Skip to main content
main-content

2022 | Buch

Research Challenges in Information Science

16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings

share
TEILEN
insite
SUCHEN

Über dieses Buch

This book constitutes the proceedings of the 16th International Conference on Research Challenges in Information Sciences, RCIS 2022, which took place in Barcelona, Spain, during May 17–20, 2022. It focused on the special theme "Ethics and Trustworthiness in Information Science".

The scope of RCIS is summarized by the thematic areas of information systems and their engineering; user-oriented approaches; data and information management; business process management; domain-specific information systems engineering; data science; information infrastructures, and reflective research and practice.

The 35 full papers presented in this volume were carefully reviewed and selected from a total 100 submissions. The 18 Forum papers are based on 11 Forum submissions, from which 5 were selected, and the remaining 13 were transferred from the regular submissions. The 6 Doctoral Consortium papers were selected from 10 submissions to the consortium. The contributions were organized in topical sections named: Data Science and Data Management; Information Search and Analysis; Business Process Management; Business Process Mining; Digital Transformation and Smart Life; Conceptual Modelling and Ontologies; Requirements Engineering; Model-Driven Engineering; Machine Learning Applications. In addition, two-page summaries of the tutorials can be found in the back matter.

Inhaltsverzeichnis

Frontmatter

Data Science and Data Management

Frontmatter
Personal Data Markets: A Narrative Review on Influence Factors of the Price of Personal Data

Personal data has been described as the “the new oil of the Internet.” The global data monetization market is projected to increase to USD 6.1bn by 2025, and the success of giants like Facebook or Google speaks for itself. Almost all companies create, store, share and/or use personal data i.e. information from or about individuals. While the current assumption is that data subjects voluntarily share their data in exchange for a “free” service, the awareness of the value of personal data and data sovereignty is growing amongst consumers, businesses, and regulators alike. However, there is currently no consensus on which factors influence the value of personal data and how personal data should be priced regarding self-determination and data sovereignty. With this narrative review, we answer the following research question: Which factors influence the pricing of personal data? We show that research on the subject is diverse and that there is no consensus on the optimal pricing mechanism. We identify individual privacy and risk preferences, informational self-determination, sensitivity of data and data volume and inferability as most prevalent influence factors. We underline the need to establish ways for data owners to exercise data sovereignty and informed consent about data usage.

Julia Busch-Casler, Marija Radic
What’s in a (Data) Type? Meaningful Type Safety for Data Science

Data science incorporates a variety of processes, concepts, techniques and domains, to transform data that is representative of real-world phenomena into meaningful insights and to inform decision-making. Data science relies on simple datatypes like strings and integers to represent complex real-world phenomena like time and geospatial regions. This reduction of semantically rich types to simplistic ones creates issues by ignoring common and significant relationships in data science including time, mereology, and provenance. Current solutions to this problem including documentation standards, provenance tracking, and knowledge model integration are opaque, lack standardization, and require manual intervention to validate. We introduce the meaningful type safety framework (MeTS) to ensure meaningful and correct data science through semantically-rich datatypes based on dependent types. Our solution encodes the assumptions and rules of common real-world concepts, such as time, geospatial regions, and populations, and automatically detects violations of these rules and assumptions. Additionally, our type system is provenance-integrated, meaning the type environment is updated with every data operation. To illustrate the effectiveness of our system, we present a case study based on real-world datasets from Statistics Canada (StatCAN). We also include a proof-of-concept implementation of our system in the Idris programming language.

Riley Moher, Michael Gruninger, Scott Sanner
Research Data Management in the Image Lifecycle: A Study of Current Behaviors

Research data management (RDM) practices are critical for ensuring research success. Data can assume diverse formats and data in image format have been understudied in RDM. To understand image management habits in research, we have conducted semi-structured interviews with researchers from four research domains. Most researchers do not formally manage their images, nor do they develop RDM plans. They assume that image management is not a topic discussed at project meetings. In turn, they tend to perform some individual practices, depending on the context and their own opinion, such as creating captions to describe the images and organizing and storing the images in specific locations. However, they see these habits as necessary and admit that they will start to do so in a formal and collaborative way with the working group. These results provide valuable information on practical aspects of the use and production of images in research.

Joana Rodrigues, Carla Teixeira Lopes

Information Search and Analysis

Frontmatter
Towards an Arabic Text Summaries Evaluation Based on AraBERT Model

The evaluation of text summaries remains a challenging task despite the large number of studies in this field for more than two decades. This paper describes an automatic method for assessing Arabic text summaries. In fact, the proposed method will predict the “Overall Responsiveness” manual score, which is a combination of the content and the linguistic quality of a summary. To predict this manual score, we aggregate, with a regression function, three types of features: lexical similarity features, semantic similarity features and linguistic features. Semantic features include multiple semantic similarity scores based on Bert model. While linguistic features are based on the calculation of entropy scores. To calculate the similarity between a candidate summary and a reference summary, we begin by doing an exact match between n-grams. For the unmatched n-grams, we present them as Bert vectors, and then we compute the similarity between Bert vectors. The proposed method yielded competitive results compared to metrics based on lexical similarity such as ROUGE.

Samira Ellouze, Maher Jaoua
A Recommender System for EOSC. Challenges and Possible Solutions

European Open Science Cloud (EOSC) is a pan-European environment providing researchers with a plethora or publicly-available, open resources and services to help them conduct their research. Availability of publications, datasets, computational power, networks or storage allows researchers to concentrate on their research rather than the technical infrastructures. However, the plenitude and diversity of items offered in EOSC increases and becomes overwhelming for researchers who expect guidance and support. Recommender systems allow them to assign rankings to subject object, based on their value for specific end users, inferred from diverse data about them, their behaviour or various relationships between users and objects. In this paper we present architectural and functional challenges related to the EOSC Recommender System that could substantially improve the experience of researchers using EOSC offerings.

Marcin Wolski, Krzysztof Martyn, Bartosz Walter
ERIS: An Approach Based on Community Boundaries to Assess Polarization in Online Social Networks

Detection and characterization of polarization are of major interest in Social Network Analysis, especially to identify conflictual topics that animate the interactions between users. As gatekeepers of their community, users in the boundaries significantly contribute to its polarization. We propose ERIS, a formal graph approach relying on community boundaries and users’ interactions to compute two metrics: the community antagonism and the porosity of boundaries. These values assess the degree of opposition between communities and their aversion to external exposure, allowing an understanding of the overall polarization through the behaviors of the different communities. We also present an implementation based on matrix computations, freely available online. Our experiments show a significant improvement in terms of efficiency in comparison to existing solutions. Finally, we apply our proposal on real data harvested from Twitter with a case study about the vaccines and the COVID-19.

