• Home
  • Blog
  • People
  • Projects
  • Publications
  • Seminars
  • DSR Expo
  • Courses

Data Science Research

Menu
  • Home
  • Blog
  • People
  • Projects
  • Publications
  • Seminars
  • DSR Expo
  • Courses
Home › Uncategorized › Taming The Data Monster To Make Better Decisions

Taming The Data Monster To Make Better Decisions

Daisy Zhe Wang August 17, 2018     Comment Closed     Uncategorized

Daisy Zhe Wang

[Source: News from The Herbert Wertheim College of Engineering]

In today’s increasingly connected world, the sheer volume of messages we receive on any subject, including speech, images, video and metadata, as well as text imbedded in images and videos, is mind-boggling.

When something unexpected happens, government policy analysts need to inform and advise our nation’s leaders about the causes and effects immediately. Trying to deal with huge volumes of information, they can quickly reach capacity overload.

In 2013, Frank Konkel wrote in FCW, “Less than 24 hours after two explosions killed three people and injured dozens more at the April 15 Boston Marathon, the Federal Bureau of Investigation had compiled 10 terabytes of data in hopes of finding needles in haystacks of information that might lead to the suspects.”

Daisy Zhe Wang, at the University of Florida, is collaborating with researchers at USC’s Information Systems Institute (ISI), Rensselaer Polytechnic Institute and Columbia University to shorten the time it takes intelligence analysts to collect and interpret data about national and international events.

Dr. Boyan Onyshkevych, DARPA Program Manager for the AIDA (Active Interpretation of Disparate Alternatives) research program, emphasizes that analysts face a difficult task because messages from each data source are frequently considered independently of other sources, often resulting in only one interpretation. He elaborates that when two or more single interpretations are compared late in the process, the conclusion reached by analysts may not reflect a true consensus.

This research team led by ISI, one of several in the AIDA program funded by DARPA, will use machine intelligence to collect all messages from different sources and will then construct a complete knowledge base with the extracted information.

The research program will consider all messages as having equal value, but the probability graphs created from the knowledge base should help reduce possible bias or ambiguities in the information.

With a $1.17 million grant from ISI/DARPA, Wang will develop computer algorithms that can answer a query by reasoning over an event-driven knowledge base and generating disparate hypotheses about the links between causes and effects for the event in question.

“My algorithms will look at the knowledge graph and extract multiple hypotheses to answer an analyst’s question,” said Wang. “We will take into consideration a variety of measures including relevancy, uncertainty, similarity, consistency and connectedness between the knowledge elements, to generate a ranked list of hypotheses to answer a query.”

Dr. Wang is an Associate Professor in the Department of Computer & Information Science & Engineering at the UF’s Herbert Wertheim College of Engineering. She is also Director of the UF Data Science Research Lab and a member of the National Science Foundation’s Center for Big Learning at UF. Her research centers on reasoning and query processing over a probabilistic knowledge base. A probabilistic knowledge base is composed of entities, events, attributes and relationships between them. Her research has been supported by NSF and Google.

By using the multi-hypothesis semantic engine (MHSE) that will be developed from the AIDA research, analysts can generate clear “alternative interpretations of events, situations, and trends from sometimes noisy, conflicting, and potentially deceptive information environments,” according to Onyshkevych.

As analysts examine a hypothesis, they will be able to view the stream of messages associated with that hypothesis, elevating their confidence in its reliability.

Dependable information supplied to our country’s leaders in a timely manner will help produce sound government policies, fulfilling the ultimate vision of Dr. Wang and her colleagues for making the world a safer place.

Uncategorized

 Previous Post

Mining Rules Incrementally over Large Knowledge Bases

― February 12, 2018

Next Post 

Efficient Conditional Rule Mining over Knowledge Bases

― December 12, 2018

Related Articles

Efficient Conditional Rule Mining over Knowledge Bases
Mining Rules Incrementally over Large Knowledge Bases
Multimodal Learning for Web Information Extraction
Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases
Extracting Visual Knowledge from the Web with Multimodal Learning

Sponsors

pcori uf-clinical

NIST ICHP

DTCC

Recent Posts

Recent Posts

  • Efficient Conditional Rule Mining over Knowledge Bases
  • Taming The Data Monster To Make Better Decisions
  • Mining Rules Incrementally over Large Knowledge Bases
  • Multimodal Learning for Web Information Extraction
  • Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases

Related Blogs

  • ampLab
  • Data Beta
  • Fast ML

Post Categories

Categories

  • courses
  • NIST and open eval
  • publications
  • research directions
  • Uncategorized

Archives

Archives

  • December 2018
  • August 2018
  • February 2018
  • November 2017
  • June 2017
  • May 2017
  • March 2017
  • December 2016
  • October 2016
  • April 2016
  • March 2016
  • December 2015
  • November 2015
  • October 2015
  • May 2015
  • November 2014
  • October 2014
  • July 2014
  • May 2014
  • March 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013

Meta

DSR Wiki
Site Admin
WordPress.org

Recent Posts

  • Efficient Conditional Rule Mining over Knowledge Bases
  • Taming The Data Monster To Make Better Decisions
  • Mining Rules Incrementally over Large Knowledge Bases
  • Multimodal Learning for Web Information Extraction
  • Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases