[AISWorld] Call for papers for 2 journals (IJOCI and OJBD)

[Chang V.I.]

Dear All,

Apologies if you've received it from somewhere. Our 2 journals have our calls for papers and special issues.

International Journal of Organizational and Collective Intelligence (IJOCI) with 2 special issues:
http://www.igi-global.com/calls-for-papers-special/international-journal-organizational-collective-intelligence/1140

Open Journal of Big Data (OJBD) with 5 special issues:
http://www.ronpub.com/index.php/journals/ojbd

OJBD has the fee waiver for the accepted papers submitted by the 15th of February 2015. Those who submit to IJOCI, will be based on the first-come-first serve basis.
Details about the scope, topics, our motivation and impacts of our research contributions are available at the end of this email. We blend our journals with other scholarly activities.

I have included the scope and details of these 2 journals at the end of this email. I hope you can consider.

Many thanks again for your kind assistance and interests. Thanks and regards.

Dr. Victor Chang,
Editor-in-Chief, OJBD and IJOCI

----
International Journal of Organizational and Collective Intelligence (IJOCI): http://www.igi-global.com/journal/international-journal-organizational-collective-intelligence/1140

Mission
The mission of the International Journal of Organizational and Collective Intelligence (IJOCI) is to provide researchers and practitioners in the communities of computer and information sciences with a forum to advance the practice and understanding of computing theories and empirical analyses for realizing “organizational intelligence and collective intelligence”, i.e., intelligent computing for organizational and collective information from not only technical but also institutional and social aspects.

Topics Covered
Agents, HCI and business intelligence systems
Artificial intelligence for organizational management
Big Data (system design, implementations, applications, proof-of-concepts and use cases)
Case studies
Classification and clustering
Cloud Computing (IaaS, PaaS, SaaS, experiments, simulations proof-of-concepts and consulting)
Collaboration and communication systems
Corporate management systems
Data mining and knowledge bases for organizational management
Decision making theory and modeling
Decision science
Decision support systems and crisis management systems
Expert systems
Game theoretic and information economic analysis
Genetic algorithms and evolutionary computing
Global enterprise systems
Information content security
Information systems and sustainability (human, social and management aspects)
Intellectual property management
Intelligent Agents and Multi-Agent Systems
Intelligent Web-based systems
Knowledge Discovery
Knowledge engineering, e-Learning and computing for education
Machine and computer vision
Machine Learning
Metadata and multimedia information systems
Monitoring and planning
Neural networks, bayesian networks,and fuzzy techniques and systems
Optimization
Organizational systems, middleware, applications, and experiences
Risk modeling and financial computing
Robotics for intelligent organizations
Security and access control
Self-organizing and complex systems
Semantic Web architecture and applications
Service computing
Signal and time series processing
Soft computing in organizations
Software engineering and formal methods

---

Open Journal of Big Data (OJBD): http://www.ronpub.com/index.php/journals/ojbd

Big Data research is expected to be the hottest topic for the next five years. We shall have solid plans and regular meetings to ensure that our journal attracts the best papers from reputable researchers to support our mission continuously. Our objectives are as follows:

Disseminate the emerging techniques, technologies and services associated with Big Data.
Offer empirical evidence and approaches to demonstrate contributions made by Big Data.
Offer recommendations to research and enterprise communities that use Big Data as a solution for their work.
Offer guidelines and strategic directions in the way that Big Data research should progress.
We will seek recommendations and practices that can be successfully delivered to other disciplines such as healthcare, finance, education and science, providing us quality papers centered on Big Data and whose lessons learned will be transferable across disciplines to encourage inter-disciplinary research and funding activities essential for progressive research and development. We will cover extensive studies to ensure that the research and enterprise communities can take our recommendations, guidelines and best practices, which will make real positive impacts to their services and projects. We will ensure that key lessons taken from our journal can be very useful to communities. By blending workshops and calls for papers in our journal, we will ensure that our articles are of the highest caliber and can demonstrate added values and benefits to the people adopting our recommendations. We will ensure all submitters understand and use our recommendations, so that their citations and adoptions of our key lessons will keep our quality high.

Our journal has an advantage over the competing journals in Big Data as follows. First, steps involved in Big Data development should be reproducible to allow organizations to follow. Some articles in competing journals are very theoretical, making reproduction difficult. Second, all demonstrated deliveries in our journal should be easy to use, and provide real added value to technology-adopting organizations beyond just technical implementations. Unlike some articles in competing journals, whose deliveries are hard to understand and don’t consider technical or organizational adoption. We also encourage industrial partners to provide their latest developments, success stories (empirical) and best practices (quantitative and qualitative) to ensure our journal articles have the edge over others.

The Open Journal of Big Data (OJBD) welcomes high-quality and scholarly papers, which include new methodologies, processes, case studies, proofs-of-concept, scientific demonstrations, industrial applications and adoption. The journal covers a wide range of topics including Big Data science, frameworks, analytics, visualizations, recommendations and data-intensive research. The OJBD presents the current challenges faced by Big Data adoption and implementation, and recommends ways, techniques, services and technologies that can resolve existing challenges and improve on the current practices. We focus on how Big Data can make huge positive impacts to different disciplines in addition to IT, which include healthcare, finance, education, physical science, biological science, earth science, business & management, information systems, social sciences and law. There are eight major topics as follows:

- Techniques, algorithms and innovative methods of processing Big Data (or Big datasets) that achieve performance, accuracy and low-costs.
- Design, implementation, evaluation and services related to Big Data, including the development process, use cases, experiments and associated simulations.
- Systems and applications developed by Big Data and descriptions of how Big Data can be used in disciplines such as bioinformatics, finance, education, natural science, weather science, life science, physics, astronomy, law and social science.
- Security, privacy, trust, data ownership, legal challenges, business models, information systems, social implications, social network analyses and social science related to Big Data.
- Consolidation of existing technologies (databases, web, mobile, HPC) and how to integrate them in Big Data such as SOA Big Data, data mining, machine learning, HPC Big Data and cloud storage.
- Recommendations, emerging technologies and techniques associated with Big Data such as mobile Big Data, standards, multi-clouds and internet of things.
- Data analysis, analytics and visualization, including GPU techniques, new algorithms and methods showing how to achieve significant improvements from existing methods.
- Surveys, case studies, frameworks and user evaluations involved with qualitative, quantitative and/or computational research methods.