Alexis Guyot, Annabelle Gillet, Éric Leclercq, Nadine Cullot

Business Process Management

Frontmatter
Progress Determination of a BPM Tool with Ad-Hoc Changes: An Empirical Study

One aspect of monitoring business processes in real-time is to determine their current progress. For any real-time progress determination it is of utmost importance to accurately predict the remaining share still to be executed in relation to the total process. At run-time, however, this constitutes a particular challenge, as unexpected ad-hoc changes of the ongoing business processes may occur at any time. To properly consider such changes in the context of progress determination, different progress variants may be suitable. In this paper, an empirical study with 194 participants is presented that investigates user acceptance of different progress variants in various scenarios. The study aims to identify which progress variant, each visualised by a progress bar, is accepted best by users in case of dynamic process changes, which usually effect the current progress of the respective progress instance. The results of this study allow for an implementation of the most suitable variant in business process monitoring systems. In addition, the study provides deeper insights into the general acceptance of different progress measurements. As a key observation for most scenarios, the majority of the participants give similar answers, e.g., progress jumps within a progress bar are rejected by most participants. Consequently, it can be assumed that a general understanding of progress exists. This underlines the importance of comprehending the users’ intuitive understanding of progress to implement the latter in the most suitable fashion.

Lisa Arnold, Marius Breitmayer, Manfred Reichert
Enabling Conformance Checking for Object Lifecycle Processes

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.

Marius Breitmayer, Lisa Arnold, Manfred Reichert
A Framework to Improve the Accuracy of Process Simulation Models

Business process simulation is a methodology that enables analysts to run the process in different scenarios, compare the performances and consequently provide indications into how to improve a business process. Process simulation requires one to provide a simulation model, which should accurately reflect reality to ensure the reliability of the simulation findings. This paper proposes a framework to assess the extent to which a simulation model reflects reality and to pinpoint how to reduce the distance. The starting point is a business simulation model, along with a real event log that records actual executions of the business process being simulated and analyzed. In a nutshell, the idea is to simulate the process, thus obtaining a simulation log, which is subsequently compared with the real event log. A decision tree is built, using the vector of features that represent the behavioral characteristics of log traces. The tree aims to classify traces as belonging to the real and simulated event logs, and the discriminating features encode the difference between reality, represented in the real event log, and the simulation model, represented in the simulated event logs. These features provide actionable insights into how to repair simulation models to become closer to reality. The technique has been assessed on a real-life process for which the literature provides a real event log and a simulation model. The results of the evaluation show that our framework increases the accuracy of the given initial simulation model to better reflect reality.

Francesca Meneghello, Claudia Fracca, Massimiliano de Leoni, Fabio Asnicar, Alessandro Turco
Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations

Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process performance. The contributions in the literature providing support to the tracking and impact analysis of small-scale changes are limited in scope and functionality. In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. More in detail, we model the updates using the configuration of DTOs and quantitatively assess different types of impacts of information system updates (structural, operational, and performance-related). We implemented a prototype of the proposed approach. Moreover, we discuss a case study involving a standard ERP procure-to-pay business process.

Gyunam Park, Marco Comuzzi, Wil M. P. van der Aalst
Hybrid Business Process Simulation: Updating Detailed Process Simulation Models Using High-Level Simulations

Process mining techniques transfer historical data of organizations into knowledge for the purpose of process improvement. Most of the existing process mining techniques are “backward-looking” and provide insights w.r.t. historical event data. Foreseeing the future of processes and capturing the effects of changes without applying them to the real processes are of high importance. Current simulation techniques that benefit from process mining insights are either at detailed levels, e.g., Discrete Event Simulation (DES), or at aggregated levels, e.g., System Dynamics (SD). System dynamics represents processes at a higher degree of aggregation and accounts for the influence of external factors on the process. In this paper, we propose an approach for simulating business processes that combines both types of data-driven simulation techniques to generate holistic simulation models of processes. These techniques replicate processes at various levels and for different purposes, yet they both present the same process. SD models are used for strategical what-if analysis, whereas DES models are used for operational what-if analysis. It is critical to consider the effects of strategical decisions on detailed processes. We introduce a framework integrating these two simulation models, as well as a proof of concept to demonstrate the approach in practice.

Mahsa Pourbafrani, Wil M. P. van der Aalst

Business Process Mining

Frontmatter
Towards Event Log Management for Process Mining - Vision and Research Challenges

Organizations act in dynamic and constantly changing business environments, as the current times unfortunately illustrate. As a consequence, business processes need to be able to constantly adapt to new realities. While the dynamic nature of business processes is hardly ever challenged, the complexity of processes and the information systems (IS) supporting them make effective business process management (BPM) a challenging task. Process mining (PM) is a maturing field of data-driven process analysis techniques that addresses this challenge. PM techniques take event logs as input to extract process-related knowledge, such as automatically discovering and visualizing process models. The popularity of PM applications is growing in both industry and academia and the integration of PM with machine learning, simulation and other complementary trends, such as Digital Twins of an Organization, is gaining significant attention. However, the success of PM is directly related to the quality of the input event logs, thus the need for high-quality event logs is evident. While a decade ago the PM manifesto already stressed the importance of high-quality event logs, stating that event data should be treated as first-class citizens, event logs are often still considered as “by-products” of an IS. Even within the PM research domain, research on event logs is mostly focused on ad-hoc preparation techniques and research on event log management is critically lacking. This paper addresses this research gap by positioning event logs as first-class citizens through the lens of an event log management framework, presenting current challenges and areas for future research.

Ruud van Cruchten, Hans Weigand

Open Access

Process Mining for Process Improvement - An Evaluation of Analysis Practices

Organizations have a vital interest in continuously improving their business processes. Process analysts can use process mining tools that provide data-driven discovery and analysis of business processes to achieve this. Current research has mainly focused on creating and evaluating new tools or reporting process mining case studies from different domains. Although usage of process mining has increased in industry, insights into how analysts work with such methods to identify improvement opportunities have consequently been limited. To reduce this gap, we conducted an exploratory interview study of seven process analysts from different domains. Our findings indicate that process analysts assess improvement opportunities by their impact, the feasibility of required implementation, and stakeholders’ input. Furthermore, our results indicate that process mining tools, when used to identify improvement opportunities, do not provide sufficient support for analysis, requiring process analysts to use additional tools. Lastly, analysts use storytelling to frame and communicate their findings to various stakeholders.

Kateryna Kubrak, Fredrik Milani, Alexander Nolte

Open Access

Data-Driven Analysis of Batch Processing Inefficiencies in Business Processes

Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.

