A Survey on Truth Discovery <s> Streaming data <s> This paper presents a streaming approach to solve the truth estimation problem in crowdsourcing applications. We consider a category of crowdsourcing applications where a group of individuals volunteer (or are recruited to) share certain observations or measurements about the physical world. Examples include reporting locations of gas stations that remain operational after a natural disaster or reporting locations of potholes on city streets. We call such applications social sensing. Ascertaining the correctness of reported observations is a key challenge in such applications, referred to as the truth estimation problem. This problem is made difficult by the fact that the reliability of individual sources is usually unknown a priori, since any concerned citizen may, in principle, participate. Moreover, the timescales of crowdsourcing campaigns of interest can be as small as a few hours or days, which does not offer enough history for a reputation system to converge. Instead, recent prior work, including our own, developed fact-finding algorithms to solve this problem by iteratively assessing the credibility of sources and their claims in the absence of reputation scores. Such algorithms, however, operate on the entire dataset of reported observations in a batch fashion, which makes them less suited to applications where new observations arrive continually. In this paper, we describe a streaming fact-finder that recursively updates previous estimates based on new data. The recursive algorithm solves an expectation maximization (EM) problem to determine the odds of correctness of different observations. We compare the performance of our recursive EM algorithm to a batch EM algorithm, as well as to several state-of-art fact-finders through extensive simulations. We also demonstrate convergence of the recursive algorithm to the results of the batch version through a real social sensing experiment. Our evaluation shows that the proposed approach can process data streams much more efficiently while keeping the truth estimation accuracy close to that of the (much slower) batch algorithm. Ours is therefore the first fact-finder developed with explicit consideration to the continuous update needs of crowd-sourcing applications. <s> BIB001 </s> A Survey on Truth Discovery <s> Streaming data <s> Truth discovery is a long-standing problem for assessing the validity of information from various data sources that may provide different and conflicting information. With the increasing prominence of data streams arising in a wide range of applications such as weather forecast and stock price prediction, effective techniques for truth discovery in data streams are demanded. However, existing work mainly focuses on truth discovery in the context of static databases, which is not applicable in applications involving streaming data. This motivates us to develop new techniques to tackle the problem of truth discovery in data streams. In this paper, we propose a probabilistic model that transforms the problem of truth discovery over data streams into a probabilistic inference problem. We first design a streaming algorithm that infers the truth as well as source quality in real time. Then, we develop a one-pass algorithm, in which the inference of source quality is proved to be convergent and the accuracy is further improved. We conducted extensive experiments on real datasets which verify both the efficiency and accuracy of our methods for truth discovery in data streams. <s> BIB002 </s> A Survey on Truth Discovery <s> Streaming data <s> In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods. <s> BIB003
In many real-world applications, data continue to arrive over time. Most of the existing truth discovery methods are batch algorithms and work on static data. These methods are inefficient to process streaming data as they need to re-run the batch algorithms when new data are available. To tackle this challenge, some truth discovery algorithms BIB003 BIB001 BIB002 have been designed for different types of streaming data.