Model-Driven Engineering for Artificial Intelligence - A Systematic Literature Review

Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 703 candidate studies, eventually retaining 15 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study's findings show that the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM \textit{Business Understanding} phase, are rarely reflected. Conclusion: The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area

Message from the EDOC 2020 Workshop, Demo, and Doctoral Consortium Chairs

International audienceFor over twenty years the EDOC conference has been the primary annual event for disseminating and discussing the latest developments in the area of enterprise computing. The workshop program is an important satellite event of the EDOC conference. Workshops cover more focused topics and allow for the presentation and discussion of work that is in the earlier development stages. As such, the workshops provide an excellent forum for discussing topics from the area of enterprise computing that have the potential to become important research streams within the next few years, and for discussing topics that are already important in a smaller and more focused setting. In addition to the workshop program, the EDOC Demonstration track offers an exciting and highly interactive outlet for researchers and practitioners to present prototypes and applications in the context of enterprise computing. Four demonstrations are presented this year. 2020 is the year of the COVID-19 pandemic, with a strong impact on research and social life. Despite the challenges of new organization (the conference and the workshops will be held on-line), we are proud to present an interesting program and the four workshops that will be held in conjunction with EDOC this year:• The workshop on Trends in Enterprise Architecture Research, (TEAR)• The workshop on Service-oriented Enterprise Architecture for Enterprise Engineering (SoEA4EE)• The workshop on Privacy and Security in Enterprise Modeling (PriSEM)• The workshop on Frontiers of Process Aware Systems (FoPAS).[...

Transient Receptor Potential Vanilloid 6 (TRPV6) Proteins Control the Extracellular Matrix Structure of the Placental Labyrinth

Calcium-selective transient receptor potential Vanilloid 6 (TRPV6) channels are expressed in fetal labyrinth trophoblasts as part of the feto–maternal barrier, necessary for sufficient calcium supply, embryo growth, and bone development during pregnancy. Recently, we have shown a less- compact labyrinth morphology of Trpv6-deficient placentae, and reduced Ca2+ uptake of primary trophoblasts upon functional deletion of TRPV6. Trpv6-/- trophoblasts show a distinct calcium-dependent phenotype. Deep proteomic profiling of wt and Trpv6-/- primary trophoblasts using label-free quantitative mass spectrometry leads to the identification of 2778 proteins. Among those, a group of proteases, including high-temperature requirement A serine peptidase 1 (HTRA1) and different granzymes are more abundantly expressed in Trpv6-/- trophoblast lysates, whereas the extracellular matrix protein fibronectin and the fibronectin-domain-containing protein 3A (FND3A) were markedly reduced. Trpv6-/- placenta lysates contain a higher intrinsic proteolytic activity increasing fibronectin degradation. Our results show that the extracellular matrix formation of the placental labyrinth depends on TRPV6; its deletion in trophoblasts correlates with the increased expression of proteases controlling the extracellular matrix in the labyrinth during pregnancy

Social Network Mining from Natural Language Text and Event Logs for Compliance Deviation Detection

Social network mining aims at discovering and visualizing information exchange of resources and relations of resources among each other. For this, most existing approaches consider event logs as input data and therefore only depict how work was performed (as-is) and neglect information on how work should be performed (to-be), i.e., whether or not the actual execution is in compliance with the execution specified by the company or law. To bridge this gap, the presented approach considers event logs and natural language texts as input outlining rules on how resources are supposed to work together and which information may be exchanged between them. For pre-processing the natural language texts the large language model GPT-4 is utilized and its output is fed into a customized organizational mining component which delivers the to-be organizational perspective. In addition, we integrate well-known process discovery techniques from event logs to gather the as-is perspective. A comparison in the form of a graphical representation of both, the to-be and as-is perspectives, enables users to detect deviating behavior. The approach is evaluated based on a set of well-established process descriptions as well as synthetic and real-world event logs

Predicting Unseen Process Behavior Based on Context Information from Compliance Constraints

