This paper presents a three-layered methodology to contrast variants of services ofered by Municipalities with the main aim of improving their business processes re-engineering as well as other significant phases of the software life cycle, such as configuration and maintenance. The methodology makes it possible to detect discrepancies or alignments among services' variants. It relies on execution logs and applies clustering algorithms to reduce the huge amount of available logs into few clusters of "equivalent" executions. Then variance mining becomes a cornerstone to contrast clusters representatives and enables analysis on the ofered services or those a specific Municipality would like to ofer. The methodology has been validated on real case studies.
because of diferent locally-applicable laws, but also in the way services are exposed to Every day, Municipalities provide to the citi- citizens, because of the increasing availabilzens a number of diferent services by means ity of digital services the Public Administraof PAIS (process-aware information system). tions can rely on.
PAIS is a software system that bases its exe- Variants are part of the Municipalities’ incution logic on business process models. These formation system and as such can provide use"business processes" even though similar in ful insights. In this paper, we concentrate on scope, may vary from Municipality to Mu- the usage of variants to get information usenicipality. The diferent versioning processes ful to contrast their business processes and to are called variants. Just to cite a few exam- improve their re-engineering as well as other ples, there might be diferences in the inter- phases of the software life cycle such as connal management and organisation, such as figuration and maintenance. Just to mention the human resources involved to carry out a few examples, our methodology aims at despecific tasks, or in the process control flow tecting "anomalous" tasks among variants, bottlenecks to be removed to improve services performance, compliance concerning municipalities guidelines or local laws, best practices to be replicated, or trends on the software functionalities depending on the territories or Municipalities’ size.
The proposed 3L methodology depicted in Figure 1 exploits the log files generated by running variants on PAIS systems. Such log ifles provide information on data and activ- mation system. Section 3 introduces two variities on variants execution and, hence, pro- ance mining algorithms for comparing two vide suitable and useful information for our variants. Section 4 describes the validation of purposes. By contrasting variant log files we Process Comparator algorithm on our data. detect variants diferences/similarities that al- Section 5 proposes a collection of works on low analysis on the ofered services or those a the comparison between variants Section 6 specific Municipality would like to ofer. Vari- is devoted to concluding remarks and further ability mining becomes, hence, a significant work. cornerstone of our methodology. We exploit suitable techniques for approaching variability and provide a way to deal with many vari- 2. Background ants because this is the case for our application domain. A PAIS (process-aware information system)
The following three-layered architecture de- is a process management and execution softscribes in more detail our proposal. ware that enables the separation of process
LEVEL 1 Rely on the PAIS – process-aware logic from application code. The logic is exinformation system – (more details on next pressed in terms of the process model, in this section 2) of any Municipality [1] and collect way, monolithic applications can be broken logs regarding variants of specific services. down into smaller services. This architec
LEVEL 2 Apply clustering algorithms to ture makes it easier to maintain the code, e.g. the (huge) set of log variants. The clustering a service can be modified without having to has been done on logs exposing the same ac- change the others. PAIS is therefore a tool tivities and a "closed" execution flow (within capable of expressing the flexibility needed a fixed interval) for the corresponding activ- to evolve processes and manage exceptions. ities. [1]
In our application domain, Municipalities, PAIS can be observed from diferent perthe clustering considerably reduces to few clus- spectives: functional, behavioural, organisaters (of "equivalent" logs). We elect one rep- tional, operational, and temporal. resentative log for each cluster. The functional perspective concerns the ac
LEVEL 3 Contrast the clusters represen- tivities that are performed. They constitute tatives through algorithms of variance min- the simplest unit of the process model and ing. We are actually using the Process Com- require human or machine resources to be parator in [2] as a basic algorithm for variant executed. The behavioural perspective conanalysis techniques. cerns the control flow between activities, i.e.
The 3L methodology will be evaluated on the order in which they are performed. The real data provided by a PAIS software installed languages that have been developed to exin eight thousand Italian municipalities. The press control flow also allow the expression software allows users to manage all the pro- of notions such as succession, parallel, condicesses that can take place in a municipality, tional, and loops. The information perspecfrom registration at the registry ofice to chan- tive concerns data objects and data flow. In ge of residence. The software is highly con- data-driven process models it is related to the ifgurable and this gives rise to a great deal of behavioural perspective. The organisational variability. The rest of the paper is organized perspective concerns actors, roles, and organas follows. The next section contains a brief isational units and their relationships. The introduction to PAIS – process-aware infor- operational perspective relates to the control lfow of activities, where they are considered Variant analysis techniques were used in as black-boxes. The time perspective concerns our case study to gain interesting insights. e.g. activity deadlines, duration, and waiting time between one activity and another.
