Presentation of the paper "Modeling and Reasoning over Declarative Data-Aware Processes with Object-Centric Behavioral Constraints" at the 17th Int. Conference on Business Process Management (BPM 2019). Paper available here: https://link.springer.com/chapter/10.1007/978-3-030-26619-6_11
Abstract
Existing process modeling notations ranging from Petri nets to BPMN have difficulties capturing the data manipulated by processes. Process models often focus on the control flow, lacking an explicit, conceptually well-founded integration with real data models, such as ER diagrams or UML class diagrams. To overcome this limitation, Object-Centric Behavioral Constraints (OCBC) models were recently proposed as a new notation that combines full-fledged data models with control-flow constraints inspired by declarative process modeling notations such as DECLARE and DCR Graphs. We propose a formalization of the OCBC model using temporal description logics. The obtained formalization allows us to lift all reasoning services defined for constraint-based process modeling notations without data, to the much more sophisticated scenario of OCBC. Furthermore, we show how reasoning over OCBC models can be reformulated into decidable, standard reasoning tasks over the corresponding temporal description logic knowledge base.
The document discusses challenges with modeling processes that involve multiple interacting objects. Conventional process modeling approaches encourage separating objects and focusing on one object type per process, which can lead to issues when objects interact. The document proposes modeling objects as first-class citizens and capturing relationships between objects to better represent real-world processes where objects corelate and influence each other. It provides examples of how conventional case-centric modeling can struggle to accurately capture a hiring process that involves interacting candidate, application, job offer and other objects.
Slides of our BPM 2022 paper on "Reasoning on Labelled Petri Nets and Their Dynamics in a Stochastic Setting", which received the best paper award at the conference. Paper available here: https://link.springer.com/chapter/10.1007/978-3-031-16103-2_22
Slides of the keynote speech on "Constraints for process framing in Augmented BPM" at the AI4BPM 2022 International Workshop, co-located with BPM 2022. The keynote focuses on the problem of "process framing" in the context of the new vision of "Augmented BPM", where BPM systems are augmented with AI capabilities. This vision is described in a manifesto, available here: https://arxiv.org/abs/2201.12855
Keynote speech at KES 2022 on "Intelligent Systems for Process Mining". I introduce process mining, discuss why process mining tasks should be approached by using intelligent systems, and show a concrete example of this combination, namely (anticipatory) monitoring of evolving processes against temporal constraints, using techniques from knowledge representation and formal methods (in particular, temporal logics over finite traces and their automata-theoretic characterization).
Presentation (jointly with Claudio Di Ciccio) on "Declarative Process Mining", as part of the 1st Summer School in Process Mining (http://www.process-mining-summer-school.org). The Presentation summarizes 15 years of research in declarative process mining, covering declarative process modeling, reasoning on declarative process specifications, discovery of process constraints from event logs, conformance checking and monitoring of process constraints at runtime. This is done without ad-hoc algorithms, but relying on well-established techniques at the intersection of formal methods, artificial intelligence, and data science.
1. The document discusses representing business processes with uncertainty using ProbDeclare, an extension of Declare that allows constraints to have uncertain probabilities.
2. ProbDeclare models contain both crisp constraints that must always hold and probabilistic constraints that hold with some probability. This leads to multiple possible "scenarios" depending on which constraints are satisfied.
3. Reasoning involves determining which scenarios are logically consistent using LTLf, and computing the probability distribution over scenarios by solving a system of inequalities defined by the constraint probabilities.
Presentation on "From Case-Isolated to Object-Centric Processes - A Tale of Two Models" as part of the Hasselt University BINF Research Seminar Series (see https://www.uhasselt.be/en/onderzoeksgroepen-en/binf/research-seminar-series).
Invited seminar on "Modeling and Reasoning over Declarative Data-Aware Processes" as part of the KRDB Summer Online Seminars 2020 (https://www.inf.unibz.it/krdb/sos-2020/).
The document discusses challenges with modeling processes that involve multiple interacting objects. Conventional process modeling approaches encourage separating objects and focusing on one object type per process, which can lead to issues when objects interact. The document proposes modeling objects as first-class citizens and capturing relationships between objects to better represent real-world processes where objects corelate and influence each other. It provides examples of how conventional case-centric modeling can struggle to accurately capture a hiring process that involves interacting candidate, application, job offer and other objects.
Slides of our BPM 2022 paper on "Reasoning on Labelled Petri Nets and Their Dynamics in a Stochastic Setting", which received the best paper award at the conference. Paper available here: https://link.springer.com/chapter/10.1007/978-3-031-16103-2_22
Slides of the keynote speech on "Constraints for process framing in Augmented BPM" at the AI4BPM 2022 International Workshop, co-located with BPM 2022. The keynote focuses on the problem of "process framing" in the context of the new vision of "Augmented BPM", where BPM systems are augmented with AI capabilities. This vision is described in a manifesto, available here: https://arxiv.org/abs/2201.12855
Keynote speech at KES 2022 on "Intelligent Systems for Process Mining". I introduce process mining, discuss why process mining tasks should be approached by using intelligent systems, and show a concrete example of this combination, namely (anticipatory) monitoring of evolving processes against temporal constraints, using techniques from knowledge representation and formal methods (in particular, temporal logics over finite traces and their automata-theoretic characterization).
Presentation (jointly with Claudio Di Ciccio) on "Declarative Process Mining", as part of the 1st Summer School in Process Mining (http://www.process-mining-summer-school.org). The Presentation summarizes 15 years of research in declarative process mining, covering declarative process modeling, reasoning on declarative process specifications, discovery of process constraints from event logs, conformance checking and monitoring of process constraints at runtime. This is done without ad-hoc algorithms, but relying on well-established techniques at the intersection of formal methods, artificial intelligence, and data science.
1. The document discusses representing business processes with uncertainty using ProbDeclare, an extension of Declare that allows constraints to have uncertain probabilities.
2. ProbDeclare models contain both crisp constraints that must always hold and probabilistic constraints that hold with some probability. This leads to multiple possible "scenarios" depending on which constraints are satisfied.
3. Reasoning involves determining which scenarios are logically consistent using LTLf, and computing the probability distribution over scenarios by solving a system of inequalities defined by the constraint probabilities.
Presentation on "From Case-Isolated to Object-Centric Processes - A Tale of Two Models" as part of the Hasselt University BINF Research Seminar Series (see https://www.uhasselt.be/en/onderzoeksgroepen-en/binf/research-seminar-series).
Invited seminar on "Modeling and Reasoning over Declarative Data-Aware Processes" as part of the KRDB Summer Online Seminars 2020 (https://www.inf.unibz.it/krdb/sos-2020/).
