Summary of A Gap in Time: the Challenge Of Processing Heterogeneous Iot Data in Digitalized Buildings, by Xiachong Lin et al.
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
by Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue, Matthew Amos, Sam Behrens, Flora D. Salim
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of effectively utilizing Internet-of-Things (IoT) point data within deep-learning frameworks to enhance energy efficiency and operational performance in digitalized buildings. They investigate the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts, examining its implications for predictive modeling. The study also benchmarks state-of-the-art time series models on this complex dataset, highlighting their performance limitations. To overcome these challenges, the authors emphasize the need for multi-modal data integration, domain-informed modeling, and automated data engineering pipelines. Additionally, they advocate for collaborative efforts to establish high-quality public datasets essential for advancing intelligent and sustainable energy management systems in digitalized buildings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make better use of IoT data in smart buildings to reduce energy waste and improve efficiency. The researchers looked at the different types of data collected from sensors inside and outside buildings, and found that it’s hard to analyze this data because of its complexity. They tested various machine learning models on this data and found that they don’t work well together. To solve this problem, the authors suggest combining different types of data, using knowledge specific to the building, and creating automated tools to process the data. They also think it’s important for people to work together to create high-quality datasets that can be shared with others. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Multi modal » Time series
