Multi-source heterogeneous data fusion

Lili Zhang · Yuxiang Xie · Xin Zhang

126 citations0/0 files storedInternational Conference on Artificial Intelligence and Big Data (ICAIBD)

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Data Quality and Management Advanced Graph Neural Networks Big Data Technologies and Applications

Abstract

As the exponential growth of data in internet era, there comes the big data era. Big data fusion creates huge values that makes it a research hotspot. However, in big data era, data shows characters of large volume, velocity, veracity and especially variety which is also called heterogeneity. Multiple different sources of data lead to data heterogeneity. Multi-source heterogeneous data brings opportunities and challenges to big data fusion. This paper introduces big data fusion and methods for heterogeneous data fusion, especially focus on the application of deep learning methods in multi-source heterogeneous data fusion. Challenges of dealing with multi-source heterogeneous data fusion is also discussed.

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References

2014Data-intensive applications, challenges, techniques and technologies: A survey on Big Data 2013Data mining with big data 2017A survey on deep learning for big data 2015Social big data: Recent achievements and new challenges 2015Methodologies for Cross-Domain Data Fusion: An Overview 2014Data quality: The other face of Big Data 2017Heterogeneous Data and Big Data Analytics 2017A Survey on Deep Learning in Big Data

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