Presentation of the paper "Soundness of Data-Aware Processes with Arithmetic Conditions" at the 34th International Conference on Advanced Information Systems Engineering (CAiSE 2022). Paper available here: https://doi.org/10.1007/978-3-031-07472-1_23
Abstract:
Data-aware processes represent and integrate structural and behavioural constraints in a single model, and are thus increasingly investigated in business process management and information systems engineering. In this spectrum, Data Petri nets (DPNs) have gained increasing popularity thanks to their ability to balance simplicity with expressiveness. The interplay of data and control-flow makes checking the correctness of such models, specifically the well-known property of soundness, crucial and challenging. A major shortcoming of previous approaches for checking soundness of DPNs is that they consider data conditions without arithmetic, an essential feature when dealing with real-world, concrete applications. In this paper, we attack this open problem by providing a foundational and operational framework for assessing soundness of DPNs enriched with arithmetic data conditions. The framework comes with a proof-of-concept implementation that, instead of relying on ad-hoc techniques, employs off-the-shelf established SMT technologies. The implementation is validated on a collection of examples from the literature, and on synthetic variants constructed from such examples.
Presentation of the paper "Probabilistic Trace Alignment" at the 3rd International Conference on Process Mining (ICPM 2021). Paper available here: https://doi.org/10.1109/ICPM53251.2021.9576856
Abstract:
Alignments provide sophisticated diagnostics that pinpoint deviations in a trace with respect to a process model. Alignment-based approaches for conformance checking have so far used crisp process models as a reference. Recent probabilistic conformance checking approaches check the degree of conformance of an event log as a whole with respect to a stochastic process model, without providing alignments. For the first time, we introduce a conformance checking approach based on trace alignments using stochastic Workflow nets. This requires to handle the two possibly contrasting forces of the cost of the alignment on the one hand and the likelihood of the model trace with respect to which the alignment is computed on the other.
Presentation of the paper "Strategy Synthesis for Data-Aware Dynamic Systems with Multiple Actors" at the 7th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020). Paper available here: https://proceedings.kr.org/2020/32/
Abstract: The integrated modeling and analysis of dynamic systems and the data they manipulate has been long advocated, on the one hand, to understand how data and corresponding decisions affect the system execution, and on the other hand to capture how actions occurring in the systems operate over data. KR techniques proved successful in handling a variety of tasks over such integrated models, ranging from verification to online monitoring. In this paper, we consider a simple, yet relevant model for data-aware dynamic systems (DDSs), consisting of a finite-state control structure defining the executability of actions that manipulate a finite set of variables with an infinite domain. On top of this model, we consider a data-aware version of reactive synthesis, where execution strategies are built by guaranteeing the satisfaction of a desired linear temporal property that simultaneously accounts for the system dynamics and data evolution.
Presentation of the paper "Extending Temporal Business Constraints with Uncertainty" at the 18th Int. Conference on Business Process Management (BPM 2020). Paper available here: https://doi.org/10.1007/978-3-030-58666-9_3
Abstract: Temporal business constraints have been extensively adopted to declaratively capture the acceptable courses of execution in a business process. However, traditionally, constraints are interpreted logically in a crisp way: a process execution trace conforms with a constraint model if all the constraints therein are satisfied. This is too restrictive when one wants to capture best practices, constraints involving uncontrollable activities, and exceptional but still conforming behaviors. This calls for the extension of business constraints with uncertainty. In this paper, we tackle this timely and important challenge, relying on recent results on probabilistic temporal logics over finite traces. Specifically, our contribution is threefold. First, we delve into the conceptual meaning of probabilistic constraints and their semantics. Second, we argue that probabilistic constraints can be discovered from event data using existing techniques for declarative process discovery. Third, we study how to monitor probabilistic constraints, where constraints and their combinations may be in multiple monitoring states at the same time, though with different probabilities.
Presentation of the paper "Extending Temporal Business Constraints with Uncertainty" at the CAiSE2020 Forum. The paper is available here: https://link.springer.com/chapter/10.1007/978-3-030-58135-0_8
Abstract: Conformance checking is a fundamental task to detect deviations between the actual and the expected courses of execution of a business process. In this context, temporal business constraints have been extensively adopted to declaratively capture the expected behavior of the process. However, traditionally, these constraints are interpreted logically in a crisp way: a process execution trace conforms with a constraint model if all the constraints therein are satisfied. This is too restrictive when one wants to capture best practices, constraints involving uncontrollable activities, and exceptional but still conforming behaviors. This calls for the extension of business constraints with uncertainty. In this paper, we tackle this timely and important challenge, relying on recent results on probabilistic temporal logics over finite traces. Specifically, we equip business constraints with a natural, probabilistic notion of uncertainty. We discuss the semantic implications of the resulting framework and show how probabilistic conformance checking and constraint entailment can be tackled therein.
Keynote speech at the Belgian Process Mining Research Day 2021. I discuss the open, critical challenge of data preparation in process mining, considering the case where the original event data are implicitly stored in (legacy) relational databases. This case covers the common situation where event data are stored inside the data layer of an ERP or CRM system. This is usually handled using manual, ad-hoc, error-prone ETL procedures. I propose instead to adopt a pipeline based on semantic technologies, in particular the framework of ontology-based data access (also known as virtual knowledge graph). The approach is code-less, and relies on three main conceptual steps: (1) the creation of a data model capturing the relevant classes, attributes, and associations in the domain of interest (2) the definition of declarative mappings from the source database to the data model, following the ontology-based data access paradigm (3) the annotation of the data model with indications on which classes/associations/attributes provide the relevant notions of case, events, event attributes, and event-to-case relation. Once this is done, the framework automatically extracts the event log from the legacy data. This makes extremely smooth to generate logs by taking multiple perspectives on the same reality. The approach has been operationalized in the onprom tool, which employs semantic web standard languages for the various steps, and the XES standard as the target format for the event logs.
Keynote speech at the 7th International Workshop on DEClarative, DECision and Hybrid approaches to processes ( DEC2H 2019) In conjunction with BPM 2019.
This is a talk about the combined modeling and reasoning techniques for decisions, background knowledge, and work processes.
The advent of the OMG Decision Model and Notation (DMN) standard has revived interest, both from academia and industry, in decision management and its relationship with business process management. Several techniques and tools for the static analysis of decision models have been brought forward, taking advantage of the trade-off between expressiveness and computational tractability offered by the DMN S-FEEL language.
In this keynote, I argue that decisions have to be put in perspective, that is, understood and analyzed within their surrounding organizational boundaries. This brings new challenges that, in turn, require novel, advanced analysis techniques. Using a simple but illustrative example, I consider in particular two relevant settings: decisions interpreted the presence of background, structural knowledge of the domain of interest, and (data-aware) business processes routing process instances based on decisions. Notably, the latter setting is of particular interest in the context of multi-perspective process mining. I report on how we successfully tackled key analysis tasks in both settings, through a balanced combination of conceptual modeling, formal methods, and knowledge representation and re
Presentation at "Ontology Make Sense", an event in honor of Nicola Guarino, on how to integrate data models with behavioral constraints, an essential problem when modeling multi-case real-life work processes evolving multiple objects at once. I propose to combine UML class diagrams with temporal constraints on finite traces, linked to the data model via co-referencing constraints on classes and associations.
