6,176 research outputs found
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
Process improvement in BAe Systems and the wider aerospace sector
Purpose: To research the change management processes used to implement âworld classâ improvements in a major aerospace company, BAE SYSTEMS, and to propose a model for process improvement in the wider aerospace sector. Design/methodology/approach: The research was undertaken as a longitudinal study over a period of five years. A variety of research methodologies were used at various stages of the research including action research and observation. Semi-structured and unstructured interviews were used to gather qualitative data along with documentary evidence of the processes being used. Findings: There are three key findings. Firstly, an understanding of the production stages in the aerospace sector: future project; new product; sustain and return to work. Secondly details of a matrix-based approach and the issues regarding its implementation in a large organisation are discussed. Thirdly, a generic set of principles to aid process improvement in the aerospace sector is proposed. Research limitations/implications: Given that the study is based in one company, there are issues regarding the generalisation of the results. A potential further research project would entail the implementation of the proposed generic principles in another aerospace organisation. Practical implications: For BAE SYSTEMS, this research project aided their understanding of the issues involved in rolling out a process improvement program in a large organisation.Originality/value: Until recently, most of the research into process improvement had either been universalistic or aimed at another type of industry, such as the automotive industry. This research helps to address the specific needs of the aerospace industry
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
During time-critical situations such as natural disasters, rapid
classification of data posted on social networks by affected people is useful
for humanitarian organizations to gain situational awareness and to plan
response efforts. However, the scarcity of labeled data in the early hours of a
crisis hinders machine learning tasks thus delays crisis response. In this
work, we propose to use an inductive semi-supervised technique to utilize
unlabeled data, which is often abundant at the onset of a crisis event, along
with fewer labeled data. Specif- ically, we adopt a graph-based deep learning
framework to learn an inductive semi-supervised model. We use two real-world
crisis datasets from Twitter to evaluate the proposed approach. Our results
show significant improvements using unlabeled data as compared to only using
labeled data.Comment: 5 pages. arXiv admin note: substantial text overlap with
arXiv:1805.0515
- âŠ