Trace Clustering for User Behavior Mining

Business information systems support a large variety of business processes and tasks, yet organizations rarely understand how users interact with these systems. User Behavior Mining aims to address this by applying process mining techniques to UI logs, i.e., detailed records of interactions with a system\u27s user interface. Insights gained from this type of data hold great potential for usability engineering and task automation, but the complexity of UI logs can make them challenging to analyze. In this paper, we explore trace clustering as a means to structure UI logs and reduce this complexity. In particular, we apply different trace clustering approaches to a real-life UI log and show that the cluster-level process models reveal useful information about user behavior. At the same time, we find configurations in which trace clustering fails to generate satisfactory partitions. Our results also demonstrate that recently proposed representation learning techniques for process traces can be effectively employed in a realistic setting

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

A Visualization Framework for Designing Process Mining Diagrams

Sündmuslogid sisaldavad väärtuslikku informatsiooni äriprotsesside seisundi kohta. Informatsioonile ligi pääsemiseks peab andmestiku viima arusaadavale kujule. Protsissikaeve tööriistad kasutavad erinevaid diagramme, mis toetavad sündmuslogide visuaalset uurimist. Nende diagrammide kujundamine ei ole lihtne ülesanne, sest tihti ei tea arendaja ega kasutaja, kus huvipakkuv informatsioon võib asuda. Seepärast peavad diagrammid olema paindlikud, kuid samas lihtsad ja intuitiivsed, et nii analüütikud kui ka mitteasjatundjad saaksid tööriista kasutada. Antud töö uurib olemasolevate protsessikaeve diagrammide kujundusi ja kuidas need kujundused on autorite poolt põhjendatud. Töös tutvustatakse ka raamistikku, mis on välja töötatud selleks, et lihtsustada ja täiustada protsessikaeve diagrammide kujundamist. See põhineb andmete visualiseerimise teoorial ja visualiseerimise praktikatel protsessikaeves. Raamistiku tõhusust on katsetatud juhtumuuringus.Event logs hold valuable information about the health of business processes. In order to access this information, raw data must be transformed to a comprehensible format. Process mining tools use various diagrams to support visual exploration of process logs. Designing such diagrams is not an easy task because oftentimes neither the developer nor user know where interesting or intriguing information lays. Therefore, the diagrams require thoughtful designs that on the one hand allow flexible exploration, and on the other hand, are simple and intuitive to use for analysts as well as non-experts. This work takes a look into existing solutions of process mining visualizations and the design decisions the visualizations are based on. A framework is proposed to simplify and improve the design process for process mining diagrams. It is based on data visualization theory as well as visualization practices in process mining. The effectiveness of the framework is tested in a case study

Discovering interacting artifacts from ERP systems (extended version)

The omnipresence of using Enterprise Resource Planning (ERP) systems to support business processes has enabled recording a great amount of (relational) data which contains information about the behaviors of these processes. Various process mining techniques have been proposed to analyze recorded information about process executions. However, classic process mining techniques generally require a linear event log as input and not a multi-dimensional relational database used by ERP systems. Much research has been conducted into converting a relational data source into an event log. Most conversion approaches found in literature usually assume a clear notion of a case and a unique case identifier in an isolated process. This assumption does not hold in ERP systems where processes comprise the life-cycles of various interrelated data objects, instead of a single process. In this paper, a new semi-automatic approach is presented to discover from the plain database of an ERP system the various objects supporting the system. More precisely, we identify an artifact-centric process model describing the system’s objects, their life-cycles, and detailed information about how the various objects synchronize along their life-cycles, called interactions. In addition, our artifact-centric approach helps to eliminate ambiguous dependencies in discovered models caused by the data divergence and convergence problems and to identify the exact "abnormal flows". The presented approach is implemented and evaluated on two processes of ERP systems through case studies

