Online Prediction of User Actions through an Ensemble Vote from Vector Representation and Frequency Analysis Models

Moon, Changsung; Medd, Dakota; Jones, Paul; Harenberg, Steve; Oxbury, W. M.; Samatova, Nagiza F.

doi:10.1137/1.9781611974348.11

Cited by 3 publications

(1 citation statement)

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“…We found that an online Hidden Markov Model (HMM) [15] approach worked well on the OSXInstrumenter and ChromeInstrumenter sequence data, giving around 75% accuracy for some users when predicting the next 3 applications or web domains they might access. However, we were able to improve on this using a combination of a frequency-based analysis, and a vector representation of user actions [16]. Research on these techniques is widespread; another good example can be found in [17].…”

Section: A Foundations Of a Recommender Systemmentioning

confidence: 99%

A Versatile Platform for Instrumentation of Knowledge Worker's Computers to Improve Information Analysis

Jones

Thakur

Cox

et al. 2016

2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService)

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Information analysis tasks are becoming increasingly complex as the growth in volume and variety of available data continues to outpace methods to automatically analyze it. The result is an increasing burden on knowledge workers. Industry and academia are currently lacking platforms and tools that can help provide enterprise-wide understanding of how humans analyze information. Through instrumentation of knowledge worker's activities during analysis tasks, we can enable research into techniques that address this need.Several research and design challenges need to be addressed, however, to develop scalable, robust and efficient instrumentation methods for collecting streaming data on relevant humanmachine interactions. This data is typically high volume and multi-modal. We present an instrumentation platform to meet these and future challenges in better understanding and improving the craft of information analysis. Our platform provides an extensible framework to instrument analyst's workstations during information processing tasks, and includes a streaming data processing pipeline that supports real-time analysis of large volumes of event data. We have built the instrumentation platform using the latest open-source scalable and flexible web components and hardware infrastructure. Researchers from science, engineering, and humanities are using our platform to gain insight into tool usage, analytical workflows, and collaboration patterns. Several use cases from these groups are described. Our platform provides a unique, pragmatic and holistic foundation to understand the behavior of knowledge workers, and to support applications that assist with information analysis.

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Section: A Foundations Of a Recommender Systemmentioning

confidence: 99%