Abstract
We are now witnessing a dramatic growth of heterogeneous data, consisting of a complex set of cross-media content, such as texts, images, videos, audio, graphics, spatio-temporal data, and multivariate time series. The inception of modern techniques from computer science have offered very robust and hi-tech solutions for data and information analysis, collection, storage, and organization, as well as product and service delivery to customers. Recently, technological advancements, particularly in the form of big data, have resulted in the storage of enormous amounts of potentially valuable data in a wide variety of formats. This situation is creating new challenges for the development of effective algorithms and frameworks to meet the strong requirements of big data representation and analysis, knowledge understanding, and discovery.
Highlights
We are witnessing a dramatic growth of heterogeneous data, consisting of a complex set of cross-media content, such as texts, images, videos, audio, graphics, spatio-temporal data, and multivariate time series
Technological advancements, in the form of big data, have resulted in the storage of enormous amounts of potentially valuable data in a wide variety of formats. This situation is creating new challenges for the development of effective algorithms and frameworks to meet the strong requirements of big data representation and analysis, knowledge understanding, and discovery
This includes theories related to data acquisition, feature representation, time series analysis, knowledge understanding, data-based modeling, dimension reduction, semantic modeling, and the novel and promising big data analytic research direction, e.g., image/video captioning, affection computing, multimedia storytelling, internet commerce, healthcare, earth system, communications, and augmented/virtual reality
Summary
We are witnessing a dramatic growth of heterogeneous data, consisting of a complex set of cross-media content, such as texts, images, videos, audio, graphics, spatio-temporal data, and multivariate time series. The experimental results showed the efficiency of their proposed method in feature learning and outperformance with 4%–7% accuracy improvement compared with the traditional signal process methods and frequency trading patterns modeling approach with deep learning in stock trend prediction. Their evaluation experiments using three real-world datasets in the context of the smart city showed that their proposed dynamic ensemble strategy led to an improved error rate of up to 33% compared to the baseline strategy even when using 1/3 of the training data. In [A57], Tang et al introduced and formulated the problem of behavior pattern classification in blockchain networks and proposed a novel deep-learning-based method, termed PeerClassifier, to address the problem.
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