The document discusses representing and querying norm states using temporal ontology-based data access (OBDA). It presents the QUEN framework which models norms and their state transitions declaratively on top of a relational database. QUEN has three layers: 1) an ontological layer representing norms, 2) a specification of norm state transitions in response to database events, and 3) a legacy relational database storing events. It demonstrates QUEN on an example of patient data access consent, modeling authorizations and their lifecycles. Norm state queries are answered directly over the database using the declarative specifications without materializing states.
Presentation ad EDOC 2019 on monitoring multi-perspective business constraints accounting for time and data, with a specific focus on the (unsolvable in general) problem of conflict detection.
1) The document discusses business process management and how conceptual modeling and process mining can help understand and improve digital enterprises.
2) Process mining techniques like process discovery from event logs, decision mining, and social network mining can provide insights into how processes are executed in reality.
3) Replay techniques can enhance process models with timing information and detect deviations to help align actual behaviors with expected behaviors.
Presentation at BPM 2019, focused on a data-aware extension of BPMN encompassing read-write and read-only data, and on SMT-techniques for effectively tackling parameterized verification of the resulting integrated models.
Presentation at BPM 2019, proposing object-centric behavioral constraints as a modeling approach to capture real-life processes with many-to-many and one-to-many relations, and bringing forward a formal semantics and automated reasoning techniques grounded in temporal description logics.
Keynote at DEC2H 2019, giving an overview of our research on integrated models that respectively put DMN decisions in the context of background domain knowledge and business processes.
An introductory class for PhD students on "how to present scientific work". The class overviews the main types of presentation to which young researchers are exposed and the main critical points to consider when preparing a presentation, with various practical suggestions and examples (partly personal, and partly taken from the book "The Craft of Scientific Presentation").
This document discusses data-aware business processes and data-centric process modeling. It introduces data-centric dynamic systems (DCDSs) as a formal model for artifact-centric systems that uses a relational data layer and process layer to evolve the data. An example of a travel reimbursement process is used to illustrate DCDS modeling, including defining the data schema, modeling process actions that update the data, and showing an example execution. Automated enactment and verification of DCDS models is also discussed.
A presentation summarizing a decade of research on declarative, constraint-based process modeling. In particular, the presentation focuses on the usage of linear temporal logic on finite traces, and the corresponding automata-theoretic characterization, to support constraint-based processes during their entire lifecycle, from model consistency to enactment, monitoring, and discovery.
First part of my tutorial (jointly with Diego Calvanese) on the "Integrated Modeling and Verification of Processes and Data", held at the BPM 2017 conference. The first part focuses on the motivation for combining processes and data, and a review of the state of the art.
The document discusses approaches for modeling processes and data, focusing on ensuring state-boundedness. It describes the concept of data-centric dynamic systems (DCDSs) and how subclasses with decidable state-boundedness correspond to variants of Petri nets. While checking state-boundedness is undecidable in general, the document outlines strategies like identifying syntactic conditions or designing methods that guarantee it. It also discusses how modeling languages can combine unboundedly many cases while retaining verification decidability through data isolation and relative boundedness.
The document discusses data-centric dynamic systems (DCDS), which combine a data layer and a process layer. The data layer consists of a relational database or ontology, while the process layer contains atomic actions, condition-action rules, and service calls. DCDS can model both deterministic and non-deterministic service semantics. An example of a hotel booking system demonstrates a DCDS with currency conversion processes and data. Verification of DCDS is challenging due to their infinite state space resulting from the combination of temporal and first-order properties.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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More from Faculty of Computer Science - Free University of Bozen-Bolzano
Presentation of the paper "Soundness of Data-Aware Processes with Arithmetic Conditions" at the 34th International Conference on Advanced Information Systems Engineering (CAiSE 2022). Paper available here: https://doi.org/10.1007/978-3-031-07472-1_23
Abstract:
Data-aware processes represent and integrate structural and behavioural constraints in a single model, and are thus increasingly investigated in business process management and information systems engineering. In this spectrum, Data Petri nets (DPNs) have gained increasing popularity thanks to their ability to balance simplicity with expressiveness. The interplay of data and control-flow makes checking the correctness of such models, specifically the well-known property of soundness, crucial and challenging. A major shortcoming of previous approaches for checking soundness of DPNs is that they consider data conditions without arithmetic, an essential feature when dealing with real-world, concrete applications. In this paper, we attack this open problem by providing a foundational and operational framework for assessing soundness of DPNs enriched with arithmetic data conditions. The framework comes with a proof-of-concept implementation that, instead of relying on ad-hoc techniques, employs off-the-shelf established SMT technologies. The implementation is validated on a collection of examples from the literature, and on synthetic variants constructed from such examples.
Presentation of the paper "Probabilistic Trace Alignment" at the 3rd International Conference on Process Mining (ICPM 2021). Paper available here: https://doi.org/10.1109/ICPM53251.2021.9576856
Abstract:
Alignments provide sophisticated diagnostics that pinpoint deviations in a trace with respect to a process model. Alignment-based approaches for conformance checking have so far used crisp process models as a reference. Recent probabilistic conformance checking approaches check the degree of conformance of an event log as a whole with respect to a stochastic process model, without providing alignments. For the first time, we introduce a conformance checking approach based on trace alignments using stochastic Workflow nets. This requires to handle the two possibly contrasting forces of the cost of the alignment on the one hand and the likelihood of the model trace with respect to which the alignment is computed on the other.
Presentation of the paper "Strategy Synthesis for Data-Aware Dynamic Systems with Multiple Actors" at the 7th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020). Paper available here: https://proceedings.kr.org/2020/32/
Abstract: The integrated modeling and analysis of dynamic systems and the data they manipulate has been long advocated, on the one hand, to understand how data and corresponding decisions affect the system execution, and on the other hand to capture how actions occurring in the systems operate over data. KR techniques proved successful in handling a variety of tasks over such integrated models, ranging from verification to online monitoring. In this paper, we consider a simple, yet relevant model for data-aware dynamic systems (DDSs), consisting of a finite-state control structure defining the executability of actions that manipulate a finite set of variables with an infinite domain. On top of this model, we consider a data-aware version of reactive synthesis, where execution strategies are built by guaranteeing the satisfaction of a desired linear temporal property that simultaneously accounts for the system dynamics and data evolution.
Presentation of the paper "Extending Temporal Business Constraints with Uncertainty" at the 18th Int. Conference on Business Process Management (BPM 2020). Paper available here: https://doi.org/10.1007/978-3-030-58666-9_3
Abstract: Temporal business constraints have been extensively adopted to declaratively capture the acceptable courses of execution in a business process. However, traditionally, constraints are interpreted logically in a crisp way: a process execution trace conforms with a constraint model if all the constraints therein are satisfied. This is too restrictive when one wants to capture best practices, constraints involving uncontrollable activities, and exceptional but still conforming behaviors. This calls for the extension of business constraints with uncertainty. In this paper, we tackle this timely and important challenge, relying on recent results on probabilistic temporal logics over finite traces. Specifically, our contribution is threefold. First, we delve into the conceptual meaning of probabilistic constraints and their semantics. Second, we argue that probabilistic constraints can be discovered from event data using existing techniques for declarative process discovery. Third, we study how to monitor probabilistic constraints, where constraints and their combinations may be in multiple monitoring states at the same time, though with different probabilities.