Development of an ERP with CI/CD application, Authentication and System Auditing

Developing and maintaining software like ERPs can be challenging because of the complexity and the amount of data that these systems require maintaining. Many of the software programs can grow with weak structure, which lead to great effort to maintain, and with more probability to error. This project proposes that a development cycle that incorporates DevOps can have major bene ts, by not only removing some hassle the programmers and systems admins have with testing and deploying the system, but can also give a early feedback if the changes made into the application brings problems to the systems. The design of a CI/CD pipeline and audit logs, and the implementation in an ERP development helped get more feedback and cause of root problems, which lead to more confidence in the developers to make changes, and to escalate more quickly since the deployment is automatized.Desenvolver "software" como os ERPs podem ser difícil de manter devido à complexidade e a quantidade de dados envolvida nestes sistemas. Isto leva a que muitos destes "softwares" cresçam com uma estrutura de código fraca, o que leva a um esforço adicional para manter, e com maior probabilidade para erros. Este projeto propõe que a incorporação do conceito de DevOps no ciclo de desenvolvimento traz muitas vantagens, não só a remover algum trabalho dos programadores e dos administradores de sistemas ao ser mais fácil testar o sistema e fazer deploy do mesmo, mas também fornece uma forma de feedback mais rápida para eventuais erros. O "design" de uma pipeline CI/CD e logs para auditoria do sistema, e a respetiva implementação destes conceitos no desenvolvimento consegue dar mais feedback a problemas, o que leva a uma maior confiança dos programadores para fazer alterações, e conseguir escalar a solução mais rapidamente visto que a implantação é automatizada

Predictive Process Monitoring for Lead-to-Contract Process Optimization

Äriprotsesside toetamiseks on üha laiemalt kasutusele võetud ettevõtte ressursside planeerimise (ERP) tööriistad, sealhulgas CRM süsteemid müügiprotsessi jaoks. ERP süsteemid salvestavad oma töö käigus protsesside logisid, mille oskuslik käsitlemine võimaldab efektiivistada äriprotsesse. Protsessilogide analüüsimiseks on välja töötatud protsessikaeve meetodid, mis oskavad logidest pöördprojekteerida tegelikult käivitatud protsesside mudeleid. Neid meetodeid on rakendatud koos ennustava seire meetoditega protsesside tulemuste soovitud ja soovimatute tulemuste varajaseks tuvastamiseks.\n\rKuigi ennustav seire on hiljuti rohkelt tähelepanu saanud ja leidnud rakendamist soovitusmootorites, mis pakuvad välja soovitusi äriprotsesside parendamiseks, ei ole seni palju uuritud kontekstiandmete, nt müügisüsteemi kirjetes klientide finantsandmed, mõju ennustava seire tulemustele soovituste kontekstis.\n\rKäesolevas magistritöös uuritakse kontekstiandmete mõju ennustava seire mudelite kvaliteedile müügiprotsessi optimeerimise kontekstis. Eksperimendid näitavad, et välistel kontekstiandmetel on pigem negatiivne mõju, samas kui sisemistel, protsessi käigus kogutud kontekstiandmetel on positiivne mõju mudelite kvaliteedile. Muuhulgas selgub eksperimentidest, et juba kolme esimese sündmuse baasil saab müügiprotsessis ennustada müügi õnnestumist.Business processes today are supported by enterprise systems such as Enter-\n\rprise Resource Planning systems. These systems store large amounts of process execution\n\rlog data that can be used to improve business processes across the organization. The\n\rprocess mining methods have been developed to analyze such logs, which are capable of\n\rextracting process models. These methods, in turn, have been applied in conjunctions\n\rwith predictive monitoring methods for early differentiation of desired and undesired\n\routcomes. Although predictive monitoring approach has recently caught attention and\n\rfound application in recommendation engines, which suggest cases to improve business\n\rprocess outcomes, there is no much research on how contextual data, such as clients fi-\n\rnancial indicators and other external data, may improve the quality of recommendations.\n\rThis thesis examines whether including the external data with the event data affects the\n\raccuracy of predictive monitoring for early predictions positively. More specifically, this\n\rthesis reveals usage of context data had the adverse effect on the performance of learned\n\rmodels. Furthermore, the study indicated that the usage of first three events from the\n\revent logs with internal data is sufficient to predict the label of an opportunity in the\n\rsales funnel