Amine Abbad-Andaloussi

Thumbnail Image
Last Name
Abbad-Andaloussi
First name
Amine
Email
amine.abbad-andaloussi@unisg.ch
ORCID
Phone
+41 71 224 2635

Settings

Now showing 1 - 8 of 8
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Complexity in declarative process models: Metrics and multi-modal assessment of cognitive load
    (2023)
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Tijs Slaats
    ;
    Ekkart Kindler
    ;
    Barbara Weber  
    Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users’ cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users’ cognitive load when engaging with process models.
    Type:journal article
    Journal:Expert Systems with Applications
    Volume:233
    DOI:10.1016/j.eswa.2023.120924
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118749
    Scopus© Citations 34
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    On the declarative paradigm in hybrid business process representations: A conceptual framework and a systematic literature study
    (Elsevier BV, 2020-07)
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Tijs Slaats
    ;
    Ekkart Kindler
    ;
    Barbara Weber  
    Process modeling plays a central role in the development of today’s process-aware information systems both on the management level (e.g., providing input for requirements elicitation and fostering communication) and on the enactment level (providing a blue-print for process execution and enabling simulation). The literature comprises a variety of process modeling approaches proposing different modeling languages (i.e., imperative and declarative languages) and different types of process artifact support (i.e., process models, textual process descriptions, and guided simulations). However, the use of an individual modeling language or a single type of process artifact is usually not enough to provide a clear and concise understanding of the process. To overcome this limitation, a set of so-called “hybrid” approaches combining languages and artifacts have been proposed, but no common grounds have been set to define and categorize them. This work aims at providing a fundamental understanding of these hybrid approaches by defining a unified terminology, providing a conceptual framework and proposing an overarching overview to identify and analyze them. Since no common terminology has been used in the literature, we combined existing concepts and ontologies to define a “Hybrid Business Process Representation” (HBPR). Afterwards, we conducted a Systematic Literature Review (SLR) to identify and investigate the characteristics of HBPRs combining imperative and declarative languages or artifacts. The SLR resulted in 30 articles which were analyzed. The results indicate the presence of two distinct research lines and show common motivations driving the emergence of HBPRs, a limited maturity of existing approaches, and diverse application domains. Moreover, the results are synthesized into a taxonomy classifying different types of representations. Finally, the outcome of the study is used to provide a research agenda delineating the directions for future work.
    Type:journal article
    Journal:Information Systems
    Volume:91
    URL:https://www.sciencedirect.com/science/article/pii/S0306437920300168
    DOI:10.1016/j.is.2020.101505
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122089
    Scopus© Citations 40
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Supporting the Process of Learning and Teaching Process Models
    (Institute of Electrical and Electronics Engineers (IEEE), 2020-07-01)
    Josep Sanchez-Ferreres
    ;
    Luis Delicado
    ;
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Guillermo Calderon-Ruiz
    The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called Model Judge, focused toward the two main actors in the process of learning process model creation: novice modelers and instructors. For modelers, the platform enables the automatic validation of the process models created from a textual description, providing explanations about quality issues in the model. Model Judge can provide diagnostics regarding model structure, writing style, and semantics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modeling exercises by providing an editor to annotate the main parts of a textual description, which is empowered with natural language processing capabilities so that the annotation effort is minimized. So far around 300 students in process modeling courses of five different universities around the world have used the platform. The feedback gathered from some of these courses shows good potential in helping students to improve their learning experience, which might, in turn, impact process model quality and understandability. Moreover, our results show that instructors can benefit from getting insights into the evolution of modeling processes, including arising quality issues of single students, but also discovering tendencies in groups of students. Although the framework has been applied to process model creation, it could be extrapolated to other contexts where the creation of models based on a textual description plays an important role.
    Type:journal article
    Journal:IEEE Transactions on Learning Technologies
    Volume:13
    Issue:3
    URL:https://ieeexplore.ieee.org/document/9050851
    DOI:10.1109/TLT.2020.2983916
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122086
    Scopus© Citations 15
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Understanding Quality in Declarative Process Modeling Through the Mental Models of Experts
    (Springer International Publishing, 2020)
    Amine Abbad-Andaloussi  
    ;
    Christopher J. Davis
    ;
    Andrea Burattin
    ;
    Hugo A. López
    ;
    Tijs Slaats