Presentation of the paper "Extending Temporal Business Constraints with Uncertainty" at the CAiSE2020 Forum. The paper is available here: https://link.springer.com/chapter/10.1007/978-3-030-58135-0_8
Abstract: Conformance checking is a fundamental task to detect deviations between the actual and the expected courses of execution of a business process. In this context, temporal business constraints have been extensively adopted to declaratively capture the expected behavior of the process. However, traditionally, these constraints are interpreted logically in a crisp way: a process execution trace conforms with a constraint model if all the constraints therein are satisfied. This is too restrictive when one wants to capture best practices, constraints involving uncontrollable activities, and exceptional but still conforming behaviors. This calls for the extension of business constraints with uncertainty. In this paper, we tackle this timely and important challenge, relying on recent results on probabilistic temporal logics over finite traces. Specifically, we equip business constraints with a natural, probabilistic notion of uncertainty. We discuss the semantic implications of the resulting framework and show how probabilistic conformance checking and constraint entailment can be tackled therein.
Keynote speech at the Belgian Process Mining Research Day 2021. I discuss the open, critical challenge of data preparation in process mining, considering the case where the original event data are implicitly stored in (legacy) relational databases. This case covers the common situation where event data are stored inside the data layer of an ERP or CRM system. This is usually handled using manual, ad-hoc, error-prone ETL procedures. I propose instead to adopt a pipeline based on semantic technologies, in particular the framework of ontology-based data access (also known as virtual knowledge graph). The approach is code-less, and relies on three main conceptual steps: (1) the creation of a data model capturing the relevant classes, attributes, and associations in the domain of interest (2) the definition of declarative mappings from the source database to the data model, following the ontology-based data access paradigm (3) the annotation of the data model with indications on which classes/associations/attributes provide the relevant notions of case, events, event attributes, and event-to-case relation. Once this is done, the framework automatically extracts the event log from the legacy data. This makes extremely smooth to generate logs by taking multiple perspectives on the same reality. The approach has been operationalized in the onprom tool, which employs semantic web standard languages for the various steps, and the XES standard as the target format for the event logs.
Keynote speech at the 7th International Workshop on DEClarative, DECision and Hybrid approaches to processes ( DEC2H 2019) In conjunction with BPM 2019.
This is a talk about the combined modeling and reasoning techniques for decisions, background knowledge, and work processes.
The advent of the OMG Decision Model and Notation (DMN) standard has revived interest, both from academia and industry, in decision management and its relationship with business process management. Several techniques and tools for the static analysis of decision models have been brought forward, taking advantage of the trade-off between expressiveness and computational tractability offered by the DMN S-FEEL language.
In this keynote, I argue that decisions have to be put in perspective, that is, understood and analyzed within their surrounding organizational boundaries. This brings new challenges that, in turn, require novel, advanced analysis techniques. Using a simple but illustrative example, I consider in particular two relevant settings: decisions interpreted the presence of background, structural knowledge of the domain of interest, and (data-aware) business processes routing process instances based on decisions. Notably, the latter setting is of particular interest in the context of multi-perspective process mining. I report on how we successfully tackled key analysis tasks in both settings, through a balanced combination of conceptual modeling, formal methods, and knowledge representation and re
Presentation at "Ontology Make Sense", an event in honor of Nicola Guarino, on how to integrate data models with behavioral constraints, an essential problem when modeling multi-case real-life work processes evolving multiple objects at once. I propose to combine UML class diagrams with temporal constraints on finite traces, linked to the data model via co-referencing constraints on classes and associations.
The document discusses representing and querying norm states using temporal ontology-based data access (OBDA). It presents the QUEN framework which models norms and their state transitions declaratively on top of a relational database. QUEN has three layers: 1) an ontological layer representing norms, 2) a specification of norm state transitions in response to database events, and 3) a legacy relational database storing events. It demonstrates QUEN on an example of patient data access consent, modeling authorizations and their lifecycles. Norm state queries are answered directly over the database using the declarative specifications without materializing states.
Presentation ad EDOC 2019 on monitoring multi-perspective business constraints accounting for time and data, with a specific focus on the (unsolvable in general) problem of conflict detection.
1) The document discusses business process management and how conceptual modeling and process mining can help understand and improve digital enterprises.
2) Process mining techniques like process discovery from event logs, decision mining, and social network mining can provide insights into how processes are executed in reality.
3) Replay techniques can enhance process models with timing information and detect deviations to help align actual behaviors with expected behaviors.
Presentation at BPM 2019, focused on a data-aware extension of BPMN encompassing read-write and read-only data, and on SMT-techniques for effectively tackling parameterized verification of the resulting integrated models.
Presentation at BPM 2019, proposing object-centric behavioral constraints as a modeling approach to capture real-life processes with many-to-many and one-to-many relations, and bringing forward a formal semantics and automated reasoning techniques grounded in temporal description logics.
Keynote at DEC2H 2019, giving an overview of our research on integrated models that respectively put DMN decisions in the context of background domain knowledge and business processes.
An introductory class for PhD students on "how to present scientific work". The class overviews the main types of presentation to which young researchers are exposed and the main critical points to consider when preparing a presentation, with various practical suggestions and examples (partly personal, and partly taken from the book "The Craft of Scientific Presentation").
This document discusses data-aware business processes and data-centric process modeling. It introduces data-centric dynamic systems (DCDSs) as a formal model for artifact-centric systems that uses a relational data layer and process layer to evolve the data. An example of a travel reimbursement process is used to illustrate DCDS modeling, including defining the data schema, modeling process actions that update the data, and showing an example execution. Automated enactment and verification of DCDS models is also discussed.
A presentation summarizing a decade of research on declarative, constraint-based process modeling. In particular, the presentation focuses on the usage of linear temporal logic on finite traces, and the corresponding automata-theoretic characterization, to support constraint-based processes during their entire lifecycle, from model consistency to enactment, monitoring, and discovery.
First part of my tutorial (jointly with Diego Calvanese) on the "Integrated Modeling and Verification of Processes and Data", held at the BPM 2017 conference. The first part focuses on the motivation for combining processes and data, and a review of the state of the art.
The document discusses approaches for modeling processes and data, focusing on ensuring state-boundedness. It describes the concept of data-centric dynamic systems (DCDSs) and how subclasses with decidable state-boundedness correspond to variants of Petri nets. While checking state-boundedness is undecidable in general, the document outlines strategies like identifying syntactic conditions or designing methods that guarantee it. It also discusses how modeling languages can combine unboundedly many cases while retaining verification decidability through data isolation and relative boundedness.
