Categories |
INTERNET OF THINGS
BIG DATA
MACHINE LEARNING
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About |
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application. All papers should motivate the problems they address with compelling examples from real or potential applications. Systems papers must be serious about experimentation either on real systems or simulations based on traces from real systems. Papers from industrial organisations are welcome.Theoretical papers should have a clear motivation from applications. They should either break significant new ground or unify and extend existing algorithms. Such papers should clearly state which ideas have potentially wide applicability. Authors of select accepted Information Systems papers are invited by the EiCs to submit the experiment described in their papers for reproducibility validation. The resulting additional reproducibility paper is co-authored by the reproducibility reviewers and the authors of the original publication. In addition to publishing submitted articles, the Editors-in-Chief will invite retrospective articles that describe significant projects by the principal architects of those projects. Authors of such articles should write in the first person, tracing the social as well as technical history of their projects, describing the evolution of ideas, mistakes made, and reality tests. Technical results should be explained in a uniform notation with the emphasis on clarity and on ideas that may have applications outside of the environment of that research. Particularly complex details may be summarised with reference to previously published papers. We will make every effort to allow authors the right to republish papers appearing in Information Systems in their own books and monographs. |
Call for Papers |
Authors are invited to submit high-quality papers containing original work from either academia or industry reporting novel advances in (but not limited to) the following topics: • Distributed architectures and reference models. • Resource Management Mechanisms. • Service placement, migration and adaptation. • Low-latency High-reliability energy-efficient network protocols and communications in edge-fog-cloud. • The impact of 5G technology on edge-fog-cloud interplay. • Edge-fog-cloud management protocols and policies for workload communication and distribution. • Privacy and security issues including secure firmware, communications, and strategies to detect and mitigate attacks, as well as Over the air updates for safety IoT devices. • Trust-Oriented Designs of next-generation hierarchical IoT systems. • Optimization of the utility-privacy tradeoffs. • Big-data analytics, machine learning algorithms, and scalable/parallel/distributed algorithms. • Collaborative distributed machine learning and data analytics from Edge to Fog and Cloud. • Privacy-preserving Machine Learning and Data Processing solutions in hierarchical IoT solutions. • Privacy-Preserving Machine Learning (PPML) and Multi-party computation (MPC) techniques. • Performance monitoring & evaluation. • Real-world experiences and use cases (eHealth, automotive, transportation and logistics, retail, industry 4.0, etc.) |
Credits and Sources |
[1] Edge-Fog-Cloud -IoT 2020 : Special issue (Q1 journal: Elsevier Information System): Emerging Trends and Challenges in Edge-Fog-Cloud Interplay in the Internet of Things (IoT) |