Process modeling and bottleneck mining in online peer-review systems
© Premchaiswadi and Porouhan. 2015
Received: 16 January 2015
Accepted: 27 July 2015
Published: 22 August 2015
This paper is divided into three main parts. In the first part of the study, we captured, collected and formatted an event log describing the handling of reviews for proceedings of an international conference in Thailand. In the second part, we used several process mining techniques in order to discover process models, social, organizational, and hierarchical structures from the proceeding’s event log. In the third part, we detected the deviations and bottlenecks of the peer review process by comparing the observed events (i.e., authentic dataset) with a pre-defined model (i.e., master map). Finally, we investigated the performance information as well as the total waiting time in order to improve the effectiveness and efficiency of the online submission and peer review system for the prospective conferences and seminars. Consequently, the main goals of the study were as follows: (1) to convert the collected event log into the appropriate format supported by process mining analysis tools, (2) to discover process models and to construct social networks based on the collected event log, and (3) to find deviations, discrepancies and bottlenecks between the collected event log and the master pre-defined model. The results showed that although each paper was initially sent to three different reviewers; it was not always possible to make a decision after the first round of reviewing; therefore, additional reviewers were invited. In total, all the accepted and rejected manuscripts were reviewed by an average of 3.9 and 3.2 expert reviewers, respectively. Moreover, obvious violations of the rules and regulations relating to careless or inappropriate peer review of a manuscript—committed by the editorial board and other staff—were identified. Nine blocks of activity in the authentic dataset were not completely compatible with the activities defined in the master model. Also, five of the activity traces were not correctly enabled, and seven activities were missed within the online submission system. On the other hand, dealing with the feedback (comments) received from the first and the third reviewers; the conference committee members and the organizers did not attend to those feedback/comments in a timely manner.
KeywordsOnline peer review system Process discovery Conformance checking Performance analysis Bottleneck mining Social network analysis LTL checker Petri nets
Currently, online and blind peer review of manuscripts submitted by authors is an important part of the publication process. However, a peer review does not necessarily include the assessment of a work by one or more reviewers who are experts in that field of study only. It is a process that includes selection and invitation of expert reviewers, allocation of deadline for reviewers, collection of comments from reviewers, discussion of manuscripts in detail, analysis of reviewers’ comments by an editorial board, invitation of more reviewers, a decision to accept or reject the manuscript, notification to authors about decisions, and so on. Typically, the most difficult part of a peer review process is selecting appropriate reviewers. The invited reviewers may be too busy with other commitments and obligations and are not able to participate in any manuscript revision tasks. On the other hand, some reviewers may initially show interest to take part in a peer review process but later refuse to provide any feedback or comments concerning the assigned manuscript. The main objective of the study was to investigate and scrutinize the peer review process of an international conference in a private university in Thailand. To do this, we initially developed an online reviewing system that: (1) could automatically store and acknowledge the online submissions, (2) could assign independent reviewers for the submitted manuscripts, (3) could easily show the status of the submission during the review, (4) could indicate whether a reviewer has participated in the reviewing process or not, (5) could automatically collect feedbacks from invited reviewers, (6) could send the received comments and feedback to the editorial board for making a decision, (7) could invite more reviewers and repeat the same steps if necessary, (8) could accept or reject a manuscript based on the final decisions made by the editorial board, and (9) could record and extract all of the above mentioned steps in terms of datasets.
In this study, we aimed to apply two classes of process mining techniques (i.e., Discovery and Conformance Analysis) in order to discover models, organizational structures, and bottlenecks related to the handling of proceedings’ peer reviews in an international conference in Thailand. Knowing that process mining analysis tools receive the input logs only in MXML (Mining eXtensible Markup Language) and XES (eXtensible Event Stream) formats, we initially converted the collected event log into a MXML-formatted log. Later, Alpha (α) Algorithm, Heuristic, Fuzzy and Social Network mining techniques (from Discovery class) were used in order to automatically construct the proceedings’ review models based on the authentic data and without having any priori model. Though the Heuristic Miner looked much similar to the Alpha algorithm, the technique had the privilege to better deal with XOR and AND connectors based on the dependency relations of the event log. Next, the actual process behavior was projected onto fuzzy models. The result was an animation movie which helped us to better understand the real activities occurred (during the proceeding’ peer review process). Furthermore, by using Social Network Miner technique we aimed to analyze the organizational perspective of the peer review process in terms of three metrics, namely as: (a) Handover of Work, (b) Working Together, and (c) Similar Tasks (Aalst 2011).