The document discusses data-centric dynamic systems (DCDS), which combine a data layer and a process layer. The data layer consists of a relational database or ontology, while the process layer contains atomic actions, condition-action rules, and service calls. DCDS can model both deterministic and non-deterministic service semantics. An example of a hotel booking system demonstrates a DCDS with currency conversion processes and data. Verification of DCDS is challenging due to their infinite state space resulting from the combination of temporal and first-order properties.
More from Faculty of Computer Science - Free University of Bozen-Bolzano (20)
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
Modeling and Reasoning over Declarative Data-Aware Processes with Object-Centric Behavioral Constraints
1. Modeling and Reasoning over
declarative data-aware processes
with object-centric behavioural
constraints
Alessandro Artale, Alisa Kovtunova, Marco Montali, Wil M. P. van der Aalst
5. Modeling with conventional notations
Two main commitments:
• A business process should be con
fi
ned within a pool.
• A pool relates to a case object type.
11. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
12. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
13. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
14. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
15. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
16. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
serialisation
17. Case-Centricity
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
6 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
Hiring
Company
Candidate
post
offer
check if
eligible
Application
…
Job Offer
(a) A job hiring process receiving at most
one application
Hiring
Company
post
offer
Application
Job Offer
handle candidate
…
check if
eligible
Candidate
(b) A job hiring process receiving multiple applica-
tions in a sequential way; a new application is only han-
dled when the previous applications has been checked
for eligibility
Fig. 2: Common beginner mistakes when capturing a job hiring process (diagrams
inspired from [12])
serialisation
•Multiple pools required
•Additional constructs needed
•Synchronization issues
33. data model
(UML class diagram)
Activities refer to classes
C1 C2
R
activities
A B C
# # #
1 1
R1 R2 R3
34. data model
(UML class diagram)
Activities refer to classes
C1 C2
R
activities
A B C
# # #
1 1
R1 R2 R3
“generating”
35. Hiring Example
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
which instances of tasks are related by the constraint depending on
manipulate.
In particular, the relevant constraints for our job hiring example are:
C.1 The register data task is about a Person.
C.2 A Job Offer is created by executing the post offer task.
C.3 A Job Offer is closed by determining the winner.
C.4 A Job Offer is stopped by canceling the hiring.
Enriching Data Models with Behavioral Co
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data a
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least o
responding to that Job Offer has been previously marked as el
C.9 For each Application responding to a Job Offer, if the Applica
as eligible then a winner must be finally determined for that
this is done only once for that Job Offer.
C.10 When a winner is determined for a Job Offer, Applications res
36. Hiring Example
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
which instances of tasks are related by the constraint depending on
manipulate.
In particular, the relevant constraints for our job hiring example are:
C.1 The register data task is about a Person.
C.2 A Job Offer is created by executing the post offer task.
C.3 A Job Offer is closed by determining the winner.
C.4 A Job Offer is stopped by canceling the hiring.
Enriching Data Models with Behavioral Co
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data a
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least o
responding to that Job Offer has been previously marked as el
C.9 For each Application responding to a Job Offer, if the Applica
as eligible then a winner must be finally determined for that
this is done only once for that Job Offer.
C.10 When a winner is determined for a Job Offer, Applications res
37. “Emerging” Business Artifacts
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
“Application”
“Job O
ff
er”
many
one
38. Expressing the Process
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
The most fundamental issue when trying to capture the job hiring example of Section 2.1
using case-centric notation is to identify what is the case. This, in turn, determines
what is the orchestration point for the process, that is, which participant coordinates
process instances corresponding to different case objects. This problem is apparent when
looking at BPMN, which specifies that each process should correspond to a single locus
of control, i.e., confined within a single pool.4
In our example, we have two participants: candidates (in turn responsible for man-
aging Applications), and the job hiring organisation (in turn responsible for the
management of JobOffers). However, we cannot use neither of the two to act as unique
locus of control for the process: on the one hand, candidates may simultaneously create
and manage different applications for different job offers; on the other hand, the organi-
sation may simultaneously spawn and manage different job offers, each one resulting
Enriching Data Models with Behavioral Constraints 5
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
39. Expressing the Process
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
The most fundamental issue when trying to capture the job hiring example of Section 2.1
using case-centric notation is to identify what is the case. This, in turn, determines
what is the orchestration point for the process, that is, which participant coordinates
process instances corresponding to different case objects. This problem is apparent when
looking at BPMN, which specifies that each process should correspond to a single locus
of control, i.e., confined within a single pool.4
In our example, we have two participants: candidates (in turn responsible for man-
aging Applications), and the job hiring organisation (in turn responsible for the
management of JobOffers). However, we cannot use neither of the two to act as unique
locus of control for the process: on the one hand, candidates may simultaneously create
and manage different applications for different job offers; on the other hand, the organi-
sation may simultaneously spawn and manage different job offers, each one resulting
Enriching Data Models with Behavioral Constraints 5
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
temporal constraints
40. Temporal Constraints
8 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A B
response
A B
unary-response
A B
non-response
A B
precedence
A B
unary-precedence
A B
non-precedence
A B
responded-existence
A B
non-coexistence
Fig. 3: Types of temporal constraints between activities
response(A, B) If A is executed, then B must be executed afterwards.
unary- response(A, B) If A is executed, then B must be executed exactly once after-
wards.
precedence(A, B) If A is executed, then B must have been executed before.
Inspired by the Declare declarative process modeling notation
41. Is That Enough? No!
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
The most fundamental issue when trying to capture the job hiring example of Section 2.1
using case-centric notation is to identify what is the case. This, in turn, determines
what is the orchestration point for the process, that is, which participant coordinates
process instances corresponding to different case objects. This problem is apparent when
looking at BPMN, which specifies that each process should correspond to a single locus
of control, i.e., confined within a single pool.4
In our example, we have two participants: candidates (in turn responsible for man-
aging Applications), and the job hiring organisation (in turn responsible for the
management of JobOffers). However, we cannot use neither of the two to act as unique
locus of control for the process: on the one hand, candidates may simultaneously create
and manage different applications for different job offers; on the other hand, the organi-
sation may simultaneously spawn and manage different job offers, each one resulting
Enriching Data Models with Behavioral Constraints 5
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
temporal constraints
42. Is That Enough? No!
10 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
The most fundamental issue when trying to capture the job hiring example of Section 2.1
using case-centric notation is to identify what is the case. This, in turn, determines
what is the orchestration point for the process, that is, which participant coordinates
process instances corresponding to different case objects. This problem is apparent when
looking at BPMN, which specifies that each process should correspond to a single locus
of control, i.e., confined within a single pool.4
In our example, we have two participants: candidates (in turn responsible for man-
aging Applications), and the job hiring organisation (in turn responsible for the
management of JobOffers). However, we cannot use neither of the two to act as unique
locus of control for the process: on the one hand, candidates may simultaneously create
and manage different applications for different job offers; on the other hand, the organi-
sation may simultaneously spawn and manage different job offers, each one resulting
Enriching Data Models with Behavioral Constraints 5
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
temporal constraints
co-reference on object (V co-reference)
co-reference on relation (U co-reference)
43. Behavioral Constraints
Temporal constraints + V/U co-reference through the data model
12 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A1 A2
O
R1 R2
Every time an instance a1 of A1 is executed
on some object o of type O (i.e., with R1(a1, o)),
then an instance a2 of A2 must be executed afterwards
on the same object o (i.e., with R2(a2, o))
(a) Co-reference of response over an object class
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A1 is executed
on some object o1 of type O1 (i.e., with R1(a1, o1)),
then an instance a2 of A2 must be executed afterwards
on some object o2 of type O2 (i.e., with R2(a2, o2))
that relates to o1 via R
(i.e., having R(o1, o2) at the moment of execution of a2).