A business model may present variability 3. Variance Analysis according to each of these perspectives. One Algorithms of the most frequently used techniques for dealing with variability is process mining. In literature, there are several approaches to
Process mining is a set of applications of comparing variants. Here below we compare
data science to process science, where pro- the most used variance analysis algorithms
cess science is understood as the common field suitable for our methodology.
between information technology and manage- In [2] Bolt, Leoni, and van der Aalst present
ment science [
Through process mining, business process parator), for comparing two variants for both execution logs can be analysed according to control flow and performance. The logs are four categories of techniques: automated pro- represented as annotated transition systems, cess discovery (extraction of a model from a and statistical tests are then performed to idenlog), conformance checking (comparison of tify significant diferences between the two a log with the model to identify diferences), models. Consider the log in Fig. 1 and break performance mining (performance monitor- it down into two sub-logs, where the first two ing), variant analysis (comparison of variants) traces belong to sub-log 1 and the third to [4]. sub-log 2. cate the extent of the efect in terms of pooled
The two sub-logs are then represented standard deviations. through an annotated transition system. The tool also allows to analyze the perfor
As can be seen in Fig. 2 the nodes stand mance of the two logs by measuring the avfor the states and the arrows show the transi- erage activity duration for each log and runtions between them. Annotations appear be- ning the same tests. The frequency of activilow states and to the side of transitions. If ties and transitions is visually translated with the trace visits that state (or performs that the thickness of arrows and margins. transition) a 1 will be annotated, otherwise A similar algorithm capable of visualizing a 0. To determine if the two logs have sta- the statistically significant diferences of two tistically significant diferences in a state (or variants from both control flow and perfortransition) a "Mann-Whitney U-test" is per- mance perspectives was introduced in [6] by formed, i.e. a non-parametric test to deter- Taymouri, La Rosa, Carmona. They intromine whether two statistical samples come duce the concept of "mutual fingerprints" that from the same population [5]. If the two states is, a directly-follows graph that shows only (or the two transitions) turn out to be statis- the behavior by which the two variants diftically diferent, the "Cohen’s d" is then mea- fer from each other. sured, which allows us to measure the dif- The method consists of three phases: feaference in the sample averages in terms of ture generation, feature selection, and filterpooled standard deviation units. The efect ing. size is then translated into a color code. The first phase is in itself divided into three
As can be seen in Fig. 3 the activities in parts: binarization, vectorization, and stakwhite (and the transitions in black) are those ing. In binarization, traces are represented as for which no statistically significant difer- time series of 0,1, depending on whether or ence was found. Colored activities (or tran- not an event exists in the given trace. Consitions), on the other hand, are those whose sider for example the trace = 1 2 1 1 in frequency is higher in one log than another. the event space = 1, 2, 3. It can be repreShades of red indicate that a state (or transi- sented in a vector space in which ( 1, ) = tion) is more frequent in the first log, shades 1011, ( 2, ) = 1011, ( 3, ) = 0000. In of blue indicate the opposite. The colors have the vectorization, the binarized vectors are a gradation, from lightest to darkest, to indi- transformed into the vectors of wavelet coefthe Process Comparator. The AB and AC arcs are black because only very high-frequency differences are detected with a few traces.
thodology on data coming from a large Italian company that provides PAIS systems for about eight thousand Italian municipalities.
After a clustering phase using the K-medoids [7] algorithm, we identified numerous clusters, which difered from each other in their control flow and activity set. For the sake of space, the discussion on the dimensions of clusters is kept out of this work. Clearly, the result is strongly dependent on the objective defined by the user that has to identify the number of clusters to consider.
medoids from here on are indicated according to the dimension of the municipality that generated them. In particular, the following were analysed: one of 7000 inhabitants, one ifcients according to the vector equation =
where H is the Haar basis matrix (in the augmented design matrix is built, where each individual trace and columns are con- clusters and the corresponding medoids. These of 10800, and one of 20800. that execute a certain transition or activity.
