Aims and Scope

The past few years have seen Data Science and Big Data moving forward dramatically. While Big Data may not technically be a technology it is going to be a massive disruptor. The entire business world has been transformed and will continue to be so in the upcoming years. Humanized big data is just a part of that. At the heart of things, humanizing big data seems like something that would be the opposite of productive and certainly counter intuitive. The reality is that big data must start and end with humanity. Because people are the source of big data, they must also be heavily involved in the processing and the interpretation. To get value from Big Data, you must add contextual information and place analytical capability in the hands of those who need it. In other words, Big Data needs to be “humanized”: taken from the world of bits and bytes and converted into real insight for real businesspeople. This journal aims to address humanized big data, gather researches on advance methodologies, strategies, frameworks, architectures and algorithms that can effectively be used for humanizing big data.

Topics Covered

The topics covered by Journal of Data Management and Analysis include the following but not limited to:
Information Empowerment, Knowledge Sharing, Service Enrichment, Big Data and Human Brain, Complex Systems Modeling, Digital Ecosystems, Digital Epidemiology, Global Brain, Data Integration and Data Provenance, Location-Based Data Analytics, Multimedia and Mobile Data Processing, NoSQL Data Stores and DB Scalability, Mechanism Design, Multi-Agent Systems, Remembrance Agents, Ubiquitous Computing, Algorithms and Computational Complexity, Big Data Applications, Big Data Infrastructure, Security, Privacy, Trust, and Legal Issues to Big Data, Semi-Structured and Unstructured Data Analytics, Sentiment and Opinion Mining, Service-Oriented Computing, Spatial, Temporal, and Graph Data Mining, Human-Centric Computing, Graph Clustering, Information-Theoretic Analysis, Data Science, Data-driven Science, Data Storage, Data Artisan, Social Network Analysis, Spectral Clustering, Contextual Data, Alternating Minimization, Big Data Theory, Predictive Analysis, Cross-border Network-based Information Systems, Emerging Networking Trends, Frameworks for Wireless Security, Organizational  Impact of E-commerce Connectivity, Outsourcing of Networking and Data Communication Services, Usability of Business Data Communication Networks, Cloud Computing Data Centers, Computing Resources, Storage Resources, Computational Resources, Network Automation, Network Virtualization Technology, Data Center LAN, Data Center WAN.


Volume 1, No. 1, 2020


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Editorial Boards

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