Katsiaryna Lashkevich, Fredrik Milani, David Chapela-Campa, Marlon Dumas
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection

Anomaly detection in process mining focuses on identifying anomalous cases or events in process executions. The resulting diagnostics are used to provide measures to prevent fraudulent behavior, as well as to derive recommendations for improving process compliance and security. Most existing techniques focus on detecting anomalous cases in an offline setting. However, to identify potential anomalies in a timely manner and take immediate countermeasures, it is necessary to detect event-level anomalies online, in real-time. In this paper, we propose to tackle the online event anomaly detection problem using next-activity prediction methods. More specifically, we investigate the use of both ML models (such as RF and XGBoost) and deep models (such as LSTM) to predict the probabilities of next-activities and consider the events predicted unlikely as anomalies. We compare these predictive anomaly detection methods to four classical unsupervised anomaly detection approaches (such as Isolation forest and LOF) in the online setting. Our evaluation shows that the proposed method using ML models tends to outperform the one using a deep model, while both methods outperform the classical unsupervised approaches in detecting anomalous events.

Suhwan Lee, Xixi Lu, Hajo A. Reijers

Open Access

Process Mining: A Guide for Practitioners

In the last years, process mining has significantly matured and has increasingly been applied by companies in industrial contexts. However, with the growing number of process mining methods, practitioners might find it difficult to identify which ones to apply in specific contexts and to understand the specific business value of each process mining technique. This paper’s main objective is to develop a business-oriented framework capturing the main process mining use cases and the business-oriented questions they can answer. We conducted a Systematic Literature Review (SLR) and we used the review and the extracted data to develop a framework that (1) classifies existing process mining use cases connecting them to specific methods implementing them, and (2) identifies business-oriented questions that process mining use cases can answer. Practitioners can use the framework to navigate through the available process mining use cases and to identify the process mining methods suitable for their needs.

Fredrik Milani, Katsiaryna Lashkevich, Fabrizio Maria Maggi, Chiara Di Francescomarino

Digital Transformation and Smart Life

Frontmatter
How Do Startups and Corporations Engage in Open Innovation? An Exploratory Study

In recent years, the increasing market pressure and disruption have driven firms to undertake digital transformations to create value and deliver better products for customers. Large corporations frequently face difficulties to digitalize their internal processes. They have well-established and mature routines that are hard to change. By contrast, startups are recognized for their innovation capacity and agile processes. In the quest for speed and innovation, corporations are engaging with startups to match complementary goals. Corporations desire the creative potential of startups, while startups need resources that are plentiful in corporations. This paper explores how open innovation is performed from the perspective of startups and corporations. We conducted an exploratory interview study at eight startups and five corporations to understand the dynamics of their relationships during open innovation initiatives. Our results reveal the main drivers, benefits, and challenges involved in the engagement between startups and corporations. Finally, we present a set of recommendations to foster startup-corporation relationships.

Maria Cecília Cavalcanti Jucá, Carina Frota Alves
Smart Life: Review of the Contemporary Smart Applications

Research efforts in various fields related to Smart life increase constantly. There are very well-established fields like Smart home, Smart city, and Smart grid, and more emergent ones, like Smart farming, Smart university, and Smart tourism. Smart life intends to enhance human life and serves as an umbrella term for all Smart topics. However, the research domain of Smart life is very diverse and multifaceted. Our main goal is to systematize the existing research work around Smart life to provide direction for the development and maintenance of Smart artefacts and applications, thus, to move towards Smart life engineering. To achieve this goal, a mandatory step is to understand and organize all the different Smart topics studied in the scientific literature by means of a systematic mapping study. We analyzed 2341 existing Smart state-of-the-art works and research agendas. We propose a taxonomy of Smart applications, study their evolution over time, analyze their venues, specific terminology, and driving factors. The resulting overview is useful to researchers and practitioners to improve the positioning of their work and to identify the research opportunities in all types of smartness for humans, organizations, and society.

Elena Kornyshova, Rebecca Deneckère, Kaoutar Sadouki, Eric Gressier-Soudan, Sjaak Brinkkemper

Conceptual Modelling and Ontologies

Frontmatter
Conceptual Integration for Social-Ecological Systems
An Ontological Approach

Sustainability research and policy rely on complex data that couples social and ecological systems (SESs) to draw results and make decisions, therefore understanding the dynamics between human society and natural ecosystems is crucial to tackle sustainability goals. SESs frameworks are employed to establish a common vocabulary that facilitates the identification of variables and the comparison of results. A variety of SESs approaches have been proposed and explored, however integration and interoperability between frameworks is missing, which results in a loss of relevant information. In addition, SESs frameworks often lack semantic clarity which exacerbates difficulties in developing a unified perspective. In this paper we demonstrate the use of ontological analysis to unify the main elements of two prominent SESs paradigms, the social-ecological system framework (SESF) and the Ecosystem Services (ESs) approach, to build an integrated social-ecological perspectives framework. The proposed conceptual framework can be adopted to combine existent and future results from the two paradigms in unified databases and to develop broader explanatory and decision-making tools for SESs and sustainability research.

Greta Adamo, Max Willis
Contratto – A Method for Transforming Legal Contracts into Formal Specifications

Legal contracts have been used for millennia to conduct business transactions world-wide. Such contracts are expressed in natural language, and usually come in written form. We are interested in producing formal specifications from such legal text that can be used to formally analyze contracts, also serve as launching pad for generating smart contracts, information systems that partially automate, monitor and control the execution of legal contracts. We have been developing a method for transforming legal contract documents into specifications, adopting a semantic approach where transformation is treated as a text classification, rather than a natural language processing problem. The method consists of five steps that (a) Identify domain terms in the contract and manually disambiguate them when necessary, in consultation with stakeholders; (b) Semantically annotate text identifying obligations, powers, contracting parties, assets and situations; (c) Identify relationships among the concepts mined in (b); (d) Generate a domain model based on the terms identified in (a), as well as parameters and local variables for the contract; (e) Generate expressions that formalize the conditions of obligations and powers using terms identified in earlier steps in a contract specification language. This paper presents the method through an illustrative example, also reports on a prototype implementation of an environment that supports the method.

Michele Soavi, Nicola Zeni, John Mylopoulos, Luisa Mich
Ontological Foundations for Trust Dynamics: The Case of Central Bank Digital Currency Ecosystems

In recent years, disruptive technologies have advanced at a rapid pace. These new developments have the power to accelerate the production and delivery, improve the quality, and reduce the costs of goods and services, as well as to contribute to individual and collective well-being. However, their adoption relies largely on user trust. And trust, due to its dynamic nature, is fragile. Therefore, just as important as to build trust is to maintain it. To build sustainable trust it is fundamental to understand the composition of trust relations and what factors can influence them. To address this issue, in this paper, we provide ontological foundations for trust dynamics. We extend our previous work, the Reference Ontology of Trust (ROT), to clarify and provide a deeper account of some building blocks of trust as well as the many factors that can influence trust relations. We illustrate the working of ROT by applying it to a real case study concerning citizens’ trust in central bank digital currency ecosystems, which has been conducted in close collaboration with a national central bank.

