The 1st International Workshop on Mobile Applications and Data Management (MADM 2024)

Workshop Aim and Scope

As mobile technology advances rapidly, the significance of mobile applications becomes more pronounced. The development of mobile OSes like OpenHarmony has opened up new horizons for mobile application development and management, bringing fresh possibilities to both academia and industry. The changes in programming languages (e.g., ArkTS) require the runtime system to provide more dynamic features and support for parallel capabilities. In addition, the enhanced distributed capabilities of operating systems provide convenience for the development of distributed applications, while scenarios such as device-cloud collaboration, multi-device collaboration, and cross-device migration also present new opportunities and challenges for distributed data management. At the same time, new application scenarios represented by large language models also pose new requirements for the mobile domain.

The first International APWeb-WAIM Workshop on Mobile Applications and Data Management (MADM2024) will take place on Aug 30th-Sep 1st, 2024, Jinhua, China, in conjunction with APWeb-WAIM, an annual international conference on Web and Big Data. This workshop aims to focus on innovative research on mobile application development, application and web framework, language runtime, data management, security, system supports, and so on. Participants will have the opportunity to engage with industry experts, researchers, and practitioners to explore new perspectives on mobile systems.

Submission Guidelines

The topics of interest for MADM include, but are not limited to:

• Mobile application frameworks:
  

  Declarative application development
  Distributed execution support
  Decomposable application architecture
  Performance optimization and tuning
  Intent framework
  Support for Emerging applications

• High-level language runtimes on mobile systems
  

  Optimizations for dynamic languages
  Automatic memory management
  Parallel programming support and optimizations

• Data management on mobile devices
  

  Data synchronization and conflict resolution
  Data storage on mobile devices
  Data compression
  Mobile data analytics

• Large language models (LLMs) for mobile applications and data management
  

  On-device inference acceleration
  Model compression and optimization
  Federated learning and model collaboration

• Web and graphics framework for mobile OSes and beyond
  

  Web engine, framework and applications
  One-stop development framework (e.g., ArkUI-X, Flutter)
  Graphics frameworks (e.g., Skia, OpenGL, Vulkan)

• Security and privacy
  

  System security (OS, Trusted execution environment, hypervisor, etc.)
  Hardware security (IoT, Autonomous Vehicles, etc.)
  Application security
  Cyber security
  Isolation and access control
  Formal methods

• System support for mobile application
  

  Operating system supports for mobile applications
  (e.g., File system for mobile, network stack, IPC, other OS services, etc.)
  New hardware extensions for mobile application

Paper Submission Guidelines:

All papers should be submitted at
https://easychair.org/conferences/?conf=madm2024

Papers should not exceed 12 pages single spacing in (LNCS (Lecture Notes in Computer Science) format). The submission of papers must be in either PDF or Word format. The usage of non-English fonts is not allowed. All papers accepted by MADM2024 will be published in a combined volume of Lecture Notes in Computer Science series published by Springer.

You may download formats by the following links:

LaTex2e Format:DOWNLOAD
MS Word Format:DOWNLOAD

All accepted papers MUST strictly follow the LNCS/LNAI Authors instructions.

Important Dates

Organizers

• Workshop Co-Chairs
  
  • Dong Du, Shanghai Jiao Tong University, China
  • Mingyu Wu, Shanghai Jiao Tong University, China
• Program Committee Members
  
  • Xiaocheng Hu, Huawei
  • Changlong Li, East China Normal University
  • Li Li, Beihang University
  • Xin Liu, Lanzhou University
  • Chang Lou, University of Virginia
  • Bo Wang, Beijing Jiaotong University
  • Chenxi Wang, ICT CAS
  • Chao Wu, Nanjing University of Science and Technology
  • Haoyang Zhuang, Shanghai Jiao Tong University