Identifying key factors and analyzing their causal relationships significantly enhance decision-making effectiveness in site selection. Although numerous studies have applied Multi-Criteria Decision-Making (MCDM) methods to site selection, these traditional approaches often overlook or inadequately represent causal interdependencies among factors. This study addresses these limitations by utilizing open data for transparency and employing Bayesian Networks (BN) as a robust probabilistic modeling alternative. BNs effectively represent complex factor interactions, capturing both causal relationships and uncertainties. Experimental evaluations demonstrate that the proposed framework effectively calculates final site suitability probabilities by explicitly considering hierarchical dependencies, offering enhanced decision-making insights.

Disentangling complex causal relationships is important for accurate detection of anomalies. In multivariate time series analysis, dynamic interactions among data variables over time complicate the interpretation of causal relationships. Traditional approaches assume statistical independence between variables in unsupervised settings, whereas recent methods capture feature correlations through graph representation learning. However, their representations fail to explicitly infer the causal relationships over different time periods. To solve the problem, we propose Causally Disentangled Representation Learning for Anomaly Detection (CDRL4AD) to detect anomalies and identify their causal relationships in multivariate time series. First, we design the causal process as model input, the temporal heterogeneous graph, and causal relationships. Second, our representation identifies causal relationships over different time periods and disentangles latent variables to infer the corresponding causal factors. Third, our experiments on real-world datasets demonstrate that CDRL4AD outperforms state-of-the-art methods in terms of accuracy and root cause analysis. Fourth, our model analysis validates hyperparameter sensitivity and the time complexity of CDRL4AD. Last, we conduct a case study to show how our approach assists human experts in diagnosing the root causes of anomalies.

Product attributes on e-commerce pages are often distributed across two modalities, textual content and embedded images. Such cross-modal presentation can introduce inconsistencies in wording, units, values, and attribute presence. Existing approaches match representations across modalities, but such alignment only indicates correspondence and cannot determine the relation type between paired attributes, such as equivalence and value conflict. We propose a framework that verifies attribute consistency at the attribute-pair level. First, it aligns cross-modal attributes by combining representation normalization with semantic embedding matching. Second, it classifies each aligned pair into four relation types. Third, it represents the inferred relations as typed triples in a relational graph for relational embedding learning. Finally, we evaluate the proposed framework on a real-world dataset in terms of relation classification, link prediction, and structural triple injection.