Christan Grant One of the research theme of the UF Data Science Research lab is to adapt machine learning algorithm to real-time interactive query engines. Our prior work looked at implementing and optimizing information extraction and statistical machine learning operations in exploratory query processing systems. To push the envelop further, our newly completed paper (under-submission)
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UDA-GIST: Unified Data-Parallel and State-Parallel Analytics in DB
Comment Closed publications, research directions
Kun Li This work is recently accepted, to appear in Proceedings of VLDB 2015 Vol 8 Issue 5 and to be presented in Kona in September 2015. This is part of our continued effort to enable large-scale advanced statistical machine learning based analytics inside a DBMS system/SQL engine. — With the recent boom in Big
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Efficient In-Database Analytics with Graphical Models
Daisy Zhe Wang Comment Closed publications, research directions
Daisy Zhe Wang The IEEE Bulletin September 2014 Special Issue published articles describing efforts from various research groups on the recently emerged theme of Databases, Declarative Systems and Machine Learning. The 7 research project/groups are: University of Washington: Lifted Probabilistic Inference: A Guide for the Database Researcher by Eric Gribkoff, Dan Suciu, and Guy Van den
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ProbKB: A Probabilistic Knowledge Base System for Web Knowledge
Comment Closed publications, research directions
Yang Chen Today’s World Wide Web contains huge volumes of information about people, places, and events all over the world. Unfortunately, these information is intended for human readers and not ready for direct machine processing, making it hard to query and analyze systematically. To overcome this limit, researchers are trying to convert them into structured
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SMART Electronic Discovery
No Comment publications, research directions
Clint P. George Our recent work on Electronic Discovery (E-Discovery) evolved a novel E-Discovery retrieval model – SMARTeR, which employ the state-of-the-art document modeling algorithms for relevance ranking, classification, and prioritizing the review process – based on the traditional Computer Assisted Review (CAR) process (See Background). A critical task associated with the categorization of documents
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GPText: Greenplum Parallel Statistical Text Analysis Framework
No Comment publications, research directions
Kun Li Text analytics has gained much attention in the big data research community due to the large amounts of text data generated in organizations such as companies, government and hospitals everyday in the form of emails, electronic notes and internal documents. A good understanding of this unstructured text data is crucial for companies to
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Using the Crowd to Improve Information Extraction
No Comment publications, research directions
Sean Goldberg Information Extraction (IE) is the name given to the task of converting unstructured free text into a more structured form for better searching, analysis, and organization. Automating IE tasks is crucial to making sense of the enormous amount of unstructured information being put on the web everyday. Even the most current state-of-the-art algorithms
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Research Directions in the UF DSR Lab
Daisy Zhe Wang No Comment research directions
Daisy Zhe Wang I recently wrote an article for the CISE department newsletter describing various research work we do in the DSR Lab. In particular, I highlighted the knowledge base expansion work we are doing in collaboration with Google Research, and the TREC knowledge base acceleration competition we participated in the summer. Use this I
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