A Dynamic Selection Approach for Quality Control Mechanisms in Crowdsourcing

Alabduljabbar, Reham; Al-Dossari, Hmood

doi:10.1109/access.2019.2906506

Cited by 19 publications

(11 citation statements)

References 43 publications

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“…However, an ontology would need to be part of a framework that supports constant change while being used (Gil et al, 2017). Crowdsourcing has evolved as a new approach for leveraging human knowledge and intelligence to complete activities that are difficult for computers to complete efficiently (Alabduljabbar and Al-Dossari, 2019). Therefore, for the past few years, crowdsourcing has been used in the creation and development of ontologies (Gil et al, 2017;Khider et al, 2019;Waagmeester et al, 2020).…”

Section: Ontology Design Methodologiesmentioning

confidence: 99%

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

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Open science presents a new approach for knowledge discovery, dissemination, and integrity. The idea behind open science is to reinforce research activities and create open knowledge networks by exploring, organizing, and sharing scientific data, as well as making research results transparent, open and integrated. Big data derived from remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and wide variety of structured and unstructured sources requires significant efforts for data and knowledge management. Innovations and developments in information technology for the last couple of decades have made data and knowledge management possible for insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that lead to new ideas in scientific research and business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. In this article, a systematic literature review is conducted to examine research involving ontologies related to hydrological processes and water resources management. The hydrologic cycle (water cycle) is a multi-component and multi-process system that is shaped by various dynamic factors. Because all components of the hydrologic cycle interact with one another and water distribution on Earth is not uniform in time and space, modeling the hydrologic cycle and management of hydrologic cycle components are complex and difficult endeavors. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of necessary ontology-based information and decision support systems, the comprehension of environmental and atmospheric phenomena, the development of climate and water resiliency concepts, the creation of educational tools with artificial intelligence, and the strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide development of next generation artificial intelligence applications. The authors discuss future research prospects in combination with the artificial intelligence and hydroscience.

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Section: Ontology Design Methodologiesmentioning

confidence: 99%

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

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“…Hamrouni et al [6] proposed a photo-based MCS architecture that monitors the quality of the photos submitted by reporters to cover ongoing events. Alabduljabbar et al [7] studied a dynamic approach that exploits task-quality ontology to select the most suitable quality control mechanism for a given task based on its type. Ben Said et al [16] proposed an algorithm to determine the best public transport journey plan offering based on the quality of service of available WiFi mobile crowdsourced WiFi coverage along the journey.…”

Section: Related Workmentioning

confidence: 99%

“…The quality of the returned result, which is deduced from each worker's partial result, depends heavily on the characteristics of the recruited workers. To this end, most of the MCS research focused on hiring professional workers for each task such that they can complete the tasks' successfully and provide desired results [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

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Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

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“…Fabio et al evaluated 76 crowdsourcing projects found in 72 articles in [33], which helped researchers and crowdsourcers to understand the state-of-the-art of crowdsourcing and evaluated the quality management in crowdsourcing projects preliminary. In [34], Reham et al proposed a dynamic approach for selecting the best quality control mechanism for a task rather than selecting a special one for all types of tasks. Oleson et al present an inexpensive and scalable automated quality assurance process in [35], which relies on programmatic gold creation to provide targeted training feedback to workers and to prevent common scamming scenarios.…”

Section: Framework For Cap Problemsmentioning

confidence: 99%

CrowdOS: A Ubiquitous Operating System for Crowdsourcing and Mobile Crowd Sensing

Liu

Guo

et al. 2019

Preprint

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With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms (CAP) have appeared. In the mean time, CAP-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However, CAP urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types, thus requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including Task Resolution and Assignment Framework (TRAF ), Integrated Resource Management (IRM), and Task Result quality Optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further reveals TRO brings enormous improvement in efficiency and a reduction in energy consumption.

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A Dynamic Selection Approach for Quality Control Mechanisms in Crowdsourcing

Cited by 19 publications

References 43 publications

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

CrowdOS: A Ubiquitous Operating System for Crowdsourcing and Mobile Crowd Sensing

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