Burattin, Andrea

Within the process mining field we are witnessing a tremendous growth of applications and development frameworks available to perform data analyses. Such growth, which is very positive and desirable, comes with the cost of learning each new tool and difficulties in integrating different systems in order to complement the analyses. In addition, we are noticing the lack of tools enabling collaboration among the users involved in a project. Finally, we think it would be highly recommended to enable ubiquitous processing of data. This paper proposes a solution to all these issues by presenting a chat bot which can be included in discussions to enable the execution of process mining directlyfrom the chat

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Downloaded from orbit.dtu.dk on: Sep 11, 2019Integrated, Ubiquitous and Collaborative Process Mining with Chat BotsBurattin, AndreaPublished in:Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019Publication date:2019Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Burattin, A. (2019). Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots. In Proceedings ofthe Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019 (pp. 144-48). [Paper DT6]CEUR-WS. CEUR Workshop Proceedings, Vol.. 2420Integrated, Ubiquitous and CollaborativeProcess Mining with Chat BotsAndrea Burattin[0000−0002−0837−0183]Software and Process EngineeringTechnical University of Denmarkandbur@dtu.dkAbstract. Within the process mining field we are witnessing a tremen-dous growth of applications and development frameworks available toperform data analyses. Such growth, which is very positive and desir-able, comes with the cost of learning each new tool and difficulties inintegrating different systems in order to complement the analyses. Inaddition, we are noticing the lack of tools enabling collaboration amongthe users involved in a project. Finally, we think it would be highly rec-ommended to enable ubiquitous processing of data. This paper proposesa solution to all these issues by presenting a chat bot which can be in-cluded in discussions to enable the execution of process mining directlyfrom the chat.Keywords: Process Mining · Chat bots · Ubiquitous computing.1 IntroductionProcess mining [1] has been gaining more attention over the previous years,becoming an impactful discipline, both in terms of research maturity and appli-cations/implementations availability. It comprises the analysis of data referringto business process events and, within itself, it is possible to identify severalsub-disciplines, such as control-flow discovery, conformance checking and pro-cess extension [8].With a multitude of tools available, both as standalone, web applications andlibraries, process mining projects can now be completed taking advantage of abroad range of possibilities. In particular, just by focusing on the open sourcetools, one of the leading software is ProM [13] (with its ProM Lite variant). Inaddition, some libraries are becoming popular, since they allow the developmentof software in different programming languages, such as bupaR [9] (for the Rlanguage) and PM4Py [5] (for the Python language). Data can be exchangedbetween these tools, for example by using the XES standard [7], however, com-bining these tools together can become cumbersome as it is always necessary todump the log and change environment.Another important issue, that only recently is receiving attention [12], is therole of human involvement in the data analysis process [4]. Specifically, all pro-cess mining tools currently available do not allow the collaborative execution of2 Andrea Burattinmining projects. Many of them provide functionalities to share the results of theanalysis, for example via web dashboards, but there is no support for true collab-oration and interaction. The increasing availability of network connections andsmart-devices should be a key driver of human involvement in mining projects.To address all these challenges, the tool described in this paper has beendesigned with the following functional requirements in mind:– To propose a uniform interface to interact with any number of process miningapplications in a seamless way and without the need of confronting everytime a steep learning curve;– To enable collaboration on process mining projects by design: not just shar-ing intermediate or final results, but discussing and performing each indi-vidual step of the analysis in real-time and allow all persons involved tocontribute to it;– To enable ubiquitous and truly platform independent execution of processmining projects (e.g., start a mining session at the office PC, continue on thesmartphone while commuting, and finish the analysis at home, on a tablet).The rest of the paper is structured as follows: Sec. 2 reports some technicaldetails about the tool, Sec. 3 describes the main implementation details, andfinally Sec. 4 concludes the paper.2 Technical DetailsThe strategy we decide to adopt in order fulfill our requirements is to developa chat