(b) Co-reference of response over a relationship
8 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A B
response
A B
unary-response
A B
non-response
A B
precedence
A B
unary-precedence
A B
non-precedence
A B
responded-existence
A B
non-coexistence
Fig. 3: Types of temporal constraints between activities
response(A, B) If A is executed, then B must be executed afterwards.
unary- response(A, B) If A is executed, then B must be executed exactly once after-
wards.
precedence(A, B) If A is executed, then B must have been executed before.
unary- precedence(A, B) If A is executed, then B must have been executed exactly once
before.
responded- existence(A, B) If A is execute, then B must also be executed (either before or
afterwards).
non-response(A, B) If A is executed, then B will not be executed afterwards.
12 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A1 A2
O
R1 R2
Every time an instance a1 of A1 is executed
on some object o of type O (i.e., with R1(a1, o)),
then an instance a2 of A2 must be executed afterwards
on the same object o (i.e., with R2(a2, o))
(a) Co-reference of response over an object class
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A1 is executed
on some object o1 of type O1 (i.e., with R1(a1, o1)),
then an instance a2 of A2 must be executed afterwards
on some object o2 of type O2 (i.e., with R2(a2, o2))
that relates to o1 via R
(i.e., having R(o1, o2) at the moment of execution of a2).
(b) Co-reference of response over a relationship
8 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A B
response
A B
unary-response
A B
non-response
A B
precedence
A B
unary-precedence
A B
non-precedence
A B
responded-existence
A B
non-coexistence
Fig. 3: Types of temporal constraints between activities
Every time
an instance a of A is executed
on some object o of type O (i.e., with R1(a,o))
then
an instance b of B must be executed afterwards
on the same object o (i.e., with R2(b,o))
Every time
an instance a of A is executed
on some object o1 of type O (i.e., with R1(a,o1))
then
an instance b of B must be executed afterwards
on some object o2 of type O2 (i.e., with R2(b,o2))
that relates to o1 via R
(with R(o1,o2) contextually with the execution of b)
44. Hiring Constraints
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
The most fundamental issue when trying to capture the job hiring example of Section 2.1
using case-centric notation is to identify what is the case. This, in turn, determines
what is the orchestration point for the process, that is, which participant coordinates
process instances corresponding to different case objects. This problem is apparent when
looking at BPMN, which specifies that each process should correspond to a single locus
of control, i.e., confined within a single pool.4
In our example, we have two participants: candidates (in turn responsible for man-
aging Applications), and the job hiring organisation (in turn responsible for the
management of JobOffers). However, we cannot use neither of the two to act as unique
locus of control for the process: on the one hand, candidates may simultaneously create
and manage different applications for different job offers; on the other hand, the organi-
sation may simultaneously spawn and manage different job offers, each one resulting
Enriching Data Models with Behavioral Constraints 5
C.5 An Application is created by executing the submit task.
C.6 An Application is promoted by marking it as eligible.
C.7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
C.8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
C.9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
.10 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
.11 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2.2 Capturing the Job Hiring Example with Case-Centric Notations
Enriching Data Models with Behavioral Constraints 13
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
c
c
45. Further Hiring Constraints
Enriching Data Models with Behavioral Constraints 13
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
5 An Application is created by executing the submit task.
6 An Application is promoted by marking it as eligible.
7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
0 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
1 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
2 Capturing the Job Hiring Example with Case-Centric Notations
he most fundamental issue when trying to capture the job hiring example of Section 2.1
sing case-centric notation is to identify what is the case. This, in turn, determines
hat is the orchestration point for the process, that is, which participant coordinates
rocess instances corresponding to different case objects. This problem is apparent when
ooking at BPMN, which specifies that each process should correspond to a single locus
4
46. Key Questions
1. What is the semantics of these
models?
2. Can we do automated reasoning?
Consistency, dead activities, implied
constraints…
Also relevant for process mining: discovering single
constraints does not guarantee model correctness!
47. Implied Constraints
(Hidden Dependencies)
Enriching Data Models with Behavioral Constraints 13
is about
1
1
creates
1
promotes
1
creates
1
1
stops
1
closes
1
Person
Candidate Application Job Offer Job Profile
1
/ made by
1..⇤ ⇤
responds to
1 ⇤
refers to
1
register
data submit
mark as
eligible
post
offer
cancel
hiring
determine
winner
6 An Application is promoted by marking it as eligible.
7 An Application can be submitted only if, beforehand, the data about the Candi-
date who made that Application have been registered.
8 A winner can be determined for a Job Offer only if at least one Application
responding to that Job Offer has been previously marked as eligible.
9 For each Application responding to a Job Offer, if the Application is marked
as eligible then a winner must be finally determined for that Job Offer, and
this is done only once for that Job Offer.
0 When a winner is determined for a Job Offer, Applications responding to that
Job Offer cannot be marked as eligible anymore.
1 A Job Offer closed by a determine winner task cannot be stopped by executing
the cancel hiring task (and vice-versa).
.2 Capturing the Job Hiring Example with Case-Centric Notations
he most fundamental issue when trying to capture the job hiring example of Section 2.1
sing case-centric notation is to identify what is the case. This, in turn, determines
hat is the orchestration point for the process, that is, which participant coordinates
rocess instances corresponding to different case objects. This problem is apparent when
ooking at BPMN, which specifies that each process should correspond to a single locus
f control, i.e., confined within a single pool.4
In our example, we have two participants: candidates (in turn responsible for man-
ging Applications), and the job hiring organisation (in turn responsible for the
The OCBC model implies:
48. Temporal Description Logics
Family of KR logical formalisms to represent and
reason about static, structural knowledge.
Provide foundations to:
• data models;
• ontologies and semantic web.
We focus on ALCQI:
• captures UML class diagrams, E-R, ORM.
49. Temporal Description Logics
OBDA for Log Extraction in Process Mining 25
Paper
title : String
type : String
Person
pName : String
regTime: ts
Assignment
invTime: ts
Submission
uploadTime: ts
CRUpload Creation
DecidedPaper
decTime: ts
accepted: boolean
notifiedBy
Review
subTime: ts
leadsTo
Conference
cName: String
crTime: ts
submittedTo
chairs
*
*
*
1..*
*
1
1
0..1
* 1
1
*
Fig. 9: Data model of our CONFSYS running example
Correctness of the Encoding. The encoding we have provided is faithful, in the sense
that it fully preserves in the DL-LiteA ontology the semantics of the UML class diagram.