Predictive process monitoring (PPM) offers multiple benefits for enterprises, e.g., the early planning of resources. The success of PPM-based actions depends on the prediction quality and the explainability of the prediction results. Both, prediction quality and explainability, can be influenced by unseen behavior, i.e., events that have not been observed in the training data so far. Unseen behavior can be caused by, for example, concept drift. Existing approaches are concerned with strategies on how to update the prediction model if unseen behavior occurs. What has not been investigated so far, is the question how unseen behavior itself can be predicted, comparable to approaches from machine learning such as zero-shot learning. Zero-shot learning predicts new classes in case of unavailable training data by exploiting context information. This work follows this idea and proposes an approach to predict unseen process behavior, i.e., unseen event labels, based on process event streams by exploiting compliance constraints as context information. This is reasonable as compliance constraints change frequently and are often the cause for concept drift. The approach employs state transition systems as prediction models in order to explain the effects of predicting unseen behavior. The approach also provides update strategies as the event stream evolves. All algorithms are prototypically implemented and tested on an artificial as well as real-world data set

Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions

Business process compliance is a key area of business process management and aims at ensuring that processes obey to compliance constraints such as regulatory constraints or business rules imposed on them. Process compliance can be checked during process design time based on verification of process models and at runtime based on monitoring the compliance states of running process instances. For existing compliance monitoring approaches it remains unclear whether and how compliance violations can be predicted, although predictions are crucial in order to prepare and take countermeasures in time. This work, hence, analyzes existing literature from compliance and SLA monitoring as well as predictive process monitoring and provides an updated framework of compliance monitoring functionalities. For each compliance monitoring functionality we elicit prediction requirements and analyze their coverage by existing approaches. Based on this analysis, open challenges and research directions for predictive compliance and process monitoring are elaborated

Identification and Visualization of Legal Definitions and Legal Term Relations

Reading, analyzing, and implementing regulatory documents are cumbersome and still mostly manual tasks. The constantly increasing amount of regulatory documents causes an equally increasing need for supporting those tasks through information systems. One key aspect for accomplishing those tasks is to acquire knowledge of the different legal definitions, i.e., legal terms accompanied by their explanations, in order to build a legal vocabulary or an ontology. This paper proposes an approach taking European regulations as input and i) automatically extracting legal definitions, ii) determining semantic relations such as hyponyms, meronyms, and synonyms between legal terms, and iii) visualizing the results in form of a knowledge graph and statistics. The approach is evaluated on European regulations and made accessible particularly for non-technical users through an easy-to-use web service

Verifying Resource Compliance Requirements from Natural Language Text over Event Logs

Process compliance aims to ensure that processes adhere to requirements imposed by natural language texts such as regulatory documents. Existing approaches assume that requirements are available in a formalized manner using, e.g., linear temporal logic, leaving the question open of how to automatically extract and formalize them for verification. Especially with the constantly growing amount of regulatory documents and their frequent updates, it can be preferable to provide an approach that enables the verification of processes with requirements in natural language text instead of formalized requirements. To this end, this paper presents an approach that copes with the verification of resource compliance requirements, e.g., which resource shall perform which activity, in natural language over event logs. The approach relies on a comprehensive literature analysis to identify resource compliance patterns. It then contrasts these patterns with resource patterns reflecting the process perspective, while considering the natural language perspective. We combine the state-of-the-art GPT-4 technology for pre-processing the natural language text with a customized compliance verification component to identify and verify resource compliance requirements. Thereby, the approach distinguishes different resource patterns including multiple organizational perspectives. The approach is evaluated based on a set of well-established process descriptions and synthesized event logs generated by a process execution engine as well as the BPIC 2020 dataset

Message from the EDOC 2020 Workshop, Demo, and Doctoral Consortium Chairs

International audienceFor over twenty years the EDOC conference has been the primary annual event for disseminating and discussing the latest developments in the area of enterprise computing. The workshop program is an important satellite event of the EDOC conference. Workshops cover more focused topics and allow for the presentation and discussion of work that is in the earlier development stages. As such, the workshops provide an excellent forum for discussing topics from the area of enterprise computing that have the potential to become important research streams within the next few years, and for discussing topics that are already important in a smaller and more focused setting. In addition to the workshop program, the EDOC Demonstration track offers an exciting and highly interactive outlet for researchers and practitioners to present prototypes and applications in the context of enterprise computing. Four demonstrations are presented this year. 2020 is the year of the COVID-19 pandemic, with a strong impact on research and social life. Despite the challenges of new organization (the conference and the workshops will be held on-line), we are proud to present an interesting program and the four workshops that will be held in conjunction with EDOC this year:• The workshop on Trends in Enterprise Architecture Research, (TEAR)• The workshop on Service-oriented Enterprise Architecture for Enterprise Engineering (SoEA4EE)• The workshop on Privacy and Security in Enterprise Modeling (PriSEM)• The workshop on Frontiers of Process Aware Systems (FoPAS).[...