APQL: A process-model query language

As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation

The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges

Corpus Statistics for Measuring Business Process Similarity

In a rapidly changing environment, organizations must adapt their business processes continuously. While numerous methods enable enterprises to conceptualize and analyze their organizational structure, the task of business process modeling remains complex and time-consuming. However, by reusing and adapting existing process models, enterprises can reduce the task’s complexity while improving the quality of results. To facilitate the identification of adaptable processes, several techniques of business process similarity (BPS) have been proposed in recent years. Although most approaches produce sound results in controlled evaluations, this paper argues that their applicability is limited when analyzing real-world processes, which do not fully comply with notational labeling specifications. Consequently, we aim to enhance existing BPS techniques by using corpus statistics to account for the explanatory power of words within labels of process models. Results from our evaluation suggest that corpus statistics can improve BPS computations and can positively influence the quality of practical implications

A Cross-Organizational Process Mining Framework for Obtaining Insights from Software Products: Accurate Comparison Challenges

Software vendors offer various software products to large numbers of enterprises to support their organization, in particular Enterprise Resource Planning (ERP) software. Each of these enterprises use the same product for similar goals, albeit with different processes and configurations. Therefore, software vendors want to obtain insights into how the enterprises use the software product, what the differences are in usage between enterprises, and the reasons behind these differences. Cross-organizational process mining is a possible solution to address these needs, as it aims at comparing enterprises based on their usage. In this paper, we present a novel Cross-Organizational Process Mining Framework which takes as input, besides event log, semantics (meaning of terms in an enterprise) and organizational context (characteristics of an enterprise). The framework provides reasoning capabilities to determine what to compare and how. Besides, the framework enables one to create a catalog of metrics by deducing diagnostics from the usage. By using this catalog, the framework can monitor the (positive) effects of changes on processes. An enterprise operating in a similar context might also benefit from the same changes. To accommodate these improvement suggestions, the framework creates an improvement catalog of observed changes. Later, we provide a set of challenges which have to be met in order to obtain the inputs from current products to show the feasibility of the framework. Next to this, we provide preliminary results showing they can be met and illustrate an example application of the framework in cooperation with an ERP software vendor

IT-gestütztes Compliance Management für Geschäftsprozesse

Die Einhaltung regulatorischer Anforderungen und interner Richtlinien ist zunehmend kritisch für den Unternehmenserfolg geworden. In dieser Arbeit wird ein Ansatz vorgestellt, der eine effiziente Compliance-Prüfung von Geschäftsprozessen basierend auf den in standardisierten Ereignisprotokollen aufgezeichneten Ereignisdaten ermöglicht. Neben einer Referenzimplementierung des Ansatzes werden eine Erweiterung für ein Geschäftsprozessmanagementwerkzeug sowie ein webbasiertes Dashboard entwickelt

A Cross-Organizational Process Mining Framework for Obtaining Insights from Software Products: Accurate Comparison Challenges

Software vendors offer various software products to large numbers of enterprises to support their organization, in particular Enterprise Resource Planning (ERP) software. Each of these enterprises use the same product for similar goals, albeit with different processes and configurations. Therefore, software vendors want to obtain insights into how the enterprises use the software product, what the differences are in usage between enterprises, and the reasons behind these differences. Cross-organizational process mining is a possible solution to address these needs, as it aims at comparing enterprises based on their usage. In this paper, we present a novel Cross-Organizational Process Mining Framework which takes as input, besides event log, semantics (meaning of terms in an enterprise) and organizational context (characteristics of an enterprise). The framework provides reasoning capabilities to determine what to compare and how. Besides, the framework enables one to create a catalog of metrics by deducing diagnostics from the usage. By using this catalog, the framework can monitor the (positive) effects of changes on processes. An enterprise operating in a similar context might also benefit from the same changes. To accommodate these improvement suggestions, the framework creates an improvement catalog of observed changes. Later, we provide a set of challenges which have to be met in order to obtain the inputs from current products to show the feasibility of the framework. Next to this, we provide preliminary results showing they can be met and illustrate an example application of the framework in cooperation with an ERP software vendor

Exploring the Integration of Agent-Based Modelling, Process Mining, and Business Process Management through a Text Analytics–Based Literature Review

Agent-based modelling and business process management are two interrelated yet distinct concepts. To explore the relationship between these two fields, we conducted a systematic literature review to investigate existing methods and identify research gaps in the integration of agent-based modelling, process mining, and business process management. Our search yielded 359 research papers, which were evaluated using predefined criteria and quality measures. This resulted in a final selection of forty-two papers. Our findings reveal several research gaps, including the need for enhanced validation methods, the modelling of complex agents and environments, and the integration of process mining and business process management with emerging technologies. Existing agent-based approaches within process mining and business process management have paved the way for identifying the validation methods for performance evaluation. The addressed research gaps primarily concern validation before delving deeper into specific research topics. These include improved validation methods, modelling of complex agents and environments, and a preliminary exploration of integrating process mining and business process management with emerging technologies

Knowledge graph embedding enhancement using ontological knowledge in the biomedical domain

The biomedical field is a critical area for natural language processing (NLP) applications because it involves a vast amount of unstructured data, including clinical notes, medical publications, and electronic health records. NLP techniques can help extract valuable information from these documents, such as disease symptoms, medication usage, and treatment outcomes, which can improve patient care and clinical decision-making. MAPS S.p.A. currently produces Clinika, a software that extracts knowledge from clinical corpora. Clinika performs the task of Named Entity Recognition (NER) by linking entities to medical concepts from an established knowledge base, in this case, the Unified Medical Language System (UMLS). This dissertation details how we approached designing and implementing a component for the new version of Clinika, specifically a mention embedder that uses embeddings to perform entity linking with UMLS concepts. We focused on enhancing existing dense contextual embeddings by injecting ontological knowledge, using two parallel approaches: (1) taking the embeddings as a by-product of an entity alignment model aided by an ontology, and (2) fine-tuning a contextual language model with custom sampling strategies. We evaluated both approaches with suitable experiments from the relevant literature. After testing, we discontinued the first approach but found more significant results using the second. The results on the tasks chosen to evaluate the embeddings were not promising, we address the causes in the Error Analysis section, and discuss further work on this topic

Robust query processing for linked data fragments

Linked Data Fragments (LDFs) refer to interfaces that allow for publishing and querying Knowledge Graphs on the Web. These interfaces primarily differ in their expressivity and allow for exploring different trade-offs when balancing the workload between clients and servers in decentralized SPARQL query processing. To devise efficient query plans, clients typically rely on heuristics that leverage the metadata provided by the LDF interface, since obtaining fine-grained statistics from remote sources is a challenging task. However, these heuristics are prone to potential estimation errors based on the metadata which can lead to inefficient query executions with a high number of requests, large amounts of data transferred, and, consequently, excessive execution times. In this work, we investigate robust query processing techniques for Linked Data Fragment clients to address these challenges. We first focus on robust plan selection by proposing CROP, a query plan optimizer that explores the cost and robustness of alternative query plans. Then, we address robust query execution by proposing a new class of adaptive operators: Polymorphic Join Operators. These operators adapt their join strategy in response to possible cardinality estimation errors. The results of our first experimental study show that CROP outperforms state-of-the-art clients by exploring alternative plans based on their cost and robustness. In our second experimental study, we investigate how different planning approaches can benefit from polymorphic join operators and find that they enable more efficient query execution in the majority of cases