2,630 research outputs found
Predictive task assignment in spatial crowdsourcing: A data-driven approach
With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework
Optimal Order Assignment with Minimum Wage Consideration (OOAMWC)
While the application of crowdsourcing has increased over the years, the technology experiences various issues during implementation. Examples of some of the issues that affect crowdsourcing include task assignment, profit maximizations, as well as time window issues. In some instances addressing some of the issues results in the other issues being overlooked. An example is when assigning tasks to workers, the profits of the workers might not be considered and this ends up affecting the profit maximization aspect. Various algorithms have been proposed to address the task assignment, profit maximizations, and time window issues. However, these algorithms address the issues individually and this results in the occurrence of the other noted issues. Therefore, this calls for the definition of a solution to address the task assignment issue while taking into consideration the time window issue and the minimum wage constraint. Additionally, the solution should address the profit maximization of not only the workers but also the platform and the clients of the platform. To evaluate the efficiency of the proposed solution, a comparison with the different implemented solutions to address individual issues is recommended. Comparing such solutions can provide insight into the efficiency of the proposed approach when addressing multiple issues affecting crowdsourcing
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
The increasing maturity of big data applications has led to a proliferation
of models targeting the same objectives within the same scenarios and datasets.
However, selecting the most suitable model that considers model's features
while taking specific requirements and constraints into account still poses a
significant challenge. Existing methods have focused on worker-task assignments
based on crowdsourcing, they neglect the scenario-dataset-model assignment
problem. To address this challenge, a new problem named the Scenario-based
Optimal Model Assignment (SOMA) problem is introduced and a novel framework
entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a
heterogeneous information framework that can integrate various types of
information to intelligently select a suitable dataset and allocate the optimal
model for a specific scenario. To comprehensively evaluate models, a new score
function that utilizes multi-head attention mechanisms is proposed. Moreover, a
novel memory mechanism named the mnemonic center is developed to store the
matched heterogeneous information and prevent duplicate matching. Six popular
traffic scenarios are selected as study cases and extensive experiments are
conducted on a dataset to verify the effectiveness and efficiency of SMAP and
the score function
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
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