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Chaitanya Gokhale

Researcher at University of Wisconsin-Madison

Publications -  6
Citations -  417

Chaitanya Gokhale is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Unstructured data & Data management. The author has an hindex of 5, co-authored 6 publications receiving 377 citations. Previous affiliations of Chaitanya Gokhale include Indian Institute of Technology Bombay.

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Proceedings ArticleDOI

Corleone: hands-off crowdsourcing for entity matching

TL;DR: Corleone is described, a HOC solution for EM, which uses the crowd in all major steps of the EM process, and the implications of this work to executing crowdsourced RDBMS joins, cleaning learning models, and soliciting complex information types from crowd workers.
Journal ArticleDOI

Information extraction challenges in managing unstructured data

TL;DR: The work suggests that IE in managing unstructured data can open up many interesting research challenges, and that these challenges can greatly benefit from the wealth of work on managing structured data that has been carried out by the database community.
Proceedings Article

The Case for a Structured Approach to Managing Unstructured Data.

TL;DR: Drawing on the lessons learned while managing relational data, a structured approach to managing unstructured data is outlined and the potential implications of this approach to manage other kinds of non-relational data are discussed.
Proceedings ArticleDOI

Complex Group-By Queries for XML

TL;DR: The popularity of XML as a data exchange standard has led to the emergence of powerful XML query languages like XQuery and studies on XML query optimization, but even for data integration, there is a compelling need for performing group-by style aggregate operations.
Posted Content

The Case for a Structured Approach to Managing Unstructured Data

TL;DR: In this paper, a structured approach to managing unstructured data is presented, based on the lessons learned while managing relational data, and discussed the potential implications of this approach to manage other kinds of non-relational data.