Recently, the widespread use of smart devices for Internet of Things has led to a massive growth in data and information on the World Wide Web. However, multiple sources from the Web often provide conflicting descriptions for the same objects, thereby complicating the task of truth discovery, especially when sources may copy information from others. Existing approaches typically neglect the dependence and accuracy of these sources, resulting in low accuracy and efficiency in truth discovery. To solve the problem, we proposeDepenBaye, a source-dependent truth discovery framework, which incorporates Bayesian probability, the Simulated Annealing method, and the expectation maximization (EM) method. By evaluating the copy probability of sources based on false claims, reliable sources are identified with a simulated annealing method to improve efficiency. According to the EM method, source reliability and claim confidence are iteratively calculated to discover the latent truth.DepenBaye’s performance has been validated through extensive experiments, which outperforms existing approaches in efficiency and effectiveness.

In the era of Big Data and generative artificial intelligence (AI), discovering the truth about various objects from different sources has become a pressing topic. Existing studies primarily focus on dependent sources with conflicting information, where sources may copy information from each other. However, real-world scenarios are often more complex, with dynamic dependence relationships among sources over time. This complexity makes it much more difficult to discover the truth. One of the key challenges centers on measuring the dynamic dependence among sources. To address this challenge, we have developed three models:$Depen\_{S}imple$,$Depen\_{C}omplex$, and$Depen\_{D}ynamic$. These models are based on the Hidden Markov Model (HMM) and are designed to handle different types of dependencies, namelysimple source dependence,complex source dependence, anddynamic source dependence. Based on the constructed models, we propose a generic framework for discovering the latent truth which are evaluated by three HMM-based methods. We conduct extensive experiments on three real-world datasets to evaluate the performance of the proposed methods, and the results demonstrate that all three methods achieve high accuracy over the state-of-the-art methods.

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to reduce electricity costs while satisfying the deadline constraints of workflow applications is important in GDCs, which is determined by the execution time of servers, power, and electricity price. Determining the completion time of workflows with different server frequencies can be challenging, especially in scenarios with heterogeneous computing resources in GDCs. Moreover, the electricity price is also different in geographical locations and may change dynamically. To address these challenges, we develop a geo-distributed system architecture and propose an Electricity Cost aware Multiple Workflows Scheduling algorithm (ECMWS) for servers of GDCs with fixed frequency and power. ECMWS comprises four stages, namely workflow sequencing, deadline partitioning, task sequencing, and resource allocation where two graph embedding models and a policy network are constructed to solve the Markov Decision Process (MDP). After statistically calibrating parameters and algorithm components over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art methods over two types of workflow instances. The experimental results demonstrate that our proposed algorithm significantly outperforms other algorithms, achieving an improvement of over 15% while maintaining an acceptable computational time. The source codes are available athttps://gitee.com/public-artifacts/ecmws-experiments.