Koala: capture, share, automate, personalize business processes on the web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sound index: charts for the people, by the people
Communications of the ACM - The Status of the P versus NP Problem
Sound index: charts for the people, by the people
Communications of the ACM - The Status of the P versus NP Problem
R2M: a reputation model for mashups
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
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The VLDB Journal — The International Journal on Very Large Data Bases
OXPath: little language, little memory, great value
Proceedings of the 20th international conference companion on World wide web
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Proceedings of the 20th international conference companion on World wide web
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IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
OXPath: A language for scalable data extraction, automation, and crawling on the deep web
The VLDB Journal — The International Journal on Very Large Data Bases
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Prevailing wisdom assumes that there are well-defined, effective and efficient methods for accessing Deep Web content. Unfortunately, there are a host of technical and non-technical factors that may call this assumption into question. In this paper, we present the findings from work on a software system, which was commissioned by the British Broadcasting Corporation (BBC). The system requires stable and periodic extraction of Deep Web content from a number of online data sources. The insight from the project brings an important issue to the forefront and under-scores the need for further research into access technology for the Deep Web.