bot [11] (sometimes also referred to as chatter bot) that can be includedin a discussion on an instant messaging platform. Such bot, in turn, enablesto interact with it in order to perform specific steps of the analysis (i.e., theactual process mining) and sharing the results with all people involved in thediscussion.The general idea behind a chat bot is to exploit the APIs offered by differentinstant messaging systems and define a software, i.e. the bot, that consumes eachmessage sent by the participants in a discussion. Whenever a message matchesa predefined pattern (also known as command) which is recognized by the bot,the software performs some actions and sends the response back into the dis-cussion so that all participants can read it. The software that runs the bot hasto be executed in a machine, i.e. a server, which has access to the APIs of theinstant messaging. In the context of this paper, we decided to use the instantmessaging platform Telegram1 due to its explicit support of chat bots, as wellas its portability, which is manifested in a number of implementations availablefor a large number of platforms, including open source applications2.The chat bot we devised, which is called Process Mining Bot (or pmbot),assumes that each chat is analyzing a single log at the time. This log, called the1 See https://telegram.org/.2 See https://telegram.org/apps.Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots 3pyTelegramBotAPIpmbotPM4PybupaRProMChat cjChat ciChat ckFig. 1. General architecture of the Process Mining Bot.current log, is provided to the bot by sharing it within the chat. When a file isshared, the pmbot automatically is notified and, if such file contains an eventlog, the server running the pmbot will download it and it will be used for allfollowing analyses. By uploading a new file, the current log is overwritten withthe new one. It is important to note that if a file containing the log is uploadedin a chat, it is not necessary for all clients to download it (in fact, only the pmbothas to download it). This is of critical importance as it enables the analysis alsoin devices where memory is a precious resource, such as smart-phones.As depicted on the right-hand side of Figure 1, the Process Mining Bot canbe included in as many chats as needed: it will keep track of each of themindividually and therefore each chat will have its own current log. The left-handside of Figure 1 depicts the three different process mining frameworks, writtenin three different programming languages that the current implementation ofthe pmbot interacts with ProM [13], bupaR [9], and PM4Py [5]. As previouslymentioned, the pmbot interacts with the other chat members by “reading” eachevent being generated in the chat and, whenever one of these messages matchesone of the commands, this is recognized and the corresponding action is triggered.The current implementation of the pmbot supports the following commands:\start This command initiates a new process mining session, by asking a licensecode, needed to use the resources required by the bot;\describelog Command to produce general log statistics, including the numberof traces in the log, the number of activities, their distribution and a coupleof charts with the duration of the process instances and the events over time.This command uses the PM4Py library;\hm [DEP THR] This command runs the Heuristics Miner algorithm [2] andsends back pictures (one with the heuristics net and another with the Petrinet) of the mined model. The default value of the dependency threshold canbe overwritten. This command uses the PM4Py library;\alpha This command performs the Alpha Miner algorithm [3] on the currentlog. This command uses the PM4Py library;\dfg This command computes and returns the Direct Following Graph com-puted on the current log using the PM4Py library;4 Andrea Burattin\im Command to perform the Inductive Miner algorithm [10] on the current logand to get the picture of the resulting model. This command uses ProM;\precedencematrix This command produces the precedence matrix of all ac-tivities in the current log by using the bupaR library;\dottedchart, \relativedottedchart These two commands plot the dottedchart of the current log by using either a relative or absolute time scale.These commands use the bupaR library;\resources Command to plot how often each resource is involved in an event,by using the bupaR library;\keepactivities This command changes the current log by filtering it in orderto keep only some activities. The user can enter the name of all activities tokeep until a special termination command is given;\removefilters This command can be used to reset the current log to its orig-inal form (i.e., the same status as it has been uploaded). It is useful to resetthe current log whenever the users want to the applied filters.The current set of commands already allows several interesting analyses. Actu-ally, in all data and process mining projects it is arguable that the most impor-tant element of the analysis are the