Process mining techniques and terms used in this study
Discovers a Petri net using the α-algorithm
Discovers a C-net using heuristic mining
Discovers a fuzzy model using fuzzy mining
Social network miner
Creates a social network based on a selected criterion
Checks a property expressed in terms of LTL
Computes fitness of Petri net based on event log
The process of checking, comparing and connecting traces of events in an authentic log with a pre-defined master model
Analyzes the gap between a model and the real data to detect violations
Performance analysis with petri net
Uses replaying to retrieve various Key Performance Indicators
In general, one of the main benefits of the techniques used in this paper is that information is objectively compiled. To say simple, we gathered valuable information about what actually was happening according to the review process of the proceedings’ peer review process and existing bottlenecks, and not what we just thought or expected to see happening in the event log. Considering the results of the study, conference committee chairs and organizers can better evaluate the performance of the involving reviewers (as well as the staff) within the assigned tasks. This will improve the performance, efficiency and effectiveness of the handling of reviews for prospective academic conferences.
ProM and PromMimport framework
The idea of process mining is not new. Cook and Wolf (1996) conducted many research projects in the context of software engineering processes. Initially, they described three methods for process discovery: one using neural networks, one using a purely algorithmic approach, and one using a Markovian approach. The authors considered the latter two as the most promising approaches. The purely algorithmic approach built finite state machine where states were fused if their futures (i.e., in terms of possible behavior in the next k steps) were identical (Cook and Wolf 1996). The proposed Markovian approach used a mixture of algorithmic and statistical methods and was able to deal with noise in simple process maps. However, their results were limited to sequential behavior. Cook and Wolf (1998) extended their work to concurrent processes. They proposed specific metrics—such as entropy, event type counts, periodicity, and causality—and used these metrics to discover models within event streams. However, they did not provide an approach to generate explicit process models. Later, Cook and Wolf (1999) provided a measure to quantify discrepancies between a process model and the actual behavior as registered using event-based data.
The thought of applying process mining in the context of workflow management was first introduced in the sixth International Conference on Extending Database Technology held at Valencia, Spain (Agrawal et al. 1998). This work was based on the workflow graphs which were inspired by work flow products such as IBM MQSeries workflow (formerly known as Flowmark) and InConcert (Zhang 2010). In 2001, a tool based on these algorithms was made available (Maxeiner et al. 2001).
Schimm (2000, 2002) developed a mining tool suitable for discovering hierarchically structured workflow processes. This required all splits and joins to be balanced. Herbst and Karagiannis (1998, 1999) also addressed the issue of process mining inthe context of workflow management using an inductive approach. Although, their work was limited to sequential models; their proposed approach allowed for concurrency analysis. Herbst (2000) used stochastic task graphs as an intermediate representation using a workflow model described in the ADONIS modeling language. In the introductory step, task nodes were merged and split in order to discover the underlying process. Weijters and Aalst (2001a, b) developed a heuristic approach to construct so-called “dependency/frequency tables” and “dependency/frequency graphs”. However, the preliminary results presented in their work only provided heuristic approaches and focused more on issues such as noise. Aalst and Dongen (2002) presented the EMiT tool which used an extended version of α-algorithmc to incorporate timing information.
Some other studies (Burt and Minor 1983; Aalst et al. 2004; Scott and Carrington 2011) used sociometry (also referred to as sociography) as a method to present data on interpersonal relationships in a graph or matrix form. The term sociometry was coined by Jacob Levy Moreno (Moreno and Jennings 1934) who conducted the first long-range sociometric study at the New York State Training School for Girls in Hudson, New York (Wasserman and Faust 1994; Song 2004). As part of this study, Moreno and Jennings (1934) used sociometric techniques to assign residents to various residential cottages. They found that assignments on the basis of sociometry substantially reduced the number of runaways from the facility. Since the early work of Moreno and Jennings, sociometry, and Social Network Analysisd in particular, have been active research domains (Desel et al. 2004). Workflow management systems like Staffware register the start and completion of activities (Aalst and Dongen 2002). ERP systems like SAP log all transactions, e.g., users filling out forms, changing documents, etc. Business-to-business (B2B) systems log the exchange of messages with other parties (Dumas et al. 2005). Call center packages as well as general purpose CRM systems log interactions with customers. These examples show that many systems have some type of event log often referred to as “history”, “audit trail”, “transaction file”, etc. (Aalst 2005).
