23 research outputs found
Mining object lifecycle processes : challenges, concepts, use cases
No full textEfficient and effective business processes are highly relevant for any enterprise. As a consequence, the analysis and continuous optimization of business processes is a prerequisite for staying competitive. Usually, the execution of business processes is supported by process-aware information systems, which assist enterprise staff in executing the business processes. As opposed to domain specific software systems supporting individual processes, process-aware information systems may be reused in various scenarios to implement and execute various business processes. In other words, a process-aware information system can be applied in various domains (e.g., human resources, e-learning, or logistics). Consequently, the process model specifies the behavior supported by the information system, providing maximum guidance to users. In case a deviation from the modeled behavior is required, which has not been foreseen in the process model, the information system cannot support the necessary measures to fulfill the business needs.
Object-centric and data-driven business processes provide powerful means to tackle such problems and to increase flexibility. Instead of focusing on the control flow of activities (e.g., traditional processes), object-centric processes emphasize process data and the various ways users may interact with these data. However, especially in such highly flexible scenarios, the need for analyzing and enhancing corresponding processes persists, and adequate approaches are very much needed.
Object-centric process management has evolved from a theoretical concept to the implementation of the PHILharmonicFlows framework capable of executing object-centric processes. The latter has already been applied in multiple real-world scenarios. This thesis extends the previous research with techniques and algorithms for the innovative analysis and optimization of object behavior as specified in so-called object lifecycle processes. The analysis includes checking the conformance of object lifecycle process models with the actual object behavior recorded in the event log, while at the same time considering flexibility and granularity issues. Additionally, two ways of automatically enhancing object lifecycle processes (i.e., model evolution and instance customization) based on event log information are presented. Moreover, approaches enabling process analysis by deriving event logs from software systems that typically do not provide suitable event data (e.g., legacy software systems), and the discovery of object lifecycle processes from event logs are presented in this thesis. Overall, the approaches presented in this thesis enable a holistic analysis and enhancement of object behavior in the context of object-centric process management
Applying Process Mining Algorithms in the Context of Data Collection Scenarios
No full textDespite the technological progress, paper-based questionnaires are still widely used to collect data in many application domains like education, healthcare or psychology. To facilitate the enormous amount of work involved in collecting, evaluating and analyzing this data, a system enabling process-driven data collection was developed. Based on generic tools, a process-driven approach for creating, processing and analyzing questionnaires was realized, in which a questionnaire is defined in terms of a process model. Due to this characteristic, process mining algorithms may be applied to event logs created during the execution of questionnaires. Moreover, new data that might not have been used in the context of questionnaires before may be collected and analyzed to provide new insights in regard to both the participant and the questionnaire.
This thesis shows that process mining algorithms may be applied successfully to process-oriented questionnaires. Algorithms from the three process mining forms of process discovery, conformance checking and enhancement are applied and used for various analysis. The analysis of certain properties of discovered process models leads to new ways of generating information from questionnaires. Different techniques for conformance checking and their applicability in the context of questionnaires are evaluated. Furthermore, new data that cannot be collected from paper-based questionnaires is used to enhance questionnaires to reveal new and meaningful relationships
Object detection in picking: Handling variety of a warehouse’s articles
No full textPurpose: The automation of picking is still a challenge as a high amount of flexibility is needed to handle different articles according to their requirements. Enabling robot picking in a dynamic warehouse environment consequently requires a sophisticated object detection system capable of handling a multitude of different articles.
Methodology: Testing the applicability of object detection approaches for logistics research started with few objects producing promising results. In the context of warehouse environments, the applicability of such approaches to thousands of different articles is still doubted. Using different approaches in parallel may enable handling a plethora of different articles as well as the maintenance of object detection approach in case of changes to articles or assortments occur.
Findings: Existing object detection algorithms are reliable if configured correctly. However, research in this field mostly focuses on a limited set of objects that need to be distinguished showing the functionality of the algorithm. Applying such algorithms in the context of logistics offers great potential, but also poses additional challenges. A huge variety of articles must be distinguished during picking, increasing complexity of the system with each article. A combination of different Convolutional Neural Networks may solve the problem.
