Contextual crowd intelligence

Ooi, Beng Chin; Tan, Kian Lee; Trần, Quốc Trung; Yip, James; Chen, Gang; Ling, Zheng; Nguyen, Thi; Tung, Anthony K. H.; Zhang, Meihui

doi:10.1145/2674026.2674032

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…The process of data interpretation and knowledge extraction has the following challenges including addressing noisy real-world data and the ability to develop further inference techniques that do not have the limitations of traditional algorithms. Usually, It is very complex to formalize and model the contextual information related to human behaviors in a standard way due to the complex physiological, psychological and behavioral aspects of human beings [291].…”

Section: B Sbs and Context-aware Computingmentioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

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Section: B Sbs and Context-aware Computingmentioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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“…Past works cannot fit the decentralization features of mobile edge computing, and meet the differentiated demands of a crowd-intelligence ecosystem [1], [3], [17], [10], [8]. For example, workers' reliability must be inferred from workers' history records [1], [3], and at the same time much money is wasted for bad workers and no penalty for their bad affects as compensation is paid to the task publisher [17], [10]. The reward-penalty model gives a much more flexible way to manage the interests of three stakeholders.…”

Section: Related Workmentioning

confidence: 99%

“…To relieve bandwidth overburden and response delay, the nearest ES/MN is able to replace a RC to serve as an intermediary node and third party guarantee during a raw data transfer between a worker and publisher. As most of workers and publishers have limited hardware resources (e.g., mobile phones and sensors), they need only be a node of simplified payment verification (SPV node) 1 , and do not have to host a full replica of the blockchain like ES/MNs. In addition, as a ES/MN can be in close proximity to workers/publishers than the RC, it can help them preprocess the task data at edge network (e.g., videos clips or images).…”

Section: A Trustless Platform Enabled By Blockchain Smart Contract An...mentioning

confidence: 99%

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A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

Wang

Bhargava

et al. 2019

IEEE Trans. Ind. Inf.

104

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Crowd-intelligence tries to gather, process, infer and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things (IIoT). A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First we use a rewardpenalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers of the mobile edge computing network, which together function as a trustless hybrid humanmachine crowd-intelligence platform. As edge servers are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved edge servers and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method respectively.Index Terms-Mobile edge computing, blockchain smart contract, crowd-intelligence ecosystem, trustless, hybrid humanmachine, reward and penalty, strong Nash equilibrium.

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“…Therefore, Ooi et al [16] advise that machine learning alone cannot be used for this purpose. They propose a hybrid human-machine database engine where the machine interacts with the subject matter experts as part of a feedback loop to gather, infer, ascertain, and enhance the database knowledge and processing and discuss the challenges towards building such a system through examples in healthcare predictive analysis.…”

Section: Related Workmentioning

confidence: 99%

Improving Hospital Readmission Prediction Using Domain Knowledge Based Virtual Examples

Vukičević

Radovanović

Kovačević

et al. 2015

Lecture Notes in Business Information Processing

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Abstract. In recent years, prediction of 30-day hospital readmission risk received increased interest in the area of Healthcare Predictive Analytics because of high human and financial impact. However, lack of data, high class and feature imbalance, and sparsity of the data make this task so challenging that most of the efforts to produce accurate data-driven readmission predictive models failed. We address these problems by proposing a novel method for generation of virtual examples that exploits synergetic effect of data driven models and domain knowledge by integrating qualitative knowledge and available data as complementary information sources. Domain knowledge, presented in the form of ICD-9 hierarchy of diagnoses, is used to characterize rare or unseen co-morbidities, which presumably have similar outcome according to ICD-9 hierarchy. We evaluate the proposed method on 66,994 pediatric hospital discharge records from California, State Inpatient Databases (SID), Healthcare Cost and Utilization Project (HCUP) in the period from 2009 to 2011, and show improved prediction of 30-day hospital readmission accuracy compared to state-of-the-art alternative methods. We attribute the improvement obtained by the proposed method to the fact that rare diseases have high percentage of readmission, and models based entirely on data usually fail to detect this qualitative information.

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Contextual crowd intelligence

Cited by 14 publications

References 18 publications

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

Improving Hospital Readmission Prediction Using Domain Knowledge Based Virtual Examples

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