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Conference Paper
Learning for Spatio-temporal and Relational Data
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- Authors
- Kihyuk Nam, Taewhi Lee, Sung-Soo Kim, Insik Shin
- Issue Date
- 2023-12
- Citation
- International Conference on Big Data (Big Data) 2023, pp.6239-6241
- Language
- English
- Type
- Conference Paper
- Abstract
- The vast amounts of spatio-temporal data generated by a variety of devices are best utilized in conjunction with relational, tabular data rather than being referenced separately. To enhance the efficiency of data analysis, learned models are frequently used to approximate query results by increasing responsiveness at the cost of some accuracy. Machine learning techniques exist for spatio-temporal data and tabular data separately, but it is not straightforward to represent the combined data in a unified learned model. This paper explores the challenge of learning from heterogeneous data, particularly trajectory and tabular data, and proposes approaches for representation learning using probabilistic circuits and deep neural networks.
- KSP Keywords
- Approximate query, Data analysis, Deep neural network(DNN), Heterogeneous Data, Machine Learning technique(MLT), Representation learning, Spatiotemporal data, Tabular Data, neural network(NN), relational data