Methodologies for Cross-Domain Data Fusion: An Overview

By Yu Zheng. 2015

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Philip S. Yu
3. Yu Zheng
4. Yoshua Bengio
5. Jianfeng Gao
6. Jiawei Han
7. Kyunghyun Cho
8. Dacheng Tao

In Management Science and Operations Research

1. Qiang Yang
2. Hao Wang
3. Yan Yang
4. Zhong Ming
5. Weike Pan
6. Yuchao Duan
7. Ben Tan
8. Junbo Zhang

In Signal Processing

1. Yu Zheng
2. Qiang Yang
3. Yan Yang
4. Hao Wang
5. Junbo Zhang
6. Yoshua Bengio
7. Weike Pan
8. Zhong Ming

Citations in the corpus

• A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “The literatures [15-18] have provided deep discussion and introduction to these methods.”
• A survey of data fusion in smart city applications (2019, background): “Zheng [21] Discusses about differences on fusing sources and varying techniques for cross domain data fusion.”
• Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2016, background): “Predicting crowd flows in a city is of great importance to traffic management and public safety (Zheng et al. 2014).”
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “A survey on data fusion methodologies can be found at [46].”
• Multi-view clustering: A survey (2018, positive): “Moreover, Zheng[4] provided an overview on the methodologies for multi-view (cross-domain) data fusion, in which some specific applications were discussed.”
• Transfer Learning for Behavior Ranking (2017, methodology): “…algorithm to more types of user behavior and information such as the textual content [Mikolov et al. 2013] of users’ searched queries and visual content [Zhao et al. 2016] in the items’ advertisements and promotions and to more cross-domain and transfer learning paradigms [Zheng 2015].”
• p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, background): “RELATED WORK Data-driven Air Pollution Analysis: In recent years, air pollution analysis has drawn a lot of attention from the data mining community [10][11].”
• Tackling Climate Change with Machine Learning (2019, background): “a management [341]. Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, which includes data matching [342, 343], data fusion [344], and ensemble learning [345]. Low-emissions infrastructure When smart city projects are proper”
• Machine Learning With Big Data: Challenges and Approaches (2017, background): “long time, but with the emergence of Big Data, they have attracted renewed attention [48].”
• Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, neutral): “[Zheng 2015b] on algorithms and applications of mining trajectory data, especially the vehicle trajectory data.”
• Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design (2019, background): “directly and indirectly related contents for comprehensive data fusion [7], [14].”
• Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning (2017, background): “With such substantial urbanization, urban activity modeling is widely recognized as a fundamental task [43, 42] for tackling urban challenges (e.”
• Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics (2017, background): “How to unlock the power of knowledge from disparate datasets is of paramount importance in big data research, essentially distinguishing big data from traditional data mining tasks [45].”