Originality: The suitability of existing object detection algorithms originates from research on automation of established processes in existing warehouses. A process model was already introduced enabling the transformation of laboratory trained CNNs to industrial warehouses. Experiments with CNNs according to this approach are published now
Towards the Discovery of Object-Aware Processes
No full textThere has been a huge body of research in order to reduce manual efforts in creating executable process models through the automated discovery of process models from the event logs created by information systems. Regarding activity-centric processes, such event logs comprise case ids and events related to the execution of process activities. However, there exist alternative process management paradigms, such as object-aware processes, for which existing algorithms fail to discover a sound model. These algorithms do not treat data as first-class citizens, but solely rely on the information from event logs. In consequence, existing discovery algorithms are insufficient for discovering object-aware processes. To address this issue, discovery algorithms need to consider additional data sources (e.g., existing forms). This paper discusses the need for dedicated discovery techniques in object-aware processes
Towards Retrograde Process Analysis in Running Legacy Applications
No full textProcess mining algorithms are highly dependent on the existence and quality of event logs. In many cases, however, software systems (e.g., legacy systems) do not leverage workflow engines capable of producing high-quality event logs for process mining algorithms. As a result, the application of process mining algorithms is drastically hampered for such legacy systems. The generation of suitable event data from running legacy software systems, therefore, would foster approaches such as process mining, data-based process documentation, and process-oriented software migration of legacy systems. This paper discusses the need for dedicated event log generation approaches in this context
Towards Real-Time Progress Determination of Object-Aware Business Processes
No full textTo stay competitive, companies need to continuously improve and evolve their business processes. In this endeavour, business process optimisations and improvements are key elements. In particular, the monitoring of business processes enables the early discovery of problems and errors already during process enactment. Two approaches can be pursued to achieve this: real-time, also called online monitoring, and offline monitoring. A subtask of real-time monitoring is to determine the current progress of a business process, which is particularly challenging if the latter is composed of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to BPM, which result in large process structures. This position paper discusses the challenges of determining the progress of such object-aware processes in real-time and defines research questions that need to be investigated in further work
Data-Driven Evolution of Activity Forms in Object- and Process-Aware Information Systems
No full textAbstract. Object-aware processes enable the data-driven generation of forms based on the object behavior, which is pre-specified by the respective object lifecycle process. Each state of a lifecycle process comprises a number of object attributes that need to be set (e.g., via forms) before transitioning to the next state. When initially modeling a lifecycle process, the optimal ordering of the form fields is often unknown and only a guess of the lifecycle process modeler. As a consequence, certain form fields might be obsolete, missing, or ordered in a non-intuitive manner. Though this does not affect process executability, it decreases the usability of the automatically generated forms. Discovering respective problems, therefore, provides valuable insights into how object- and process-aware information systems can be evolved to improve their usability. This paper presents an approach for deriving improvements of object lifecycle processes by comparing the respective positions of the fields of the generated forms with the ones according to which the fields were actually filled by users during runtime. Our approach enables us to discover missing or obsolete form fields, and additionally considers the order of the fields within the generated forms. Finally, we can derive the modeling operations required to automatically restructure the internal logic of the lifecycle process states and, thus, to automatically evolve lifecycle processes and corresponding forms
Enabling Conformance Checking for Object Lifecycle Processes
No full textAbstract. In object-aware process management, processes are represented as multiple interacting objects rather than a sequence of activities, enabling data-driven and highly flexible processes. In such flexible scenarios, however, it is crucial to be able to check to what degree the process is executed according to the model (i.e., guided behavior). Conformance checking algorithms (e.g., Token Replay or Alignments) deal with this issue for activity-centric processes based on a process model (e.g., specified as a petri net) and a given event log that reflects how the process instances were actually executed. This paper applies conformance checking algorithms to the behavior of objects. In object-aware process management, object lifecycle processes specify the various states into which corresponding objects may transition as well as the object attribute values required to complete these states. The approach accounts for flexible lifecycle executions using multiple workflow nets and conformance categories, therefore facilitating process analysis for engineers
A Dashboard-based Approach for Monitoring Object-Aware Processes
No full textData (e.g., event logs) gathered during the execution of business processes enable valuable insights into actual process performance. To leverage this knowledge, these data should be analyzed and interpreted in the context of the respective processes. Corresponding analyses allow for a comprehensive process monitoring as well as the discovery
of weaknesses and potential process improvements. This also applies to object-aware processes, where data drives process execution and, thus, is treated as first-class citizen. This paper introduces a dashboard with advanced monitoring functions for object-aware processes
A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes
No full textReal-time monitoring of business processes offers promising perspectives to discover problems and optimisation potentials. Early detection is a key part in this endeavour. One crucial aspect of real-time monitoring is to determine the current progress of a running business process. This is particularly challenging for business processes that consist of a multitude of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to business process management. In this paper, an approach to predict the remaining portion of the process path to be still executed in relation to the overall process is proposed. This prediction is based on a one-dimensional Kalman Filter. As a major benefit of this approach, real-time progress determination can start directly with the first run of the process, i.e., without
need for comprehensive event log data. This becomes possible due to the procedure applied by the Kalman Filter, which requires no log data. A quantitative study with 250 progress estimations for large object lifecycle processes results in a deviation of the average estimated progress from the real progress, calculated after the completion of the process, of about 5%. This emphasises that reasonable progress predictions are possible even in the absence of an event log, as it is the case when deploying new or changed processes to the run-time system
