Peter B. Lubell-Doughtie

Prabhas Pokharel presented our paper, Improving Data Collection and Monitoring through Real-time Data Analysis on Friday at ACM DEV 2013 in Bangalore.  Below the is poster:

The paper was coauthored with Prabhas Pokharel, Mark Johnston, and Vijay Modi.  The abstract is below:

Feedback based on real-time data is increasingly important for ICT-based interventions in the developing world. Applications such as facility inventories, summarization of patient data from community health workers, etc. need processes for analyzing and aggregating datasets that update over time. In order to facilitate such processes, we have created a modular web service for real-time data analysis: bamboo.

If you are interested in using bamboo also see pybamboo and bamboo.js.

We now have a reasonable alpha version of Bamboo online, from the docs:

Bamboo provides an interface for merging, aggregating and adding algebraic calculations to dynamic datasets. Clients can interact with Bamboo through a REST web interface and through Python.

With JavaScript and Python libraries; and many operations to choose from:

On July 23rd and 24th Alex Dorey and I will be presenting formhub at the DataDev workshop at the IEEE Mobile Data Management (MDM 2012) conference.

Today Kenneth Hamilton and I presented at the Society for Scholarly Publishing (SSP 2012) conference. Below are the slides, which are also available here. Additionally, here is a brief post on the SSP Startup Panel and our co-presenters.

Download the presentation.

Today I will be presenting a poster at ECIR 2012 for a paper Katja Hofmann and I have written.  The abstract and full paper are included below.

Abstract

In recall-oriented search tasks retrieval systems are privy to a
greater amount of user feedback. In this paper we present a novel method of combining relevance feedback with learning to rank. Our experiments use data from the 2010 TREC Legal track to demonstrate that learning to rank can tune relevance feedback to improve result rankings for specific queries, even with limited amounts of user feedback.

P. Lubell-Doughtie and K. Hofmann, “Learning to Rank from Relevance Feedback for e-Discovery,” in ECIR, 2012. pdf