Rozinat and Aalst (2008) proposed an incremental approach to check the conformance of a process model and an event log. Their goal was the detection of inconsistencies between a process model and its corresponding execution log, and the quantification by the formation of metrics. Aalst et al. (2012) used ProM’s Conformance Checkere and Performance Analysis with Petri Netf tools—which are suitable for replaying—to establish a precise relationship between events and model elements. By applying these techniques, they could diagnose deviations from the modeled behavior, and identify the severity of each deviation. Accordingly, their results could properly detect and show bottlenecks within the processes.
Results and discussion
As mentioned above, there are many different process mining techniques with respect to three basic classes: (1) process mining techniques for discovering new models, (2) process mining techniques for auditing and conformance checking of a model with real data, and (3) process mining techniques that can be used for enhancement purposes. This study was focused on the first and second classes of process mining techniques (i.e., Discovery and Conformance Analysis). From the discovery class, Alpha algorithm, Heuristic Mining, Fuzzy Mining, and Social Network Analysis techniques were applied in order to discover models and organizational structures related to the handling of the proceedings’ peer reviews for an international conference in Thailand. From the conformance analysis class, LTL Checker and Performance Analysis techniques were used in order to compare, check, and audit the authentic dataset with a pre-defined model. Accordingly, the main goals of the study were: (1) to convert the collected event log into the appropriate format supported by process mining analysis tools, (2) to discover process models and to construct social networks based on the collected event log, and (3) to find deviations, discrepancies and bottlenecks between the collected event log and the master pre-defined model. Therefore, the results of the applied approaches—considering the second (2) and third (3) goals of the study—are discussed as the following:
Alpha (α) algorithm
Using the Fuzzy Mining technique, we also could project all log traces onto a model simultaneously. Figure 7 (right) indicates an animation based on the historic information received from all log traces in the peer-review event log. The animation shows the actual execution of the cases (i.e., revision of manuscripts) based on the fuzzy model. The animation can be played multiple times in order to come up with a better understanding about what has really occurred during the peer review process in the international conference. Moreover, watching the animation helped the organizing committee members to differentiate individual process instances and observe the overall peer review process at a particular point in time. In addition, the technique significantly increases the awareness of the conference organizers about the parts that are more important (or less important) during the proceedings review process.
Analysis of social network
The work presented in this paper investigated the social network (between the reviewers and the decision board members) with respect to three metrics as follows: (a) Handover of Task metric. This metric examined who passed a task to whom, (b) Similarity of Tasks metric. This metric examined who executed the same type of tasks and (c) Working Together metric. This metric examined how frequently individuals have worked on the same task.
Handover of task
Similarity of task
Role hierarchy mining
This approach is based on an Agglomerative Hierarchical Clustering technique which deals with joint activities in the peer review event log. Here, the main idea is to create multiple clusters which are consistent with the activities that each individual performs. Figure 11 (down) shows a dendogram derived from the Agglomerative Hierarchical Clustering technique. The technique enabled us to generate flat or disjoint organizational entities by adjusting the dendogram with a threshold (i.e., certain value). As a result, by adjusting the dendogram with a threshold value of 0, we obtained 4 different clusters (meaning there are 4 different groups of individuals with dissimilar roles and duties) within the peer review event log. The first group, cluster 1, included Mr. A as the Editor-in-Chief and head of the organizing committee. The second group, cluster 2, consisted of the Mr. E, Mr. F, Mrs. D, and Ms. G who were in charge of receiving the reviews/feedback from the invited reviewers. The third group, cluster 3, consisted of Ms. B who behaved as a medium between cluster 2 and Mr. C. Thus, Ms. B was constantly in touch with cluster 2 and Mr. C. And consequently, the fourth group, cluster 4, included Mr. C as the secretary of the conference in charge of inviting reviewers, collecting all the reviews, and announcing the board decisions to the authors (i.e., whether the manuscript is accepted or rejected). Therefore, using the Organizational Miner technique we could investigate the data at a higher level of abstraction—compared with the previously mentioned techniques. While the handover of task, similarity of task and working together metrics emphasized more on the individuals and the transactions between individuals; the Organizational Miner and Role Hierarchy Miner technique mainly focused on the teams, groups and hierarchies as a whole.