Obviously, since, due to reification, the ontology alphabet may contain additional sym-
bols with respect to those used in the UML class diagram, the two specifications cannot
have the same logical models. However, it is possible to show that the logical models
of a UML class diagram and those of the DL-LiteA ontology derived from it correspond
to each other, and hence that satisfiability of a class or association in the UML diagram
corresponds to satisfiability of the corresponding concept or role [29,7].
Example 9. We illustrate the encoding of UML class diagrams in DL-LiteA on the
UML class diagram shown in Figure 9, which depicts (a simplified version of) the in-
formation model of the CONFSYS conference submission system used for our running
example. We assume that the components of associations are given from left to right
and from top to bottom. Papers are represented through the Paper class, with attributes
A
L
C
Q
I
50. Temporal Description Logics
(title) ⌘ Paper
⇢(title) v string
(funct title)
(type) ⌘ Paper
⇢(type) v string
(funct type)
(decTime) ⌘ DecidedPaper
⇢(decTime) v ts
(funct decTime)
(accepted) ⌘ DecidedPaper
⇢(accepted) v boolean
(funct accepted)
(pName) ⌘ Person
⇢(pName) v string
(funct pName)
(regTime) ⌘ Person
⇢(regTime) v ts
(funct regTime)
(cName) ⌘ Conference
⇢(cName) v string
(funct cName)
(crTime) ⌘ Conference
⇢(crTime) v ts
(funct crTime)
(uploadTime) ⌘ Submission
⇢(uploadTime) v ts
(funct uploadTime)
(invTime) ⌘ Assignment
⇢(invTime) v ts
(funct invTime)
(subTime) ⌘ Review
⇢(subTime) v ts
(funct subTime)
DecidedPaper v Paper
Creation v Submission
CRUpload v Submission
9Submission1 ⌘ Submission
9Submission1 ⌘ Paper
(funct Submission1)
9Submission2 ⌘ Submission
9Submission2 v Person
(funct Submission2)
9Assignment1 ⌘ Assignment
9Assignment1 v Paper
(funct Assignment1)
9Assignment2 ⌘ Assignment
9Assignment2 v Person
(funct Assignment2)
9leadsTo v Assignment
9leadsTo ⌘ Review
(funct leadsTo)
(funct leadsTo )
9submittedTo ⌘ Paper
9submittedTo v Conference
(funct submittedTo)
9notifiedBy ⌘ DecidedPaper
9notifiedBy v Person
(funct notifiedBy)
9chairs v Person
9chairs ⌘ Conference
(funct chairs )
OBDA for Log Extraction in Process Mining 25
Paper
title : String
type : String
Person
pName : String
regTime: ts
Assignment
invTime: ts
Submission
uploadTime: ts
CRUpload Creation
DecidedPaper
decTime: ts
accepted: boolean
notifiedBy
Review
subTime: ts
leadsTo
Conference
cName: String
crTime: ts
submittedTo
chairs
*
*
*
1..*
*
1
1
0..1
* 1
1
*
Fig. 9: Data model of our CONFSYS running example
Correctness of the Encoding. The encoding we have provided is faithful, in the sense
that it fully preserves in the DL-LiteA ontology the semantics of the UML class diagram.
Obviously, since, due to reification, the ontology alphabet may contain additional sym-
bols with respect to those used in the UML class diagram, the two specifications cannot
have the same logical models. However, it is possible to show that the logical models
of a UML class diagram and those of the DL-LiteA ontology derived from it correspond
to each other, and hence that satisfiability of a class or association in the UML diagram
corresponds to satisfiability of the corresponding concept or role [29,7].
Example 9. We illustrate the encoding of UML class diagrams in DL-LiteA on the
UML class diagram shown in Figure 9, which depicts (a simplified version of) the in-
formation model of the CONFSYS conference submission system used for our running
example. We assume that the components of associations are given from left to right
and from top to bottom. Papers are represented through the Paper class, with attributes
A
L
C
Q
I
51. Temporal Description Logics
Family of KR logical formalisms to represent
and reason about time.
We focus on LTL:
• models: execution traces;
• (in its
fi
nite-trace version) at the basis of the
Declare process modeling language.
52. 8 A. Artale, D. Calvanese, M. Montali, and W. van
A B
response
A
unary-respons
A B
precedence
A
unary-preceden
A B
responded-existence
Fig. 3: Types of temporal constraints
response(A, B) If A is executed, then B
unary- response(A, B) If A is executed, then B
Calvanese, M. Montali, and W. van der Aalst
B A B
unary-response
A B
non-response
B A B
unary-precedence
A B
non-precedence
B
tence
A B
non-coexistence
3: Types of temporal constraints between activities
If A is executed, then B must be executed afterwards.
B) If A is executed, then B must be executed exactly once after-
8 A. Artale, D. Calvanese, M. Montali, and W. van
A B
response
A
unary-respons
A B
precedence
A
unary-preceden
A B
responded-existence
Fig. 3: Types of temporal constraints
response(A, B) If A is executed, then B
L
T
L
Temporal Description Logics
53. 8 A. Artale, D. Calvanese, M. Montali, and W. van
A B
response
A
unary-respons
A B
precedence
A
unary-preceden
A B
responded-existence
Fig. 3: Types of temporal constraints
response(A, B) If A is executed, then B
unary- response(A, B) If A is executed, then B
Calvanese, M. Montali, and W. van der Aalst
B A B
unary-response
A B
non-response
B A B
unary-precedence
A B
non-precedence
B
tence
A B
non-coexistence
3: Types of temporal constraints between activities
If A is executed, then B must be executed afterwards.
B) If A is executed, then B must be executed exactly once after-
8 A. Artale, D. Calvanese, M. Montali, and W. van
A B
response
A
unary-respons
A B
precedence
A
unary-preceden
A B
responded-existence
Fig. 3: Types of temporal constraints
response(A, B) If A is executed, then B
L
T
L
Temporal Description Logics
2(A ! 3F B)
2(A ! 3PB)
2(A ! 2
⇤ ¬B)
ns available to model business processes,
ife processes using mainstream notations
54. Temporal Description Logics
Combine static and dynamic aspects into a unique logical
framework.
LTL+DLs —> the right framework to capture OCBC.
Models: data-aware traces!
o1 : Order
ol1 : Order Line
ol2 : Order Line
ol3 : Order Line
d1 : Delivery
d2 : Delivery
...
...
...
...
...
...
co1 : Create Order
pi1 : Pick Item
pi2 : Pick Item
wi1 : Wrap Item
wi2 : Wrap Item
pi3 : Pick Item
wi3 : Wrap Item
po1 : Pay Order
di1 : Deliver Items
di2 : Deliver Items
t0 t1 t2 t3 t4 t5 t6 t7 t8 t9
creates
fills
contains
fills
contains
prepares
prepares
fills
contains
prepares
closes
refers to
results in
results in
refers to
results in
Fig. 3: Trace fragment for the OCBC model in Fig. 2
55. Combining is dangerous:
undecidability around the corner!