The log of the municipality of 7000 inhab- In the case of the municipality of 10800 initants has 386 observations made between habitants, the "Waste declaration" activity is 13/01/2014 and 06/02/2020, the log of the mu- performed in 0% of the traces, while in the nicipality of 10800 has 216 observations made municipality of 20800 it is performed 47.38% between 22/02/2011 and 06/06/2013 and the of the times. Checking the timestamps of the log of 20800 has 1739 observations made be- traces shows that the execution of this activtween 29/09/2014 and 11/02/2020. The me- ity occurs for the first time in August 2017. dian process duration is 19.1 days for the mu- This could mean that the activity is the renicipality of 7000 inhabitants, 19.5 for the mu- sult of a law that went into efect at that time. nicipality of 10800, and 50 seconds for the The 10800 inhabitants log by contrast never municipality of 20800. Such a large diference performs this activity and this is in line with between the first two municipalities and the the argument made, as the data taking ends third can be explained by assuming that in in 2013, thus before the eventual entry into the municipality of 20800 the process execu- force of this law. In this case, the variabiltions are computerized only after the process ity of the models is a symptom of a temporal is completed. evolution of the processes. In future analy
As can be seen in Fig. 6 the logs from 7000 sis of logs from other municipalities, it will and 20800 are very similar to each other, dif- be important to distinguish sources of timefering significantly from the log of the mu- dependent variability in the control flow in nicipality of 10800 inhabitants (Fig. 7, 8). This order to take into account only the most upshows that in our dataset the control flow of to-date version of the process. the process is independent of the size of the The two models coincide again in the exemunicipality, in contrast to what intuition wo- cution of activities "Opening printouts" and uld suggest. "Choice of investigation" that are executed
Fig. 7 shows the graph of the 10800 munic- with similar percentages from both processes. ipality compared to the 20800 municipality As can be seen from "Choice of investigation" it can be seen that both processes start with the flow is divided into four arcs leading to the "Start" activity followed by the "Dossier diferent activities "End of investigations", "Reopening" activity (similarity is represented by gistration of change", "Prior printouts" and a white background). The control flow chan- "Investigation". The activities and the arrows ges in the transition to the next activity: the in red are only carried out by the municipal20800 municipality runs the "Waste declara- ity with 20800 inhabitants and in blue the action" activity before running the "Opening tivities and jumps carried out by the municprintouts" activity, which is why the activ- ipality of 10800. The two processes coincide ity is colored red. The Process Comparator again in "Dossier closing", while it difers in also allows to view the percentage of traces the next two activities, which are "Action" for belong only to the 7000 municipality and evidence that the municipality of 10800 inhabitants has the same "Change of residence" process installed but with some functionality disabled.
A detail of the main variability of the three processes is given in Fig. 9. The arcs that connect "Choice of investigations" with "Prior printouts" and "Registration of changes" are present 5. Related only in the log of 20800 inhabitants (observing the detail of the comparison between the municipality of 7000 inhabitants and the one of 10800 it can be seen that only two arcs are present). The flower model-like structure of the 20800 municipality could be a consequence of the almost instantaneousness of the executed actions and could be traced back to a fluctuation in the recording of timestamps.
studied problem in the literature.
One of the earliest works on process comparison is [8]. In the paper, the authors present a technique and a tool to compare two models and their process instances. A model is generated by merging the two initial models, annotating the value of the diference between the number of instances of the first process compared to the second. Thus, it will same time extracting rules in a human-readable be possible to identify activities that are ex- form. ecuted more or less frequently in the second model than in the first.
In [9] Buijs and Reijers use the alignment 6. Conclusion and Future technique to compare event logs and mod- Works els from five municipalities. In particular, the alignment between the log of one municipal- This paper contributes to the definition of the ity and the model of another is measured, in 3L methodology to analyse and compare diforder to visualize their diferences. ferent variants of a business process. Our me
In [10] Nguyen, Dumas, La Rosa and Hof- thodology aims at identifying diferences in stede use a diferential perspective graph that the control flow, activities, frequencies, and allows to compare two event logs according also to identify the causes of these variations. to each perspective. In this case, decision trees The 3L methodology permits to simplify are generated to determine the business rules and reduce the complexity of the variance analfor each variant. In this case, decision trees ysis approach in order to permit its applicaare generated to determine the business rules bility in contexts where the cardinality of varifor each variant. ants is very high like in the public adminis
In [5], the work done in [2] is extended: in tration domain. this case decision trees are generated to de- Our methodology aims to reduce the numtermine the business rules for each variant. ber of comparisons thanks to clustering alA variant is then executed using the business gorithms that group together logs that have rules of the other, to test their exchangeabil- similar control flow and frequencies. Then ity. one representative for each cluster is com
Other authors suggested methods for iden- pared with each other using the process comtifying and use the business rules of a pro- parator, to highlight the diferences between cess. In [11] association rule mining is used the various variants of the same service. together with process mining to analyze the The proposed methodology is quite moddeviant cases of a process. The paper presents ular and we consider for future works to ima case of supervised learning in which traces prove and test other clustering and variance are labeled as deviant or non-deviant, enrich- analysis algorithms in order to find the best ing each trace with a set of relevant attributes. combination of algorithms that permits to reBusiness rules are then determined that allow duce the computation efort but at the same the recognition of unlabeled deviant cases. time keeping high the reliability. A connected
In [12] Bose and Van der Aalst address the future work concerns the validation of the problem of label incompleteness. If the event proposed approach in trusted application dolog has unlabeled instances the k-nearest mains, in such a field diferent works aim to neighbor approach is used to decide which implement PAIS systems on the blockchain class the trace belongs to. technologies [14, 15]. Retrieving information
In actual reality, it may be the case that from the blockchain permits us to have cerdata are not labeled as deviant or non-deviant, tified logs and enlarge their availability. but have numerical deviation measures, such as risk quantification. In [13] the authors present an algorithm capable of clustering data based on the deviation measure and at the