Glenda Amaral, Tiago Prince Sales, Giancarlo Guizzardi
Abstracting Ontology-Driven Conceptual Models: Objects, Aspects, Events, and Their Parts

Ontology-driven conceptual models are widely used to capture information about complex and critical domains. Therefore, it is essential for these models to be comprehensible and cognitively tractable. Over the years, different techniques for complexity management in conceptual models have been suggested. Among these, a prominent strategy is model abstraction. This work extends an existing strategy for model abstraction of OntoUML models that proposes a set of graph-rewriting rules leveraging on the ontological semantics of that language. That original work, however, only addresses a set of the ontological notions covered in that language. We review and extend that rule set to cover more generally types of objects, aspects, events, and their parts.

Elena Romanenko, Diego Calvanese, Giancarlo Guizzardi
Understanding and Modeling Prevention

Prevention is a pervasive phenomenon. It is about blocking an effect before it happens or stopping it as it unfolds: vaccines prevent (the unfolding of) diseases; seat belts prevent events causing serious injuries; circuit breaks prevent the manifestation of overcurrents. Many disciplines in the information sciences deal with modeling and reasoning about prevention. Examples include risk and security management as well as medical and legal informatics. Having a proper conceptualization of this phenomenon is crucial for devising proper modeling mechanisms and tools to support these disciplines. Forming such a conceptualization is a matter of Formal Ontology. In fact, prevention and related notions have become a topic of interest in this area. In this paper, with the support of Unified Foundational Ontology (UFO), we conduct an ontological analysis of this and other related notions, namely, the notions of countermeasures and countermeasure mechanisms, including the notion of antidotes. As a result of this conceptual clarification process, we propose an ontology-based reusable module extending UFO and capturing the relations between these elements. Finally, we employ this module to address a few cases in risk management.

Riccardo Baratella, Mattia Fumagalli, Ítalo Oliveira, Giancarlo Guizzardi

Requirements Engineering

Frontmatter
Requirements Engineering for Collaborative Artificial Intelligence Systems: A Literature Survey

Artificial Intelligence (AI) systems are pervasively exploited to manipulate large sets of data, support data-driven decisions, as well as to replace or collaborate with humans in performing boring tasks that require high level precision. Awareness of the need of engineering approaches that align with ethical principles is increasing and motivates attention by diverse research communities, such as AI research and Software Engineering research communities.In our research, we focus on Requirements Engineering (RE) for Collaborative Artificial Intelligence Systems (CAIS), such as robot arms that collaborate with human operators to perform repetitive and tiring tasks. A systematic literature review was conducted to assess the state of research, which resulted in the analysis of 41 research publications. Among the main findings, a set of challenges pointed out by researchers, such as the lack of a well-structured definition for CAIS requirements and the inadequacy of current standards. A discussion of these challenges and of recommendations for addressing them is proposed, taking into account similar results from recent related work. Similarly, the requirements types mentioned in the analysed literature are analysed according to categories proposed in related work.

Lawrence Araa Odong, Anna Perini, Angelo Susi
On the Current Practices for Specifying Sustainability Requirements

As sustainability becomes a fundamental concern in software development, it is important to understand how industry is addressing it. This paper discusses the results of a survey performed in industry aiming at identifying their current needs and practices to handle sustainability in agile software development. The survey includes an initial section to gather participants’ information, followed by a section inquiring about the impact of sustainability on their working environment, and which methods and tools are used. The enquired population is a small subset of the IT professionals in Portugal. The main findings include lack of methods, tools, knowledge and domain experts to support elicitation and specification of sustainability requirements. Still, the participants recognise that one of the main reasons to consider sustainability is for the improvement of product quality and for creating a good reputation.

Salvador Mendes, João Araujo, Ana Moreira
Defining Key Concepts in Information Science Research: The Adoption of the Definition of Feature

This paper analyzes the definitions of the concept feature in the information science literature. The concept of feature has been defined in various ways over the last three decades. To be able to obtain a common understanding of a feature in information science, it is important to conduct a thorough analysis of the definitions that can be used in research and in practice. The main contribution of this paper is a categorization of the existing definitions, which highlights similarities and differences. By means of a Concept Definition Review process, we gather a total of 23 definitions from Google Scholar using five search queries complemented by backward snowballing. Our analysis organizes the definitions according to their level of abstraction and the taken viewpoint. Within the range of analyzed definitions, we do not wish to argue that one is better or worse than another. We provide, however, guidelines for the selection of a definition for a given goal. These guidelines include: popularity based on the citations count, the research field, the abstraction level, and the viewpoint.

Sabine Molenaar, Emilie Steenvoorden, Nikita van den Berg, Fabiano Dalpiaz, Sjaak Brinkkemper
Assisted-Modeling Requirements for Model-Driven Development Tools

Model-driven development (MDD) tools allow software development teams to increase productivity and decrease software time-to-market. Although several MDD tools have been proposed, they are not commonly adopted by software development practitioners. Some authors have noted MDD tools are poorly adopted due to a lack of user assistance during modeling-related tasks. This has led model-driven engineers—i.e., engineers who create MDD tools—to equip MDD tools with intelligent assistants, wizards for creating models, consistency checkers, and other modeling assistants to address such assist-modeling-related issues. However, is this the way MDD users expect to be assisted during modeling in MDD tools? Therefore, we plan and conduct two focus groups with MDD users. We extract data around three main research questions: i) what are the challenges perceived by MDD users during modeling for later code generation? ii) what are the features of the current modeling assistants that users like/dislike? and iii) what are the user’s needs that are not yet satisfied by the current modeling assistants? As a result, we gather requirements from the MDD users’ perspective on how they would like to be assisted while using MDD tools. We propose an emerging framework for assisting MDD users during modeling based on such requirements. In addition, we outline future challenges and research efforts for next-generation MDD tools.

David Mosquera, Marcela Ruiz, Oscar Pastor, Jürgen Spielberger

Model-Driven Engineering

Frontmatter
Model-Driven Production of Data-Centric Infographics: An Application to the Impact Measurement Domain

Context and motivation: Infographics are an engaging medium for communication. Sometimes, organisations create several infographics with the same graphic design and different data; e.g., when reporting on impact measurement. Question/problem: The conventional process to produce such recurrent data-centric infographics causes rework related to the disconnection between software environments. Principal ideas/results: This paper redesigns the process following the model-driven engineering paradigm. We present a domain-specific language to model infographics, and an interpreter that generates the infographics automatically. We have been able to model and generate infographics that report impact measurement results, which the participants of a comparative experiment have found as attractive as the original ones, and that are hard, but not impossible, to distinguish from them. Contribution: An innovative model-driven approach that eliminates the software environment disconnection and could facilitate the use of data-centric infographics for reporting purposes.

