FICS Scholarly Articles

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https://repository.upou.edu.ph/handle/20.500.13073/8
Faculty and staff research papers from the Faculty of Information and Communication Studies.

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Browsing FICS Scholarly Articles by Author "Borromeo, Ria Mae"
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    On Benchmarking for Crowdsourcing and Future of Work Platforms
    (Institute of Electrical and Electronics Engineers, 2019) Borromeo, Ria Mae ; Chen, Lei ; Dubey, Abhishek ; Roy, Sudeepa ; Thirumuruganathan, Saravanan
    Online crowdsourcing platforms have proliferated over the last few years and cover a number of important domains, these platforms include from worker-task platforms such Amazon Mechanical Turk, worker-forhire platforms such as TaskRabbit to specialized platforms with specific tasks such as ridesharing like Uber, Lyft, Ola etc. An increasing proportion of human workforce will be employed by these platforms in the near future. The crowd sourcing community has done yeoman’s work in designing effective algorithms for various key components, such as incentive design, task assignment and quality control. Given the increasing importance of these crowdsourcing platforms, it is now time to design mechanisms so that it is easier to evaluate the effectiveness of these platforms. Specifically, we advocate developing benchmarks for crowdsourcing research. Benchmarks often identify important issues for the community to focus and improve upon. This has played a key role in the development of research domains as diverse as databases and deep learning. We believe that developing appropriate benchmarks for crowdsourcing will ignite further innovations. However, crowdsourcing – and future of work, in general – is a very diverse field that makes developing benchmarks much more challenging. Substantial effort is needed that spans across developing benchmarks for datasets, metrics, algorithms, platforms and so on. In this article, we initiate some discussion into this important problem and issue a call-to-arms for the community to work on this important initiative.
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    User group analytics: hypothesis generation and exploratory analysis of user data
    (The International Journal on Very Large Data Bases (VLDB), 2018-10-26) Tehrani, Behrooz Omidvar ; Yahia, Sihem Amer ; Borromeo, Ria Mae
    User data is becoming increasingly available in multiple domains ranging from the social Web to retail store receipts. User data is described by user demographics (e.g. age, gender, occupation) and user actions (e.g. rating a movie, publishing a paper, following a medical treatment). The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large scale data analysis. User data analysis usually relies on identifying group-level behaviour such as “Asian women who publish regularly in databases.” Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this paper, we introduce a framework for user group analytics by developing several components which cover the life cycle of user groups. We provide two different analytical environments to support ‘hypothesis generation” and “exploratory analysis” on user groups. Experiments on data sets with different characteristics show the usability and efficiency of our group analytics framework.