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The ability to decentralize knowledge graphs (KG) is important to exploit the full potential of the Semantic Web and realize the Web 3.0 vision. However, decentralization also renders KGs more prone to attacks with adverse effects on data integrity and query verifiability. While existing studies focus on ensuring data integrity, how to ensure query verifiability - thus guarding against incorrect, incomplete, or outdated query results - remains unsolved. We propose VeriDKG, the first SPARQL query engine for decentralized knowledge graphs (DKG) that offers both data integrity and query verifiability guarantees. The core of VeriDKG is the RGB-Trie, a new blockchain-maintained authenticated data structure (ADS) facilitating correctness proofs for SPARQL query results. VeriDKG enables verifiability of subqueries by gathering global index information on subgraphs using the RGB-Trie, which is implemented as a new variant of the Merkle prefix tree with an RGB color model. To enable verifiability of the final query result, the RGB-Trie is integrated with a cryptographic accumulator to support verifiable aggregation operations. A rigorous analysis of query verifiability in VeriDKG is presented, along with evidence from an extensive experimental study demonstrating its state-of-the-art query performance on the largeRDFbench benchmark. © 2023, VLDB Endowment. All rights reserved.

Uncertainty is an essential consideration for time series forecasting tasks. In this work, we focus on quantifying the uncertainty of traffic forecasting from a unified perspective. We develop a novel traffic forecasting framework, namely Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty. Specifically, we first leverage a spatio-temporal model to model the complex spatio-temporal correlations of traffic data. Subsequently, two independent sub-neural networks maximizing the heterogeneous log-likelihood are developed to estimate aleatoric uncertainty. To estimate epistemic uncertainty, we combine the merits of variational inference and deep ensembling by integrating the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods, respectively. Furthermore, to relax the Gaussianity assumption, mitigate overfitting, and improve horizon-wise uncertainty quantification performance, we define a new calibration method called Multi-horizon Conformal Calibration (MHCC). Finally, we provide a theoretical analysis of the proposed unified approach based on the PAC-Bayes theory. Extensive experiments are conducted on four public datasets, and the empirical results suggest that the proposed method outperforms state-of-the-art methods in terms of both point prediction and uncertainty quantification. IEEE

E-governance is capable of driving the transition of government from an administrative power allocation of public resources to a citizen-service-oriented governance model. However, during this transformation, various governmental bodies often face the challenge of "data silos" caused by factors such as cross-regional, cross-business, and cross-departmental operations. Without altering the existing information platforms, transparent access technology serves as a key solution for data access within egovernance systems. It enables convenient access to information resources stored in different mediums and formats, facilitating the sharing and consolidation of information and data within and between governmental departments, thereby addressing the issue of "data silos" and enhancing the comprehensive service capabilities of e-governance. This paper firstly provides an overview of the concept, levels, characteristics, and application scenarios of transparent access to government big data. Secondly, it conducts a comprehensive comparative analysis of transparent access technologies in the context of cloud computing and big data. Lastly, based on the requirements of various transparent access technologies and the application of transparent access to government big data, this paper proposes a visionary framework for transparent access to government big data based on cross-domain semantics and channel coupling. This framework includes modules for cross-domain semantic interoperability, coupling of heterogeneous information channels based on knowledge graph, and tracing of multi-source heterogeneous data, aiming to provide innovative solutions for achieving transparent access to government big data.