OJBD 2015 - Open Journal of Big Data (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.