Incremental Checkpointing for Fault-Tolerant Stream Processing Systems: A Data Structure Approach

Lin, Chia-Yu; Wang, Li-Chun; Chang, Shu Ping

doi:10.1109/tetc.2020.2986487

Cited by 3 publications

(1 citation statement)

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“…References [12], [13], [21], and [22] only considered how to build LTM with a large amount of data. However, in current applications, new data frequently enter the system [23]. Traditional LTM was built in the batch mode.…”

Section: Updating Latent Tree Modelmentioning

confidence: 99%

Modeling of Multilayer Multicontent Latent Tree and Its Applications

Lin

Chiu

Wang

et al. 2021

IEEE Trans. Comput. Soc. Syst.

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Latent tree model (LTM) is a probabilistic tree-structured graphical model, which can reveal the hidden hierarchical causal relations among data contents and play a key role in explainable artificial intelligence. However, because current LTM modeling techniques are only suitable for singlecontent variable, the applications of LTMs are somewhat limited. Toward this end, a multilayer LTM (ML-LTM) is first presented to deal with the hierarchical clustering issues of multicontent variables. Second, we further develop an ML-LTMbased multicontent recommendation system. Our experiment results show that the proposed ML-LTM can achieve 90% recommendation accuracy, but the current LTM can only has 20%. Third, we propose an incremental update approach for ML-LTM that can save five-sixth updating time comparing with the whole-model retraining approach for achieving the same recommendation accuracy.

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