A bioprocess is a series of biological, chemical, and physical operations used to produce a product using living cells or their components. Bioprocesses are often used for the production of monoclonal antibodies (mAbs). The first step of the mAb production bioprocess is to take a vial containing a small amount of the selected cell line and grow those cells until they are of sufficient quantity. This step is known as the seed train in bioprocess development. During the seed train phase, it is essential to monitor the stability of the cells and their growth due to challenges such as variations in cell behaviour, batch-to-batch differences, and potential changes in cultivation conditions. In this paper, we present a case study where process mining is used to analyse the stability of cell lines during the seed train phase at a large pharmaceutical company in Australia. In order to do so, first it was necessary to transform the collected seed train data into an event log. Next, process models were discovered for high- and low-growth seed trains. We then derived insights into the performance of the seed train growth rate whereby characteristics of cell cultures in early stages can be associated with growth rate performance in later stages. Finally, we showed how the discovered models can be used to predict the growth performance of new seed trains.

Automated process discovery techniques allow us to generate a process model from an event log consisting of a collection of business process execution traces. The quality of process models generated by these techniques can be assessed with respect to several criteria, includingfitness, which captures the degree to which the generated process model is able to recognize the traces in the event log, andprecision, which captures the extent to which the behavior allowed by the process model is observed in the event log. A range of fitness and precision measures have been proposed in the literature. However, existing measures in this field do not fulfil basic monotonicity properties and/or they suffer from scalability issues when applied to models discovered from real-life event logs. This article presents a family of fitness and precision measures based on the idea of comparing the$k$th order Markovian abstraction of a process model against that of an event log. The article shows that this family of measures fulfils the aforementioned properties for suitably chosen values of$k$. An empirical evaluation shows that representative exemplars of this family of measures yield intuitive results on a synthetic dataset of model-log pairs, while outperforming existing measures of fitness and precision in terms of execution times on real-life event logs.

Given a model of the expected behavior of a business process and given an event log recording its observed behavior, the problem of business process conformance checking is that of identifying and describing the differences between the process model and the event log. A desirable feature of a conformance checking technique is that it should identify a minimal yet complete set of differences. Existing conformance checking techniques that fulfill this property exhibit limited scalability when confronted to large and complex process models and event logs. One reason for this limitation is that existing techniques compare each execution trace in the log against the process model separately, without reusing computations made for one trace when processing subsequent traces. Yet, the execution traces of a business process typically share common fragments (e.g. prefixes and suffixes). A second reason is that these techniques do not integrate mechanisms to tackle the combinatorial state explosion inherent to process models with high levels of concurrency. This paper presents two techniques that address these sources of inefficiency. The first technique starts by transforming the process model and the event log into two automata. These automata are then compared based on a synchronized product, which is computed using an A* heuristic with an admissible heuristic function, thus guaranteeing that the resulting synchronized product captures all differences and is minimal in size. The synchronized product is then used to extract optimal (minimal-length) alignments between each trace of the log and the closest corresponding trace of the model. By representing the event log as a single automaton, this technique allows computations for shared prefixes and suffixes to be made only once. The second technique decomposes the process model into a set of automata, known as S-components, such that the product of these automata is equal to the automaton of the whole process model. A product automaton is computed for each S-component separately. The resulting product automata are then recomposed into a single product automaton capturing all the differences between the process model and the event log, but without minimality guarantees. An empirical evaluation using 40 real-life event logs shows that, used in tandem, the proposed techniques outperform state-of-the-art baselines in terms of execution times in a vast majority of cases, with improvements ranging from several-fold to one order of magnitude. Moreover, the decomposition-based technique leads to optimal trace alignments for the vast majority of datasets and close to optimal alignments for the remaining ones.

Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of approximate methods have been proposed, including several landmark-based methods that have been shown to scale up to very large graphs with acceptable accuracy. This paper presents two improvements to existing landmark-based shortest path estimation methods. The first improvement relates to the use of shortest-path trees (SPTs). Together with appropriate short-cutting heuristics, the use of SPTs allows to achieve higher accuracy with acceptable time and memory overhead. Furthermore, SPTs can be maintained incrementally under edge insertions and deletions, which allows for a fully-dynamic algorithm. The second improvement is a new landmark selection strategy that seeks to maximize the coverage of all shortest paths by the selected landmarks. The improved method is evaluated on the DBLP, Orkut, Twitter and Skype social networks.