https://sdq.kastel.kit.edu/index.php?title=Data-Flow_Correctness_and_Compliance_Verification_for_Data-Aware_Workflows_in_Energy_Markets&feed=atom&action=historyData-Flow Correctness and Compliance Verification for Data-Aware Workflows in Energy Markets - Versionsgeschichte2024-03-28T18:57:58ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Data-Flow_Correctness_and_Compliance_Verification_for_Data-Aware_Workflows_in_Energy_Markets&diff=517&oldid=prevErik.burger@kit.edu am 13. November 2017 um 09:14 Uhr2017-11-13T09:14:17Z<p></p>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=<del style="font-weight: bold; text-decoration: none;">- Vortrag im SR 010 - </del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in workflows is often considered as second-class object and is not sufficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance verification has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-flow of data</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in workflows is often considered as second-class object and is not sufficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance verification has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-flow of data</div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>objects for a scenario in the energy market domain. For this purpose we use a scientific workflow management system, namely the Apache Taverna. We will then insure the correctness of the data flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna workflows to BPMN 2.0. We then ensure the correctness of the data-flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>objects for a scenario in the energy market domain. For this purpose we use a scientific workflow management system, namely the Apache Taverna. We will then insure the correctness of the data flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna workflows to BPMN 2.0. We then ensure the correctness of the data-flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.</div></td></tr>
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</table>Erik.burger@kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Data-Flow_Correctness_and_Compliance_Verification_for_Data-Aware_Workflows_in_Energy_Markets&diff=515&oldid=prevJutta.muelle@kit.edu am 13. November 2017 um 08:55 Uhr2017-11-13T08:55:36Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in workflows is often considered as second-class object and is not sufficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance verification has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-flow of data</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in workflows is often considered as second-class object and is not sufficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance verification has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-flow of data</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>objects for a scenario in the energy market domain. For this purpose we use a scientific workflow management system, namely the Apache Taverna. We will then insure the correctness of the data flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna workflows to BPMN 2.0. We then ensure the correctness of the data-flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>objects for a scenario in the energy market domain. For this purpose we use a scientific workflow management system, namely the Apache Taverna. We will then insure the correctness of the data flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna workflows to BPMN 2.0. We then ensure the correctness of the data-flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.</div></td></tr>
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</table>Jutta.muelle@kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Data-Flow_Correctness_and_Compliance_Verification_for_Data-Aware_Workflows_in_Energy_Markets&diff=514&oldid=prevJutta.muelle@kit.edu am 10. November 2017 um 12:05 Uhr2017-11-10T12:05:38Z<p></p>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in <del style="font-weight: bold; text-decoration: none;">work�flows </del>is often considered as second-class object and is not <del style="font-weight: bold; text-decoration: none;">su�fficiently </del>supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance <del style="font-weight: bold; text-decoration: none;">veri�fication </del>has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-<del style="font-weight: bold; text-decoration: none;">�flow </del>of data</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in <ins style="font-weight: bold; text-decoration: none;">workflows </ins>is often considered as second-class object and is not <ins style="font-weight: bold; text-decoration: none;">sufficiently </ins>supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance <ins style="font-weight: bold; text-decoration: none;">verification </ins>has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-<ins style="font-weight: bold; text-decoration: none;">flow </ins>of data</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>objects for a scenario in the energy market domain. For this purpose we use a <del style="font-weight: bold; text-decoration: none;">scientifi�c work�flow </del>management system, namely the Apache Taverna. We will then insure the correctness of the data <del style="font-weight: bold; text-decoration: none;">�flow </del>of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna <del style="font-weight: bold; text-decoration: none;">work�flows </del>to BPMN 2.0. We then ensure the correctness of the data-<del style="font-weight: bold; text-decoration: none;">�flow </del>of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>objects for a scenario in the energy market domain. For this purpose we use a <ins style="font-weight: bold; text-decoration: none;">scientific workflow </ins>management system, namely the Apache Taverna. We will then insure the correctness of the data <ins style="font-weight: bold; text-decoration: none;">flow </ins>of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna <ins style="font-weight: bold; text-decoration: none;">workflows </ins>to BPMN 2.0. We then ensure the correctness of the data-<ins style="font-weight: bold; text-decoration: none;">flow </ins>of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td></tr>
</table>Jutta.muelle@kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Data-Flow_Correctness_and_Compliance_Verification_for_Data-Aware_Workflows_in_Energy_Markets&diff=513&oldid=prevJutta.muelle@kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Milena Nedelcheva |email=nedelcheva@outlook.com |vortragstyp=Diplomarbeit |betreuer=Jutta Mülle |termin=Institutsseminar/2017-11-24 |k…“2017-11-10T12:04:35Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Milena Nedelcheva |email=nedelcheva@outlook.com |vortragstyp=Diplomarbeit |betreuer=Jutta Mülle |termin=Institutsseminar/2017-11-24 |k…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Milena Nedelcheva<br />
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|kurzfassung=- anderer Raum wird noch gesucht -<br />
Data flow is becoming more and more important for business processes over the last few years. Nevertheless, data in work�flows is often considered as second-class object and is not su�fficiently supported. In many domains, such as the energy market, the importance of compliance requirements stemming form legal regulations or specific standards has dramatically increased over the past few years. To be broadly applicable, compliance veri�fication has to support data-aware compliance rules as well as to consider data conditions within a process model. In this thesis we model the data-�flow of data<br />
objects for a scenario in the energy market domain. For this purpose we use a scientifi�c work�flow management system, namely the Apache Taverna. We will then insure the correctness of the data �flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna work�flows to BPMN 2.0. We then ensure the correctness of the data-�flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.<br />
}}</div>Jutta.muelle@kit.edu