users who have to be smart in (i) understand-ing which algorithm is the most suitable in the given setting and (ii) interpretingthe results of the mining algorithm. On top of this, the ability to recursively ap-ply filters allows the investigation to go beyond the raw log.Whenever treating data, one of the most delicate elements is the confidential-ity of the data. Whenever implementing a bot it is important to understand thatit is actually just an application running on someone’s machine and the ownerof such machine has access to the log. Therefore, in case the data to be mined isconfidential it might be wise to have an in house and trusted machine allocatedto the purpose. In addition to the machine running the pmbot, when the log isuploaded it has to transit over the Telegram’s servers. To impede Telegram frombeing able to read the log, the current implementation supports encrypted zipfiles for transmitting the file. Of course, more advanced techniques (e.g., GPG)might be easily implemented as well.A second element to be considered is the accountability of the activitiesperformed by the bot: if the log file is very large, the machine running thepmbot might require a vast amount of resources. For this reason, in the currentimplementation of the bot we introduced a limit on the size of the event log(easily adjustable in the configuration file) and, in order to send any command,it is necessary to enter a “license code”. This code enables the access to specificset of functionalities and time-based coded might also be implemented to grantaccess only for limited periods.3 Implementation DetailsThe Process Mining Bot has been implemented as a standalone Python soft-ware, exploiting the Telegram API3 and, in particular, the corresponding Python3 See https://core.telegram.org/bots/api.Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots 5wrapping implemented in the pyTelegramBotAPI4. The current version of thesoftware embeds the PM4Py libraries. In order to execute bupaR and ProM, ex-ternal calls are done using the subprocess Python module. The complete sourcecode of the bot is available in GitHub at https://github.com/delas/pmbot.A screencast showing all functionalities in a mock-up session is available athttps://youtu.be/eg8jJ3bB0NI. Finally, a detailed tutorial presenting a walkthrough of the basic steps in order to interact with the bot is available at [6].4 ConclusionsIn this paper we presented an innovative technique of completing process min-ing projects in a collaborative, ubiquitous, platform independent and integratedmanner. The technique exploits the idea of chat bots and, by including theminto existing instant messaging discussions, it is possible to have direct access toprocess mining functionalities. The prototype implementation presented in thispaper is available for Telegram and the bot is capable of executing commandson ProM, bupaR, and PM4Py.References1. van der Aalst, W.M.: Process Mining. Springer, second edn. (2016)2. van der Aalst, W.M., Weijters, T.A.J.M.M.: Rediscovering Workflow Models fromEvent-based Data Using Little Thumb. Integr Comput Aided Eng 10(2) (2003)3. van der Aalst, W.M., Weijters, T.A.J.M.M., Maruster, L.: Workflow Mining: Dis-covering Process Models from Event Logs. IEEE Trans Knowl Data Eng 16 (2004)4. Ankerst, M.: Human Involvement and Interactivity of the Next Generation’s DataMining Tools. In: ACM SIGMOD DMKD, position paper (2001)5. Berti, A., van Zelst, S., van der Aalst, W.: Process Mining for Python (PM4Py):Bridging the Gap Between Process- and Data Science. In: CEUR-WS Online Pro-ceedings of the ICPM Demo Track (2019)6. Burattin, A.: pmbot Tutorial (2019), https://github.com/delas/pmbot/wiki7. Gu¨nther, C.W., Verbeek, E.H.M.W.: XES Standard Definition. www.xes-standard.org (2009), http://www.xes-standard.org/8. IEEE Task Force on Process Mining: Process Mining Manifesto. In: Business Pro-cess Management Workshops. pp. 169–194. Springer-Verlag (2011)9. Janssenswillen, G., Depaire, B., Swennen, M., Jans, M., Vanhoof, K.: bupaR: En-abling reproducible business process analysis. Knowl Based Syst 163 (2019)10. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.: Discovering Block-StructuredProcess Models from Event Logs - A Constructive Approach. In: Proceedings ofPetri Nets. pp. 311–329. Springer Berlin Heidelberg (2013)11. Mauldin, M.L.: ChatterBots, Tinymuds, and the Turing Test: Entering the LoebnerPrize Competition. In: AAAI. vol. 94, pp. 16–21 (1994)12. Minku, L.L., Mendes, E., Turhan, B.: Data mining for software engineering andhumans in the loop. Progress in Artificial Intelligence 5(4), 307–314 (Nov 2016)13. Verbeek, E.H.M.W., Buijs, J., van Dongen, B., van der Aalst, W.M.: ProM 6: TheProcess Mining Toolkit. In: BPM 2010 Demo. pp. 34–39 (2010)4 See https://github.com/eternnoir/pyTelegramBotAPI.

Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots

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Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots

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