Journal Information
Information Systems (IS)
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Call For Papers
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, ACM PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining, information retrieval, internet and cloud data management, web semantics, visual and audio information systems, scientific computing, and organisational behaviour. 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.

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.

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 references 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. 
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Emerging Trends and Challenges in Edge-Fog-Cloud Interplay in the Internet of Things (IoT)
Submission Date: 2020-06-15

The Intelligent Internet of Things (IoT) tsunami and public embracement, and the ubiquitous adoption of devices in virtually every industry is affecting every aspect of life, ranging from smart cars, smart homes, smart cities, smart factories to smart health, and smart environments. The integration of IoT and Cloud Computing has created another paradigm, the cloud IoT, to address some of the major challenges of IoT, such as advanced analytics capabilities and big data storage. However, in the cloud IoT model, the massive amount of data coming from “smart things” needs to be uploaded to the cloud, demanding a considerable amount of available communication bandwidth. Cloud-based IoT model cannot meet the strict computing time requirement in latency-critical applications requiring a real-time operation. An excellent example of such a case is eHealth applications such as arrhythmia monitoring and classification in which volume, variety, and velocity, as well as end-to-end response time and communication bandwidth, should be handled efficiently. Edge or Fog Computing has emerged as a solution to address the drawbacks of Cloud-based IoT solutions in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. Hierarchical collaborative edge-fog-cloud architecture brings tremendous benefits as it makes possible to distribute the intelligence and computation —including data analysis, machine learning (ML) training, and decision making—to achieve an optimal solution while satisfying the given constraints (i.e., optimization for energy versus optimization for latency) of each use case. However, due to the hierarchical, cross-layer, and distributed nature of this IoT model, many challenges from smart things, to network, architecture, algorithms/software, and security still need to be addressed to develop consistent, suitable, scalable, safe, flexible and power-efficient systems. The main objective of this Special Issue (SI) is to address all important aspects of emerging technologies for edge-fog-cloud computing in IoT covering architectures, techniques, protocols, policies, applications, distributed machine learnings, as well as the interaction between edge, fog and cloud analytics. 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.)
Last updated by Dou Sun in 2020-03-24
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