16 1 Data Science in Action Fig. , cheapest or fastest) from a large or even infinite set of alternatives. Consider, for example, the following question: Given a list of cities and the distances between each pair of cities, what is a shortest possible route that visits each city exactly once and returns to the origin city?