    Imperative process models have become immensely popular. However, their use is usually limited to rigid and repetitive processes. Considering the inherent flexibility in most processes in the real-world and the increased need for managing knowledge-intensive processes, the adoption of declarative languages becomes more pertinent than ever. While the quality of imperative models has been extensively investigated in the literature, little is known about the dimensions affecting the quality of declarative models. This work takes an advanced stride to investigate the quality of declarative models. Following the theory of Personal Construct Psychology (PCT), our research introduces a novel method within the Business Process Management (BPM) field to explore quality in the eyes of expert modelers. The findings of this work summarize the dimensions defining the quality of declarative models. The outcome shows the potential of PCT as a basis to discover quality dimensions and advances our understanding of quality in declarative process models.
    Type:conference paper
    Journal:Business Process Management
    URL:https://link.springer.com/chapter/10.1007/978-3-030-58666-9_24
    DOI:10.1007/978-3-030-58666-9_24
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122088
    Scopus© Citations 11
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    The Impact of Modularization on the Understandability of Declarative Process Models: A Research Model
    (Springer International Publishing, 2020)
    Amine Abbad-Andaloussi  
    ;
    Pnina Soffer
    ;
    Tijs Slaats
    ;
    Andrea Burattin
    ;
    Barbara Weber  
    Process models provide a blueprint for process execution and an indispensable tool for process management. Bearing in mind their trending use for requirement elicitation, communication and improvement of business processes, the need for understandable process models becomes a must. In this paper, we propose a research model to investigate the impact of modularization on the understandability of declarative process models. We design a controlled experiment supported by eye-tracking, electroencephalography (EEG) and galvanic skin response (GSR) to appraise the understandability of hierarchical process models through measures such as comprehension accuracy, response time, attention, cognitive load and cognitive integration.
    Type:conference paper
    Journal:Lecture Notes in Information Systems and Organisation, Information Systems and Neuroscience (NeuroIS)
    URL:https://link.springer.com/chapter/10.1007/978-3-030-60073-0_15
    DOI:10.1007/978-3-030-60073-0_15
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122091
    Scopus© Citations 9
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Toward an Automated Labeling of Event Log Attributes
    (Springer International Publishing, 2018)
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Barbara Weber  
    Process mining aims at exploring the data produced by executable business processes to mine the underlying control-flow and data-flow. Most of the process mining algorithms assume the existence of an event log with a certain maturity level. Unfortunately, the logs provided by process unaware information systems often do not comply with the required maturity level, since they lack the notion of process instance, also referred in process mining as “case id”. Without a proper identification of the case id attribute in log files, the outcome of process mining algorithms is unpredictable. This paper proposes a new approach that aims to overcome this challenge by automatically inferring the case id attribute from log files. The approach has been implemented as a ProM plugin and evaluated with several real-world event logs. The results demonstrate a high accuracy in inferring the case id attribute.
    Type:conference paper
    URL:https://link.springer.com/chapter/10.1007/978-3-319-91704-7_6
    DOI:10.1007/978-3-319-91704-7_6
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122093
    Scopus© Citations 15
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Scopus© Citations 13
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    From analytical purposes to data visualizations: a decision process guided by a conceptual framework and eye tracking
    (Springer Science and Business Media LLC, 2019-07-10)
    Jens Gulden
    ;
    Andrea Burattin
    ;
    Amine Abbad-Andaloussi  
    ;
    Barbara Weber  
    Data visualizations are versatile tools for gaining cognitive access to large amounts of data and for making complex relationships in data understandable. This paper proposes a method for assessing data visualizations according to the purposes they fulfill in domain-specific data analysis settings. We introduce a framework that gets configured for a given analysis domain and allows to choose data visualizations in a methodically justified way, based on analysis questions that address different aspects of data to be analyzed. Based on the concepts addressed by the analysis questions, the framework provides systematic guidance for determining which data visualizations are able to serve which conceptual analysis interests. In a second step of the method, we propose to follow a data-driven approach and to experimentally compare alternative data visualizations for a particular analytical purpose. More specifically, we propose to use eye tracking to support justified decisions about which of the data visualizations selected with the help of the framework are most suitable for assessing the analysis domain in a cognitively efficient way. We demonstrate our approach of how to come from analytical purposes to data visualizations using the example domain of Process Modeling Behavior Analysis. The analyses are performed on the background of representative analysis questions from this domain.
    Type:journal-article
    Journal:Software and Systems Modeling
    Volume:19
    Issue:3
    URL:https://link.springer.com/article/10.1007/s10270-019-00742-z
    DOI:10.1007/s10270-019-00742-z
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122090
    Scopus© Citations 7