AnServApp 2024

4th International Workshop on Analytics for Service and Application Management (AnServApp)
Monday 28 October 2024

Scope

With enterprise organizations generating petabytes of data each day, their use of, and reliance on data analytics to provide contextual insight into their operations is imperative for improving the implementation, management and delivery of services and applications. Approaches such as predictive data analytics, data mining, machine learning and deep learning are promising mechanisms to harness this immense stream of service and application data to meet the needs of an organization. The main goal of AnServApp is to present research and work-in-progress results in the area of data analytics, machine learning and cognitive science for service and application management. Its interdisciplinary approach offers practical insights and real-world applications that attendees can apply to their work. In addition to regular papers, short papers describing late-breaking advances and work-in-progress reports from ongoing research are also welcomed. The workshop will feature distinguished speakers, and numerous networking opportunities, fostering collaboration and knowledge exchange among researchers, practitioners, and industry experts.

Topics of Interest

Topics of Interest to the workshop include but are not limited to:

• Application Management Services (AMS)
• AI/ML/DL powered solutions
• Network and service security
• Ticket resolution and management
• Event log analysis
• Knowledge management
• Predictive maintenance / Industry 4.0
• Asset tracking and provisioning
• Workload optimization
• Sentiment analysis
• Social media
• Lower carbon foot print applications / services / systems
• Smart cities and smart transportation services / systems
• Social media apps / services / systems
• Smart education services / systems
• Edge, fog, cloud services / systems
• Sustainability and resilience of applications / services / systems
• Digital Twins for networks and services
• Resource management & orchestration
• Multi-Agent Reinforcement Learning (RL)
• Distributed RL over communication networks

Important Dates

Submission Deadline: 23 August 2024 6 September 2024
Acceptance Notification: 9 September 2024 20 September 2024
Camera Ready: 16 September 2024 27 September 2024
Workshop Day: 28 October 2024 28 October 2024

All times in Anywhere on Earth (AoE) timezone.

Paper Submission Guideline

Authors are invited to submit original unpublished papers not under review elsewhere. Papers should be submitted in IEEE 2-column format (Style templates can be found here). Maximum paper lengths, including title, abstract, all figures, tables, and references, are 7 pages for regular papers and 4 pages for short papers. Regular paper length could include up to 7 pages including references. Short papers are accepted as well and must not exceed 4 pages including references.

Papers have to be submitted electronically in PDF format through the EDAS conference management system, accessible via this link.

All submitted papers will be peer-reviewed and selected based on their originality, significance, technical soundness, and relevance to the workshop’s theme. For accepted papers, at least one author is expected to register and present the paper in person at the workshop. Accepted and presented papers will be published in the conference proceedings and submitted to IEEE Xplore.

Workshop Chairs

Pal Varga, Budapest University of Technology and Economics, Hungary
José Pedro Pereira dos Santos, Ghent University - imec, Belgium
Guillaume Fraysse, Orange, France
Nur Zincir-Heywood, Dalhousie University, Canada

Technical Program Committee (TPC)

Adel El-Atawy, Google, USA
Amogh Dhamdhere, CAIDA, USA
Andreas Johnsson, Ericsson Research, Sweden
Anup Kalia, IBM, USA
Ashiq Anjum, University of Leicester, UK
Ayse Bener, Ryerson University, Canada
Filippo Poltronieri, University of Ferrara, Italy
Francesca Fossati, Sorbonne Université, France
Giuliano Casale, Imperial College London, UK
Jaime Galàn-Jimènez, University of Extremadura, Spain
Jaime Llorca, New York University, NY, USA
Laurent Ciavaglia, Nokia, France
Marco Zambianco, FBK, Italy
Mauro Tortonesi, University of Ferrara, Italy
Marwa Elsayed, University of Western Ontario
Peter Orosz, Budapest University of Technology and Economics, Hungary
Philip Tee, University of Sussex and Moogsoft Inc, USA
Rita Orji, Dalhousie University, Canada
Ritu Chadha, Vencore Labs, USA
Roberto Rodrigues Filho, Federal University of Santa Catarina, Brazil
Sandrine Vaton, IMT Atlantique, France
Sidath Handurukande, CNUX Hub, Ireland
Steven Latré, University of Antwerp - iMinds, Belgium
Taghrid Samak, Google, USA
Tamas Tothfalusi, AITIA International, Hungary
Tim Wauters, University of Ghent, Belgium
Una-May O'Reilly, MIT, USA
Yu Deng, IBM, USA

For any questions, please feel free to contact Pal Varga (pvarga@tmit.bme.hu).