Semantic LTL checker
It is often the case that processes for reviewing manuscripts need to obey specific rules and regulations. For instance, in order to make a decision about a manuscript (i.e., accept or reject) for the international conference, feedback (reviews) from at least three reviewers needed be received and collected. Similarly, any invitation of an additional reviewer was based on the board decision after careful investigation of the all initially collected reviews from reviewers. One way to check whether these rules and regulations have been actually obeyed is to audit the event log using certain techniques. We used the Semantic LTL Checker tool in ProM to verify and check the property: Does the task “Invite Another Reviewer” always happen after the tasks “Board Decide” and “Collect All Reviewers”? (i.e., formula: eventually_activity A_then B_then C). The resulting screen shown on the right side of Fig. 11 (up) indicates that the event log is divided into two main parts: (1) the part including the cases that satisfy the property (i.e., formula: eventually_activity A_then B_then C), and (2) the part including the cases that do not satisfy the property. Studying the LTL results for the correct process instances, we realized that in 80 of the cases (out of the total of 87 process instances) the task “Invite another Reviewer” eventually happened after “Board Decide” and after “Collect All Reviews”. However, in 7 of the cases (out of the total of 87 process instances) the task “Invite another Reviewer” did not happened after “Board Decide” and after “Collect All Reviews”. This is assumed to be an obvious violation of rules and regulations during the peer review process.
When implementing different process mining discovery algorithms or different social network analysis metrics, all of the process instances in the input event log are built and replayed based on the input Petri net. Therefore, it is possible that a process instance is not completely compatible (or does not fit) with the Petri net. In this section, we used the Conformance Checker technique in order to replay (i.e. audit) the peer review event log. The rationale behind this approach was to compare the level of fitness between the authentic event log and the pre-defined master model. Using this approach, when an activity is executed in a log trace; one of the matching transitions in the Petri net will be paired with that activity, and then the necessary measurements are taken.
We used the “model perspective” feature of ProM’s Conformance Checker plugin in order to replay the peer-review dataset with respect to: (1) number of missing activities (i.e., activities that were missed to be taken into account in the Petri net model), (2) number of failed activities (i.e., activities that were not enabled), (3) number of remaining activities (i.e., activities that remained enabled). The results showed that 9 blocks of activity in authentic peer review event log were not compatible/(no fit) with the activities in the pre-defined Petri net model (i.e., master model). Moreover, five activity traces were not correctly enabled in the authentic event log. Also, seven activities were missed in the authentic event log compared with the pre-defined master Petri net model. In other words, the resulting Petri net model and the real peer review event log were not a complete fit and compatible with each other at a few points and nodes.
Bottleneck 1: The board has made a decision before receiving feedback/reviews from Reviewer X. This is an obvious violation of the rules, as the board can only make a decision when feedback from a reviewer is received, or when the legitimate time for a reviewer to review the manuscript has ended.
Bottleneck 2: The board has rejected a manuscript before receiving feedback from Reviewer X. This is an obvious violation of the rules, as the board can only reject a manuscript after receiving all of the feedback from all reviewers.
Bottleneck 3: The board has rejected a manuscript after only receiving feedbacks from two reviewers. This is an obvious violation of the rules, as the board can only reject a manuscript after receiving feedback from at least three of the reviewers.
Bottleneck 4: The board has accepted a manuscript before receiving feedback from Reviewer X. This is an obvious violation of the rules, as the board can only accept a manuscript after receiving all of the feedback from all reviewers.
Bottleneck 5: The board has rejected a manuscript before receiving feedback from Reviewer X. This is an obvious violation of the rules, as the board can only reject a manuscript after receiving all of the feedbacks from all reviewers.