Sergio España, Vijanti Ramautar, Sietse Overbeek, Tijmen Derikx
The B Method Meets MDE: Review, Progress and Future
Review, Progress and Future

Existing surveys about language workbenches (LWBs) ranging from 2006 to 2019 observe a poor usage of formal methods within domain-specific languages (DSLs) and call for identifying the reasons. We believe that the lack of automated formal reasoning in LWBs, and more generally in MDE, is not due to the complexity of formal methods and their mathematical background, but originates from the lack of initiatives that are dedicated to the integration of existing tools and techniques. To this aim we developed the Meeduse LWB and investigated the use of the B method to rigorously define the semantics of DSLs. The current applications of Meeduse show that the integration of DSLs together with theorem proving, animation and model-checking is viable and should be explored further. This technique is especially interesting for executable DSLs (xDSLs), which are DSLs with behavioural features. This paper is a review of our Formal MDE (FMDE) approach for xDSLs and a proposal for new avenues of investigation.

Akram Idani
Enabling Content Management Systems as an Information Source in Model-Driven Projects

Content Management Systems (CMSs) are the most popular tool when it comes to create and publish content across the web. Recently, CMSs have evolved, becoming headless. Content served by a headless CMS aims to be consumed by other applications and services through REST APIs rather than by human users through a web browser. This evolution has enabled CMSs to become a notorious source of content to be used in a variety of contexts beyond pure web navigation. As such, CMS have become an important component of many information systems. Unfortunately, we still lack the tools to properly discover and manage the information stored in a CMS, often highly customized to the needs of a specific domain. Currently, this is mostly a time-consuming and error-prone manual process.In this paper, we propose a model-based framework to facilitate the integration of headless CMSs in software development processes. Our framework is able to discover and explicitly represent the information schema behind the CMS. This facilitates designing the interaction between the CMS model and other components consuming that information. These interactions are then generated as part of a middleware library that offers platform-agnostic access to the CMS to all the client applications. The complete framework is open-source and available online.

Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
A Global Model-Driven Denormalization Approach for Schema Migration

With data’s evolution in terms of volume, variety, and velocity, Information Systems (IS) administrators have to steadily adapt their data model and choose the best solution(s) to store and manage data in accordance with users’ requirements. In this context, many existing solutions transform a source data model into a target one, but none of them leads the administrator to choose the most suitable model by offering a limited solution space automatically calculated and adapted to his needs. We propose ModelDrivenGuide, an automatic global approach for leading the model transformation process. It starts by transforming the conceptual model into a logical model, and it defines refinement rules that help to generate all possible data models. Our approach then relies on a heuristic to reduce the search space by avoiding cycles and redundancies. We also propose a formalisation of the denormalization process and we discuss the completeness and the complexity of our approach.

Jihane Mali, Shohreh Ahvar, Faten Atigui, Ahmed Azough, Nicolas Travers
State Model Inference Through the GUI Using Run-Time Test Generation

Software testing is an important part of engineering trustworthy information systems. End-to-end testing through Graphical User Interface (GUI) can be done manually, but it is a very time consuming and costly process. There are tools to capture or manually define scripts for automating regression testing through a GUI, but the main challenge is the high maintenance cost of the scripts when the GUI changes. In addition, GUIs tend to have a large state space, so creating scripts to cover all the possible paths and defining test oracles to check all the elements of all the states would be an enormous effort. This paper presents an approach to automatically explore a GUI while inferring state models that are used for action selection in run-time GUI test generation, implemented as an extension to the open source TESTAR tool. As an initial validation, we experiment on the impact of using various state abstraction mechanisms on the model inference and the performance of the implemented action selection algorithm based on the inferred model. Later, we analyse the challenges and provide future research directions on model inference and scriptless GUI testing.

Ad Mulders, Olivia Rodriguez Valdes, Fernando Pastor Ricós, Pekka Aho, Beatriz Marín, Tanja E. J. Vos

Machine Learning Applications

Frontmatter
Autonomous Object Detection Using a UAV Platform in the Maritime Environment

Maritime operations vary greatly in character and requirements, ranging from shipping operations to search and rescue and safety operations. Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons and others. Due to the unique characteristics of the maritime environment the location of even inert objects changes through time, depending on weather conditions, water currents etc. Unmanned aerial vehicles (UAV) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained and used to automatically detect and track objects of specific types and characteristics. Within the context of the EFFECTOR project we developed and present here an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. The system is meant to supplement search and rescue, as well as maritime safety operations where a report of an object in the water needs to be verified with the object detected and tracked, providing a live video stream to support decision making.

Emmanuel Vasilopoulos, Georgios Vosinakis, Maria Krommyda, Lazaros Karagiannidis, Eleftherios Ouzounoglou, Angelos Amditis
Bosch’s Industry 4.0 Advanced Data Analytics: Historical and Predictive Data Integration for Decision Support

Industry 4.0, characterized by the development of automation and data exchanging technologies, has contributed to an increase in the volume of data, generated from various data sources, with great speed and variety. Organizations need to collect, store, process, and analyse this data in order to extract meaningful insights from these vast amounts of data. By overcoming these challenges imposed by what is currently known as Big Data, organizations take a step towards optimizing business processes. This paper proposes a Big Data Analytics architecture as an artefact for the integration of historical data - from the organizational business processes - and predictive data - obtained by the use of Machine Learning models -, providing an advanced data analytics environment for decision support. To support data integration in a Big Data Warehouse, a data modelling method is also proposed. These proposals were implemented and validated with a demonstration case in a multinational organization, Bosch Car Multimedia in Braga. The obtained results highlight the ability to take advantage of large amounts of historical data enhanced with predictions that support complex decision support scenarios.

João Galvão, Diogo Ribeiro, Inês Machado, Filipa Ferreira, Júlio Gonçalves, Rui Faria, Guilherme Moreira, Carlos Costa, Paulo Cortez, Maribel Yasmina Santos
Benchmarking Conventional Outlier Detection Methods

Nowadays, businesses in many industries face an increasing flow of data and information. Data are at the core of the decision-making process, hence it is vital to ensure that the data are of high quality and no noise is present. Outlier detection methods are aimed to find unusual patterns in data and find their applications in many practical domains. These methods employ different techniques, ranging from pure statistical tools to deep learning models that have gained popularity in recent years. Moreover, one of the most popular outlier detection techniques are machine learning models. They have several characteristics which affect the potential of their usefulness in real-life scenarios. The goal of this paper is to add to the existing body of research on outlier detection by comparing the isolation forest, DBSCAN and LOF techniques. Thus, we investigate the research question: which ones of these outlier detection models perform best in practical business applications. To this end, three models are built on 12 datasets and compared using 5 performance metrics. The final comparison of the models is based on the McNemar’s test, as well as on ranks per performance measure and on average. Three main conclusions can be made from the benchmarking study. First, the models considered in this research disagree differently, i.e. their type I and type II errors are not similar. Second, considering the time, AUPRC and sensitivity metrics, the iForest model is ranked the highest. Hence, the iForest model is the best in the cases when time performance is a key consideration as well as when the opportunity costs of not detecting an outlier are high. Third, the DBSCAN model obtains the highest ranking along the F1 score and precision dimensions. That allows us to conclude that if raising many false alarms is not an important concern, the DBSCAN model is the best to employ.

