Introduction to the Special Issue on Data Science for Next Generation Big Data

The first age of Big Data started roughly ten years ago. It has had an enormous impact in many fields of science. It underlies the rapid development of data-driven applications and gives rise to many innovative data processing systems. Ten years on, Big Data is entering a new generation. In particular, data is being used at a much larger, global scale. Furthermore, there is a trend of multiple data owners coming together to perform collaborative data analytics, and many datadriven business decisions are made based on statistical analytics from multi-source, multimodal, and worldwide data. The new generation of Big Data opens the door for innovative data-driven applications that are not possible even in the early age of Big Data. However, the new scale, both in terms of the data and the number of participants, brings significant challenges ranging from secure data sharing to federated data analytics. At the same time, emerging technologies such as 5G, AI, and blockchains demand high-performance, scalable, and secure data management. It is therefore crucial to have new theories, algorithms, and systems, for future applications that make various trade-offs between security, performance, and data quality, in this new age of Big Data. This special issue aims to publish work on a variety of data science technologies including statistical theory, data management, data mining, and machine learning, which realize the potentials of next-generation Big Data. This special issue received five high-quality submissions, and three of them were accepted. The topics of the accepted articles are briefly introduced below. The article titled “Hierarchical Satellite System Graph for Approximate Nearest Neighbor Search on Big Data” presents a hierarchical method to build the Monotonic Search Networks to solve the approximate nearest neighbor search problem. The proposed index and search algorithms can be deployed in a distributed manner, effectively decreasing the search steps and reducing the computational cost over large-scale data. The article titled “Quantized Tensor Neural Network” introduces a quantized tensor neural network to improve the learning ability of the tensor network. The proposed method effectively integrates the high-order convolution operations into the non-linear tensor network to learn local features of high-dimensional data. A high-order error backpropagation algorithm is further developed to optimize the parameters in tensor networks. The proposed method can learn hidden features efficiently with fewer parameters on image classification tasks. The article titled “Differentially Private Deep Learning with Iterative Gradient Descent Optimization” studies the problem of privacy preservation with deep learning. It proposes a novel perturbed iterative gradient descent optimization algorithm which satisfies the differential privacy and achieves better model accuracy. A modified moments accountant method is introduced to get the tighter bound of privacy loss compared with the existing privacy accounting methods.