Performance analysis with petri net
In this study, we used an event log describing the handling of reviews for the proceedings of an international conference at a private university in Thailand. The authentic event log was originally in Microsoft’s Access database format and was consisted of 87 cases (i.e., number of submitted manuscripts) and 3267 events (i.e., total number of tasks performed throughout the peer review process). Knowing that that ProM 5.2 only receives the input data in MXML-formatted event logs, we used the functions addPIandAttr and returnFieldNamesInArray (in Visual Basic script) to create four tables with specific structures. These functions were created based on the concept that we needed a table in which each row contained information about a unique process instance as well as additional information for each process instance. Subsequently, ProMimport was chosen as a framework to extract a MXML-formatted log from the data source, and an ODBC connection was configured between the data and the personal computer (PC). Since the event log contained sensitive details concerning the authors and their personal information; the event log was deliberately made anonymous in the necessary areas. After successfully converting the data into MXML format, we applied Alpha, Heuristic and Fuzzy process mining techniques (from the process discovery class of the process mining analysis tools) in order to discover models and extract information from the peer-review event log. Using the Alpha algorithm, we could investigate the peer review process with respect to the tasks that came before/after other tasks, the tasks that concurrently occurred with other tasks, or the tasks that were duplicated (i.e., loop) in the model. The results showed that although each paper was initially sent to three different reviewers; it was not always possible to make a decision after the first round of reviewing; therefore, additional reviewers were invited. In total, all the accepted and rejected manuscripts were reviewed by an average of 3.9 and 3.2 expert reviewers, respectively. However, when dealing with noisy data, the Alpha algorithm could not produce reliable and robust models—as any frequency information about dependencies of the tasks is not taken into account. Therefore, we used a more sophisticated technique named Heuristic Miner algorithm in order to illustrate and investigate the dependency between different tasks during the peer review process. Our main objective was to create models that were less sensitive to the incompleteness of logs and contain noise. Although the idea of applying Heuristic Miner algorithm was interesting, yet the technique could not properly deal with spaghetti-like structures which contain mixed AND and XOR joints/splits. Therefore, we used Fuzzy Miner technique (as a more sophisticated approach) to overcome these limitations and constraints. When studying the resulting fuzzy model, we realized that the tasks “Board Decide”, “Invite Another Reviewer”, “Time-Out X”, and “Receive The X Review” were the most significant tasks (during the peer review process of the international conference) allocating 27.52, 23.16, 11.68, and 11.48% of the performed tasks to themselves, respectively. Furthermore, we applied ideas obtained from cartography to build process-model maps based on the Fuzzy Mining algorithm. The result was an animation that produced with a better understanding of what has occurred during the proceedings peer-review process.
As a part of the process discovery, we also applied three metrics with the purpose of building graphical social network models in terms of: (a) Handover of Task, (b) Similarity of Task, and (c) Working Together. The main idea was to identify different groups and communities of the conference’s committee members and organizers (clusters) through different Social Network Miner techniques.
Consequently, using LTL approach, Conformance Checking, and Performance Analysis with Petri Nets (from the conformance checking class of process mining tools), we could assess the fitness of the models by detecting discrepancies between the authentic data and the pre-defined master model. The results showed that 9 blocks of activity in authentic peer review event log were not compatible (not fit) with the activities in the pre-defined Petri net model (i.e., master model). Moreover, 5 activity traces were not correctly enabled in the authentic event log. Also, 7 activities were missed in the authentic event log compared with the pre-defined master Petri net model. In other words, the resulting Petri net model and the real peer review event log were not a complete fit and compatible with each other at a few points and nodes. Moreover, only in 45% of the cases a feedback from the second reviewer was received on time, while in 55% of the cases, the second reviewers did not participate in any peer review process until the time-out was reached. On the other hand, dealing with the feedback (comments) received from the first and the third reviewers; the conference committee members and the organizers did not attend to those feedback/comments in a timely manner.
aFor more information about ProM and Process Mining visit the website:http://www.processmining.org/.
bFor more information about ProMimport visit the website: http://www.promtools.org/promimport/.
cFor a detailed description of the α-algorithm and a proof of its correctness please check the link: http://en.wikipedia.org/wiki/Alpha_algorithm.
dFor more information about basics of the Social Network Analysis techniques read the book titled “Social Network Analysis Theory and Applications” in the link below: http://train.ed.psu.edu/WFED-543/SocNet_TheoryApp.pdf.
eFor more information about the Conformance Checker tool visit the link below: http://www.processmining.org/online/conformance_checker.
fFor further information about the Performance Analysis with Petri Nets check the link below: http://www.processmining.org/online/performanceanalysiswithpetrinet?s=performance&s=analysis.
gFor detailed information about the functions please check the “manual” provided in the link below: http://www.processmining.org/promimport/%20tutorials.
hFor more information about the Similarity Coefficient please visit and read our article titled “Prom: Analysis of Social Network in students registration system” in 10th International Conference of ICT and Knowledge Engineering (ICT & Knowledge Engineering) published in IEEE Xplore.
WP made substantial contributions to conception and design of data analysis and interpretation of data; has been involved in revising the manuscript critically for important intellectual content. PP made substantial contributions to conception and design of data analysis and interpretation of data, collecting and acquisition of data; has been involved in drafting the manuscript. Both authors read and approved the final manuscript.
We would like to express our sincere gratitude to Emeritus Professor Dr. James. G. Williams from School of Information Sciences, University of Pittsburgh, for doing a great favor and proofreading this article.
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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