Elena Tiukhova, Manon Reusens, Bart Baesens, Monique Snoeck

Forum

Frontmatter
Handling Temporal Data Imperfections in OWL 2 - Application to Collective Memory Data Entries

Dealing with imperfect temporal data entries in the context of Collective and Personal Memory applications is an imperative matter. Data are structured semantically using an ontology called “Collective Memo Onto”. In this paper, we propose an approach that handles temporal data imperfections in OWL 2. We reduce to four types of imperfection defined in our typology of temporal data imperfections which are imprecision, uncertainty, simultaneously uncertainty and imprecision and conflict. The approach consists of representing imperfect quantitative and qualitative time intervals and time points by extending the 4D-fluents approach and defining new components, as well as reasoning about the handled data by extending the Allen’s Interval algebra. Based on both extensions, we propose an OWL 2 ontology named “TimeOntoImperfection”. The proposed qualitative temporal relations are inferred via a set of 924 SWRL rules. We validate our work by implementing a prototype based on the proposed ontology and we apply it in the context of the Collective Memory Temporal Data.

Nassira Achich, Fatma Ghorbel, Bilel Gargouri, Fayçal Hamdi, Elisabeth Métais, Faiez Gargouri
Integrated Approach to Assessing the Progress of Digital Transformation by Using Multiple Objective and Subjective Indicators

Digital transformation affects not only IT and innovative business models and processes, it is significantly influenced by the skills and competence of top managers and new requirements and expectations of customers. The basic driver of ongoing digital transformation in any organization is performed with the active support of the Chief Information Officer, Chief Information Security Officer, Chief Technology Officer, Chief Digital Officer, or their cooperation work. Depending on the organization/company size, the number of these chiefs could be reduced and corresponding responsibilities merged. To this end, it is necessary to identify the main responsibilities and to clarify the existing hierarchy between them. To assess the progress of the digitalization process, the article proposes a multi-criteria mathematical model, the essence of which is the consideration of both objective and subjective criteria. The realized numerical application through five objective and eight subjective criteria demonstrate the applicability of described approach.

Daniela Borissova, Zornitsa Dimitrova, Naiden Naidenov, Radoslav Yoshinov
Towards Integrated Model-Driven Engineering Approach to Business Intelligence

This paper presents a vision for an integrated and comprehensive Model-Driven Engineering (MDE) framework for Business Intelligence (BI), called BIG - Business Intelligence Generator. It starts from two observations: (i) MDE is a common approach to implement parts of a BI system and (ii) existing MDE approaches to BI are heterogeneous, not always methodologically and technically aligned, and sometimes even overlooking entire layers of the BI systems. This paper objectifies the heterogeneity of existing MDE approaches, extends on the problems it is likely to lead to, and calls for a proper end-to-end MDE-BI approach, with each layer of the MDE-BI architecture capable of proper communication and exchange with the next one. As a response, the BIG framework is introduced, under the form of a vision. The paper describes the BIG framework in general and discusses for each of its modules the benefits of the proposal. Future works required to fulfill the vision are also discussed, suggesting new avenues for research around BI and MDE.

Corentin Burnay, Benito Giunta

Open Access

Method for Evaluating Information Security Level in Organisations

This paper introduces a method for evaluating information security levels of organisations using a developed framework. The framework is based on Estonian Information Security Standard categories which is compatible with ISO 27001 standard. The framework covers both technical and organisational aspects of information security.The results provide an overview of security to the organisation’s management, compare different organisations across the region, and support strategic decision-making on a national level.

Mari Seeba, Sten Mäses, Raimundas Matulevičius
ChouBERT: Pre-training French Language Model for Crowdsensing with Tweets in Phytosanitary Context

To fulfil the increasing need for food of the growing population and face climate change, modern technologies have been applied to improve different farming processes. One important application scenario is to detect and measure natural hazards using sensors and data analysis techniques. Crowdsensing is a sensing paradigm that empowers ordinary people to contribute with data their sensor-enhanced mobile devices gather or generate. In this paper, we propose to use Twitter as an open crowdsensing platform for acquiring farmers knowledge. We proved this concept by applying pre-trained language models to detect individual’s observation from tweets for pest monitoring.

Shufan Jiang, Rafael Angarita, Stéphane Cormier, Julien Orensanz, Francis Rousseaux
A Conceptual Characterization of Fake News: A Positioning Paper

Fake News have become a global phenomenon due to its explosive growth, particularly on social media. How to identify fake news is becoming an extremely attractive working domain. The lack of a sound, well-grounded conceptual characterization of what exactly a Fake news is and what are its main features, makes difficult to manage Fake News understanding, identification and creation. In this research we propose that conceptual modeling must play a crucial role to characterize Fake News content in a precise way. Only clearly delimiting what a Fake News is, it will be possible to understand and managing their different perspectives and dimensions, with the final purpose of developing any reliable framework for online Fake News detection as much automated as possible. This paper discusses the effort that should be made towards a precise conceptual model of Fake News and its relation with an XAI approach.

Nicolas Belloir, Wassila Ouerdane, Oscar Pastor, Émilien Frugier, Louis-Antoine de Barmon
Combinations of Content Representation Models for Event Detection on Social Media

Social media are becoming the preferred channel to report and discuss events happening around the world. The data from these channels can be used to detect ongoing events in real-time. A typical approach is to use event detection methods, usually consisting of a clustering phase, in which similar documents are grouped together, and then an analysis of the clusters to decide whether they deal with real-world events. To cluster together similar documents, content representation models are critical. In this paper, we individually compare the performances of different social media documents content representation models used during the clustering phase, exploiting lexical, semantic and social media specific features, like tags and URLs. To the best of our knowledge, these models are usually individually exploited in this context. We investigate their complementarity and propose to combine them.

