Big data technology is designed to handle the challenges of the three Vs of big data including volume (massive amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Big data is often captured without a specific purpose leading to most of it being task-irrelevant data. The most important feature of data is neither volume nor the other Vs but value. If big data is the technological foundation for data driven business decision-making, Smart Data is an organized way in which different data sources are semantically brought together, correlated, and analyzed, etc., to be accurate, actionable, and agile to feed smarter decision-making. Smart Data supports harnessing and overcoming the three V-challenges by utilizing Semantics and Neuroscience so that all values can be taken from the data. Dealing with volume, Semantics technology supports converting massive amounts of data into abstraction, meaning, and insight useful for human decision-making. Neural network algorithms are able to learn from whole data instead of samples of the data. Neural network algorithms, in particular, can take advantage of massively parallel (brain-like) computations, which use very simple processors that other machine learning technologies cannot use. For variety, the well-defined form of ontology, natural language processing, facilitates integration. For dealing with velocity, the ontology evolution techniques support dynamically, flexibly, and adaptively creating models of new objects, concepts, and relationships and using them to better understand new cues in the data that capture rapidly evolving events and situations. In addition, the Semantics and Neuroscience are applied for intelligent analytics to find insight that is actionable. Smart Data bridges a gap by facilitating information extraction and insight discovery. Smart Data can certainly help to make smarter decisions.

Join us in this special issue to share how to combine Semantics and Neuroscience to deploy Smart Data solutions and to share challenges in how to extract value from big data and explain how Semantics and Neuroscience standards complement foundational big data technologies like Hadoop to tackle these challenges.

Potential topics include, but are not limited to:

• Neural network learning algorithms for high-velocity streaming data
• Applied Semantics and Neuroscience for intelligent analytics
• Deep neural network learning
• Applied ontology evolution for velocity solutions
• Natural language processing for knowledge graph building
• Applied Semantics for large-scale feature learning
• Applied Semantics for high dimensional data learning
• Applied Semantics for massive data learning
• Smart Data analytics using neural networks and Semantics in healthcare/medical applications
• Smart Data analytics using neural networks in electric power and energy systems
• Smart Data analytics using neural networks in large sensor networks
• Smart Data and neural network learning in computational biology and bioinformatics