32 research outputs found
Disentangled Causal Graph Learning forOnline Unsupervised Root Cause Analysis
The task of root cause analysis (RCA) is to identify the root causes of
system faults/failures by analyzing system monitoring data. Efficient RCA can
greatly accelerate system failure recovery and mitigate system damages or
financial losses. However, previous research has mostly focused on developing
offline RCA algorithms, which often require manually initiating the RCA
process, a significant amount of time and data to train a robust model, and
then being retrained from scratch for a new system fault.
In this paper, we propose CORAL, a novel online RCA framework that can
automatically trigger the RCA process and incrementally update the RCA model.
CORAL consists of Trigger Point Detection, Incremental Disentangled Causal
Graph Learning, and Network Propagation-based Root Cause Localization. The
Trigger Point Detection component aims to detect system state transitions
automatically and in near-real-time. To achieve this, we develop an online
trigger point detection approach based on multivariate singular spectrum
analysis and cumulative sum statistics. To efficiently update the RCA model, we
propose an incremental disentangled causal graph learning approach to decouple
the state-invariant and state-dependent information. After that, CORAL applies
a random walk with restarts to the updated causal graph to accurately identify
root causes. The online RCA process terminates when the causal graph and the
generated root cause list converge. Extensive experiments on three real-world
datasets with case studies demonstrate the effectiveness and superiority of the
proposed framework
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
Recently, recommender systems have been able to emit substantially improved
recommendations by leveraging user-provided reviews. Existing methods typically
merge all reviews of a given user or item into a long document, and then
process user and item documents in the same manner. In practice, however, these
two sets of reviews are notably different: users' reviews reflect a variety of
items that they have bought and are hence very heterogeneous in their topics,
while an item's reviews pertain only to that single item and are thus topically
homogeneous. In this work, we develop a novel neural network model that
properly accounts for this important difference by means of asymmetric
attentive modules. The user module learns to attend to only those signals that
are relevant with respect to the target item, whereas the item module learns to
extract the most salient contents with regard to properties of the item. Our
multi-hierarchical paradigm accounts for the fact that neither are all reviews
equally useful, nor are all sentences within each review equally pertinent.
Extensive experimental results on a variety of real datasets demonstrate the
effectiveness of our method
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Forecasting on sparse multivariate time series (MTS) aims to model the
predictors of future values of time series given their incomplete past, which
is important for many emerging applications. However, most existing methods
process MTS's individually, and do not leverage the dynamic distributions
underlying the MTS's, leading to sub-optimal results when the sparsity is high.
To address this challenge, we propose a novel generative model, which tracks
the transition of latent clusters, instead of isolated feature representations,
to achieve robust modeling. It is characterized by a newly designed dynamic
Gaussian mixture distribution, which captures the dynamics of clustering
structures, and is used for emitting timeseries. The generative model is
parameterized by neural networks. A structured inference network is also
designed for enabling inductive analysis. A gating mechanism is further
introduced to dynamically tune the Gaussian mixture distributions. Extensive
experimental results on a variety of real-life datasets demonstrate the
effectiveness of our method.Comment: This paper is accepted by AAAI 202