Elliot Maître, Max Chevalier, Bernard Dousset, Jean-Philippe Gitto, Olivier Teste
Increasing Awareness and Usefulness of Open Government Data: An Empirical Analysis of Communication Methods

Over the past decade, governments around the world have implemented Open Government Data (OGD) policies to make their data publicly available, with collaboration and citizen engagement being one of the main goals. However, even though a lot of data is published, only a few citizens are aware of its existence and usefulness, which hinders fulfilling the purpose of OGD initiatives. The objective of this paper is to fill this gap by identifying the appropriate communication methods for raising awareness and usefulness of OGD to citizens. To achieve this goal, we first conducted a literature review to identify methods used to raise citizen awareness of OGD. Then, these identified methods were confronted with the results obtained from an online survey completed by 30 participants on their preferred methods to provide recommendations to governments. The contribution of this paper is twofold. First, it provides an inventory of communication methods identified in the literature. Second, it analyzes the gap between the use of these methods in practice and citizens’ preference and uses this analysis to propose a list of methods that governments can use to promote OGD.

Abiola Paterne Chokki, Anthony Simonofski, Benoît Frénay‬, Benoît Vanderose
Toward Digital ERP: A Literature Review

Many organizations have a long history with the use of ERP. However, organizations are increasingly turning to digital capabilities to transform operational processes and business models. Extant literature has increased our understanding of ERP, but we lack comprehensive insights into the evolving nature of ERP in the context of digital transformation. Through a review of articles from the AIS Basket of Eight IT journals, we identified digital capabilities associated with contemporary ERP across five categories. The identified capabilities foreground the evolving nature of ERP, resulting in the introduction of a definition for digital ERP (D-ERP) and a call for research studying the co-evolution of D-ERP and digital transformation.

Benjamin De Brabander, Amy Van Looy, Stijn Viaene
Trust Model Recommendation Driven by Application Requirements

Recommendation systems have taken the turn of the new uses of the Internet, with the emergence of trust-based recommendation systems. These use trust relationships between users to predict ratings based on experiences and feedback. To obtain these ratings, many computational models have been developed to help users make decisions, and to improve interactions between different users within a system. Hence, choosing the appropriate model is challenging. To address this issue, we propose a two-step approach that, first, allows the user to define the requirements of his/her target system and, then, guides him/her to select the most appropriate computational model for his/her application according to the defined requirements.

Chayma Sellami, Mickaël Baron, Stephane Jean, Mounir Bechchi, Allel Hadjali, Dominique Chabot
Predicting the State of a House Using Google Street View
An Analysis of Deep Binary Classification Models for the Assessment of the Quality of Flemish Houses

Currently, the state of a house is typically assessed by an expert, which is time and resource intensive. Therefore, an automatic assessment could have economic, social and ecological benefits. Hence, this study presents a binary classification model using transfer learning to classify Google Street View images of houses. For this purpose, a three-by-three analysis is conducted that allows to compare three different network architectures and three differently-sized data sets, using properties located in Leuven, Belgium. A DenseNet201 architecture was found to work best, as illustrated quantitatively as well as by means of state-of-the-art explainability methods.

Margot Geerts, Kiran Shaikh, Jochen De Weerdt, Seppe Vanden Broucke
Towards a Comprehensive BPMN Extension for Modeling IoT-Aware Processes in Business Process Models

Internet of Thing (IoT) devices enable the collection and exchange of data over the Internet, whereas Business Process Management (BPM) is concerned with the analysis, discovery, implementation, execution, monitoring, and evolution of business processes. By enriching BPM systems with IoT capabilities, data from the real world can be captured and utilized during process execution in order to improve online process monitoring and data-driven decision making. Furthermore, this integration fosters prescriptive process monitoring, e.g., by enabling IoT-driven process adaptions when deviations between the digital process and the one actually happening in the real world occur. As a prerequisite for exploiting these benefits, IoT-related aspects of business processes need to be modeled. To enable the use of sensors, actuators, and other IoT objects in combination with process models, we introduce a BPMN 2.0 extension with IoT-related artifacts and events. We provide a first evaluation of this extension by applying it in two case studies for modeling of IoT-aware processes.

Yusuf Kirikkayis, Florian Gallik, Manfred Reichert
Team Selection Using Statistical and Graphical Approaches for Cricket Fantasy Leagues

Fantasy Sports are becoming more and more popular these days, hence the race to crack it is more trending than ever. In this paper, we focus on cricket (IPL) and Dream11. Using advanced statistical and graphical models, and new performance metrics for batting and bowling we aim to build a model that can predict the top performing 11 players out of the two teams. This involves predicting the player performance and selecting the best 11 while complying with league constraints. The proposed model on an average predicts 70% of the players from the Dream Team.

S. Mohith, Rebhav Guha, Sonia Khetarpaul, Samant Saurabh
PM4Py-GPU: A High-Performance General-Purpose Library for Process Mining

Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to commercial tools, they lack the performance and struggle to analyze large amounts of data. This paper presents PM4Py-GPU, a Python process mining library based on the NVIDIA RAPIDS framework. Thanks to the dataframe columnar storage and the high level of parallelism, a significant speed-up is achieved on classic process mining computations and processing activities.

Alessandro Berti, Minh Phan Nghia, Wil M. P. van der Aalst
Interactive Business Process Comparison Using Conformance and Performance Insights - A Tool

Process mining techniques make the underlying processes in organizations transparent. Historical event data are used to perform conformance checking and performance analyses. Analyzing a single process and providing visual insights has been the focus of most process mining techniques. However, comparing two processes or a single process in different situations is essential for process improvement. Different approaches have been proposed for process comparison. However, most of the techniques are either relying on the aggregated KPIs or their comparisons are based on process models, i.e., the flow of activities. Existing techniques are not able to provide understandable and insightful results for process owners. The current paper describes a tool that provides aggregated and detailed comparisons of two processes starting from their event logs using innovative visualizations. The visualizations provided by the tool are interactive. We exploit some techniques recently proposed in the literature, e.g., stochastic conformance checking and the performance spectrum, for conformance and performance comparison.

Mahsa Pourbafrani, Majid Rafiei, Alessandro Berti, Wil M. P. van der Aalst
Research Incentives in Academia Leading to Unethical Behavior

A current practice in academia is to reward researchers for achieving outstanding performance. Although intended to boost productivity, such a practice also promotes competitiveness and could lead to unethical behavior. This position paper exposes common misconducts that arise when researchers try to game the system. It calls the research community to take preventive actions to reduce misconduct and treat such a pervasive environment with proper acknowledgment of researchers’ efforts and rewards on quality rather than quantity.

