Computing and Information Systems - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 11
-
ItemDetection and removal of infrequent behavior from event streams of business processes(Elsevier Ltd, 2020-05-01)Process mining aims at gaining insights into business processes by analyzing the event data that is generated and recorded during process execution. The vast majority of existing process mining techniques works offline, i.e. using static, historical data, stored in event logs. Recently, the notion of online process mining has emerged, in which techniques are applied on live event streams, i.e. as the process executions unfold. Analyzing event streams allows us to gain instant insights into business processes. However, most online process mining techniques assume the input stream to be completely free of noise and other anomalous behavior. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out infrequent behavior from live event streams. Our experiments show that we are able to effectively filter out events from the input stream and, as such, improve online process mining results.
-
ItemScalable alignment of process models and event logs: An approach based on automata and S-components(PERGAMON-ELSEVIER SCIENCE LTD, 2020-12)
-
ItemProcess querying in apromore(CEUR Workshop Proceedings, 2015-01-01)Process querying addresses the problem of automatically retrieving process models from collections thereof on the basis of user-defined queries. Process querying can be used to tackle problems of process compliance, reuse, redesign, and standardization [1].In this paper, we demonstrate a process querying environment that resulted from integrating Process Query Language (PQL) [2] into the Apromore process model reposi-tory [3]. PQL is a programming language based upon temporal logic with an intuitive SQL-like syntax for the specification of queries. The semantics of PQL queries is grounded in process model behavior. The intent of a PQL query is to retrieve process models from a collection of models based on the arrangements of activities and/or events in the process instances that these models describe. A screen cast that demonstrates the environment is available at https://youtu.be/S_U6frTWd3M. In the remainder of this paper, we provide an overview of PQL and its implementation,present the Apromore process model repository, discuss the integration of PQL into Apromore, and demonstrate the use of PQL in Apromore using a typical process querying scenario in the context of a process model collection taken from industry
-
ItemAutomated Discovery of Process Models from Event Logs: Review and Benchmark(Institute of Electrical and Electronics Engineers, 2019-04-01)Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
-
ItemSplit Miner: Automated Discovery of Accurate and Simple Business Process Models from Event Logs(Springer Verlag, 2019-05)The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or over-generalize it (low precision). Striking a trade-off between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method, namely Split Miner, which produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while achieving considerably faster execution times than state-of-the-art methods, measured on a benchmark covering twelve real-life event logs. Split Miner combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph. Split Miner is also the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models
-
ItemSplit Miner: Automated Discovery of Accurate and Simple Business Process Models from Event Logs(Springer Verlag, 2019)The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of propos- als, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or over-generalize it (low precision). Striking a tradeoff between these quality di- mensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method, namely Split Miner, which produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while achieving considerably faster execution times than state- of-the-art methods, measured on a benchmark covering twelve real-life event logs. Split Miner combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict, and causal relations between neighbors in the directly-follows graph. Split Miner is also the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models.
-
ItemThe 4C spectrum of fundamental behavioral relations for concurrent systems(Springer International Publishing, 2014-01-01)The design of concurrent software systems, in particular process-aware information systems, involves behavioral modeling at various stages. Recently, approaches to behavioral analysis of such systems have been based on declarative abstractions defined as sets of behavioral relations. However, these relations are typically defined in an ad-hoc manner. In this paper, we address the lack of a systematic exploration of the fundamental relations that can be used to capture the behavior of concurrent systems, i.e., co-occurrence, conflict, causality, and concurrency. Besides the definition of the spectrum of behavioral relations, which we refer to as the 4C spectrum, we also show that our relations give rise to implication lattices. We further provide operationalizations of the proposed relations, starting by proposing techniques for computing relations in unlabeled systems, which are then lifted to become applicable in the context of labeled systems, i.e., systems in which state transitions have semantic annotations. Finally, we report on experimental results on efficiency of the proposed computations.
-
ItemTimestamp Repair for Business Process Event Logs( 2018-04-05)This paper contributes an approach for automatically correcting timestamp errors in business process execution logs. These errors are quite common in practice due to the logging granularity or the performance load of the logging system. Analyzing logs that have not been properly screened for such problems is likely to lead to wrong or misleading process insights. The proposed approach revolves around two techniques: one to reorder events with erroneous timestamps, the other to assign an estimated timestamp to each such event. The approach has been implemented in a software tool and extensively evaluated in different settings, using both synthetic and real-life logs. The experiments show that the approach significantly reduces the amount of incorrect timestamps, while the reordering of events scales well to large and complex datasets. The evaluation is complemented by a case study in the meat & livestock domain showing the usefulness of the approach in practice.
-
ItemAbstract and Compare: A Framework for Defining Precision Measures for Automated Process Discovery(SpringerLink, 2018-03-19)Automated process discovery techniques allow us to extract business process models from event logs. The quality of process models discovered by these techniques can be assessed with respect to various quality criteria related to simplicity and accuracy. One of these criteria, namely precision, captures the extent to which the behavior allowed by a discovered process model is observed in the log. While numerous measures of precision have been proposed in the literature, a recent study has shown that none of them fulfils a set of five axioms that capture intuitive properties behind the concept of precision. In addition, several existing precision measures suffer from scalability issues when applied to models discovered from real-life event logs. This paper presents a versatile framework for defining precision measures based on behavior abstractions. The key idea is that a precision measure can be defined by three ingredients: a function that abstracts a process model (e.g. as a transition system), a function that does the same for an event log, and a function that compares the behavior abstraction of the model with that of the log. We show empirically that different instances of this framework allow us to strike different tradeoffs between scalability and sensitivity. We also show that two instances of the framework based on lossless abstraction functions yield a precision measure that fulfils all the above-mentioned axioms.
-
ItemFiltering Spurious Events from Event Streams of Business Processes(Springer, 2018-06-04)Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions un- fold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviour. Hence, applying these techniques on real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to effectively filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.