Can we formalize OCBC in a well-behaved temporal DL?
56. Which Route?
A1 A2
O
R1 R2
Every time an instance a1 of
on some object o of type O (
then an instance a2 of A2 mu
on the same object o (i.e., wi
(a) Co-reference of response over an object cl
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A
on some object o1 of type O1
then an instance a2 of A2 mus
on some object o2 of type O2
that relates to o1 via R
(i.e., having R(o1, o2) at the m
(b) Co-reference of response over a relationsh
A1 A2
O
R1 R2
Every time an instance a1 of A
on some object o of type O (i.e
then no instance a2 of A2 that
(i.e., with R2(a2, o)) can be ex
57. Which Route?
A1 A2
O
R1 R2
Every time an instance a1 of
on some object o of type O (
then an instance a2 of A2 mu
on the same object o (i.e., wi
(a) Co-reference of response over an object cl
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A
on some object o1 of type O1
then an instance a2 of A2 mus
on some object o2 of type O2
that relates to o1 via R
(i.e., having R(o1, o2) at the m
(b) Co-reference of response over a relationsh
A1 A2
O
R1 R2
Every time an instance a1 of A
on some object o of type O (i.e
then no instance a2 of A2 that
(i.e., with R2(a2, o)) can be ex
Chaining relations across time
58. Question
A1 A2
O
R1 R2
Every time an instance a1 of
on some object o of type O (
then an instance a2 of A2 mu
on the same object o (i.e., wi
(a) Co-reference of response over an object cl
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A
on some object o1 of type O1
then an instance a2 of A2 mus
on some object o2 of type O2
that relates to o1 via R
(i.e., having R(o1, o2) at the m
(b) Co-reference of response over a relationsh
A1 A2
O
R1 R2
Every time an instance a1 of A
on some object o of type O (i.e
then no instance a2 of A2 that
(i.e., with R2(a2, o)) can be ex
Do we need to global constraints on activities,
without co-referencing in the data model?
No: everything scoped by data objects!
59. Activities Fade Away
A1 A2
O
R1 R2
Every time an instance a1 of
on some object o of type O (
then an instance a2 of A2 mu
on the same object o (i.e., wi
(a) Co-reference of response over an object cl
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A
on some object o1 of type O1
then an instance a2 of A2 mus
on some object o2 of type O2
that relates to o1 via R
(i.e., having R(o1, o2) at the m
(b) Co-reference of response over a relationsh
A1 A2
O
R1 R2
Every time an instance a1 of A
on some object o of type O (i.e
then no instance a2 of A2 that
(i.e., with R2(a2, o)) can be ex
Do we need to global constraints on activities,
without co-referencing in the data model?
The temporal DL TusALCQI su
ffi
ces!
[Combines ALCQI with LTL over concepts only]
No: everything scoped by data objects!
Temporal constraint
over O1 and O2
62. Response Example
12 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A1 A2
O
R1 R2
Every time an instance a1 of A1 is execut
on some object o of type O (i.e., with R1(
then an instance a2 of A2 must be execute
on the same object o (i.e., with R2(a2, o))
(a) Co-reference of response over an object class
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A1 is executed
on some object o1 of type O1 (i.e., with R1(
then an instance a2 of A2 must be executed
on some object o2 of type O2 (i.e., with R2(
that relates to o1 via R
(i.e., having R(o1, o2) at the moment of exe
(b) Co-reference of response over a relationship
A1 A2
O
R1 R2
Every time an instance a1 of A1 is executed
on some object o of type O (i.e., with R1(a1
then no instance a2 of A2 that relates to the
(i.e., with R2(a2, o)) can be executed afterw
8 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A B
response
A B
unary-response
A
no
A B
precedence
A B
unary-precedence
A
non
A B
responded-existence
A
non-
Fig. 3: Types of temporal constraints between activit
response(A, B) If A is executed, then B must be executed
unary- response(A, B) If A is executed, then B must be execute
wards.
precedence(A, B) If A is executed, then B must have been ex
unary- precedence(A, B) If A is executed, then B must have been e
Every time an instance a of A is executed
on some object o1 of type O (i.e., with R1(a,o1))
then an instance b of B must be executed afterwards
on some object o2 (i.e., with R2(b,o2)) that relates to o1 via R
(with R(o1,o2) contextually with the execution of b)
63. Response Example
12 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A1 A2
O
R1 R2
Every time an instance a1 of A1 is execut
on some object o of type O (i.e., with R1(
then an instance a2 of A2 must be execute
on the same object o (i.e., with R2(a2, o))
(a) Co-reference of response over an object class
A1 A2
O1 O2
R
R1 R2
Every time an instance a1 of A1 is executed
on some object o1 of type O1 (i.e., with R1(
then an instance a2 of A2 must be executed
on some object o2 of type O2 (i.e., with R2(
that relates to o1 via R
(i.e., having R(o1, o2) at the moment of exe
(b) Co-reference of response over a relationship
A1 A2
O
R1 R2
Every time an instance a1 of A1 is executed
on some object o of type O (i.e., with R1(a1
then no instance a2 of A2 that relates to the
(i.e., with R2(a2, o)) can be executed afterw
8 A. Artale, D. Calvanese, M. Montali, and W. van der Aalst
A B
response
A B
unary-response
A
no
A B
precedence
A B
unary-precedence
A
non
A B
responded-existence
A
non-
Fig. 3: Types of temporal constraints between activit
response(A, B) If A is executed, then B must be executed
unary- response(A, B) If A is executed, then B must be execute
wards.
precedence(A, B) If A is executed, then B must have been ex
unary- precedence(A, B) If A is executed, then B must have been e
Every time an instance a of A is executed
on some object o1 of type O (i.e., with R1(a,o1))
then an instance b of B must be executed afterwards
on some object o2 (i.e., with R2(b,o2)) that relates to o1 via R
(with R(o1,o2) contextually with the execution of b)
Every time some object on some object o1 of type O2
is subject to the execution of some instance of A (i.e., is target of R1)
then afterwards some object o2 of type O2
must be subject to the execution of some instance of B (i.e., is target of R2)
64. Reasoning
Consistency, dead activity detection, checking
implied constraints of OCBC models can be reduced
to standard reasoning in TusALCQI
—> ExpTime upper bound
Also conditional compliance: given a data-aware
trace, does the trace satis
fi
es the OCBC model,
possibly introducing missing events/data?
If we only adopt V-coreference on classes, and do not
consider covering on UML hierarchies
—> drops to PSPACE (same as plain old LTL)
65. Conclusion
OCBC: a declarative approach
to elegantly capture processes
with co-evolving objects
Formal logic-based semantics
in a temporal DL that enables
automated reasoning