Jefferson Seide Molléri
Assessing the Ethical, Social and Environmental Performance of Conferences

There is a rising demand for assessing the performance of organisations on ethical, social and environmental (ESE) topics. Ethical, social and environmental accounting (ESEA) is common practice in many types of organisations and initiatives. Currently, scientific conferences are not in the spotlight nor feeling pressure to disclose their ESE accounts. However, proactively adopting these practices is an opportunity to lead the way and show commitment and responsibility. Since no existing ESEA method fits the domain of conferences well, this paper presents preliminary results on engineering such a method. We discuss material ESE topics for conferences, key performance indicators, measurement and data collection methods, and ICT infrastructure. We illustrate the method concepts by applying it to the RCIS conference series. We are confident that conference organisers and scientific communities will start assessing the performance of their conferences under many organisational sustainability dimensions and, what is more important, initiate reflection processes to improve such performance over the years.

Sergio España, Vijanti Ramautar, Quang Tan Le
ANGLEr: A Next-Generation Natural Language Exploratory Framework

Natural language processing is used for solving a wide variety of problems. Some scholars and interest groups working with language resources are not well versed in programming, so there is a need for a good graphical framework that allows users to quickly design and test natural language processing pipelines without the need for programming. The existing frameworks do not satisfy all the requirements for such a tool. We, therefore, propose a new framework that provides a simple way for its users to build language processing pipelines. It also allows a simple programming language agnostic way for adding new modules, which will help the adoption by natural language processing developers and researchers. The main parts of the proposed framework consist of (a) a pluggable Docker-based architecture, (b) a general data model, and (c) APIs description along with the graphical user interface. The proposed design is being used for implementation of a new natural language processing framework, called ANGLEr.

Timotej Knez, Marko Bajec, Slavko Žitnik

Doctoral Consortium

Frontmatter
Towards a Roadmap for Developing Digital Work Practices: A Maturity Model Approach

The digital economy brings us a wide range of services and products assisted by emerging technologies in Industry 4.0. Nevertheless, already since the 1990s and early 2000s, IT has had a huge impact on the digitalization and digitization of businesses. This phenomenon of advancing in digital-oriented work practices is not only affecting the customer side, but is also changing the way of working within organizations. Although employees are one of the crucial elements in each organization and their level of work satisfaction is critical to the efficiency of a business, their work impression is still undergoing scrutiny when it gets to digital process innovations. Moreover, organizations still benefit from assistance in their adoption of digital-oriented work practices, for which a related maturity model (MM) can be one of the solutions. The current Ph.D. plan consists of three research projects that follow a mixed-method approach with a combination of quantitative and qualitative designs, within an overall design of Design-Science Research (DSR). Project 1 has two subprojects (i.e., a Systematic Literature Review (SLR) and a data-driven analysis). It starts with analyzing the relevant literature from a people–process–technology (PPT) perspective to extract relevant factors when digitalizing business processes. Afterwards, it investigates a representative set of European employee data using statistical data analysis (e.g., factor analysis and ANOVA) and data mining (e.g., clustering) techniques to delve into the impact of digital-oriented work practices on work satisfaction. After this artefact identification, we continue with Project 2 (i.e., expert panel, case study) to add evidence for a maturity-based gradation along with the identified clusters of digital-oriented work practices. Finally, Project 3 helps concretize the intended MM by focusing on the relationships with employee satisfaction and the relevant factors. Our findings will assist organizations to upgrade their work practices (i.e., including assessment and improvement advice), while simultaneously empowering their employees.

Pooria Jafari
Evolutionary Scriptless Testing

Automated scriptless testing approaches use Action Selection Rules (ASR) to generate on-the-fly test sequences when testing a software system. Currently, these rules are manually designed and implemented. In this paper we present our research on how to automatically create ASRs by evolving them using an evolutionary algorithm. Expected results are an automated system for Evolutionary Scriptless Testing containing a representation of ASRs, different fitness functions and manipulation operators.

Lianne Valerie Hufkens
Scriptless Testing for Extended Reality Systems

Extended Reality (XR) systems are complex applications that have emerged in a wide variety of domains, such as computer games and medical practice. Testing XR software is mainly done manually by human testers, which implies a high cost in terms of time and money. Current automated testing approaches for XR systems consist of rudimentary capture and replay of scripts. However, this approach only works for simple test scenarios. Moreover, it is well-known that the scripts break easily each time the XR system is changed. There are research projects aimed at using autonomous agents that will follow scripted instructions to test XR functionalities. Nonetheless, using only scripted testing techniques, it is difficult and expensive to tackle the challenges of testing XR systems. This thesis is focus on the use of automated scriptless testing for XR systems. This way we help to reduce part of the manual testing effort and complement the scripted techniques.

Fernando Pastor Ricós
Artificial Intelligence: Impacts of Explainability on Value Creation and Decision Making

Over the last few years, companies’ investment in new AI systems has seen a strong and constant progression. However, except for the Big Tech, the use of AI is still marginal at this stage, and seems to spark cautiousness and apprehension. A potential reason for this hesitation may be linked to a lack of trust related in particular to the so-called black box AI technologies such as deep learning. This is why our research objective is to explore the effects of explainability on trust in these new AI-based digital systems with which the users can either interact or directly accept its results in case of fully autonomous system. More precisely, in the perspective of an industrialized use of AI, we would like to study the role of explainability for stakeholders in the decision-making process as well as in value creation.

Taoufik El Oualidi
Towards Empirically Validated Process Modelling Education Using a BPMN Formalism

“A picture is worth more than a thousand words” may be said of process models, using the words of business and IT leaders. Business Process Model and Notation (BPMN) is a de facto standard used for business process modelling that helps flexible and responsive understanding, analysis and communication of business processes and inter-organisational collaborations. Despite the importance, there are significant gaps in providing empirically justified and systematic pedagogy in teaching process modelling. The research seeks to cover the gap through the systematic literature review of the broader area of conceptual modelling, analysis of novice modellers’ common errors and patterns, design of learning goals and course design of process modelling and validating the result using experiments.

Ilia Maslov
Multi-task Learning for Automatic Event-Centric Temporal Knowledge Graph Construction

An important aspect of understanding written language is recognising and understanding events described in a document. Each event is usually associated with a specific time or time period when it occurred. Humans naturally understand the time of each event based on our common sense and the relations between the events, expressed in the documents. In our work we will explore and implement a system for automated extraction of temporal relations between the events in a document as well as of additional attributes like date, time, duration etc. for placing the events in time. Our system will use the extracted information to build a graph representing the events seen in a document. We will also combine the temporal knowledge over multiple documents to build a global knowledge base that will serve as a collection of common sense about the temporal aspect of common events, allowing the system to use the gathered knowledge about the events to derive information not explicitly expressed in the document.

Timotej Knez
Backmatter
Metadaten
Titel
Research Challenges in Information Science
herausgegeben von
Renata Guizzardi
Dr. Jolita Ralyté
Prof. Dr. Xavier Franch
Copyright-Jahr
2022
Electronic ISBN
978-3-031-05760-1
Print ISBN
978-3-031-05759-5
DOI
https://doi.org/10.1007/978-3-031-05760-1

Premium Partner