Information Analysis
In the era of information society, people are inundated by floods of low-quality,
high volumes of information. Nowadays, in most countries of the world,
huge numbers of surveillance cameras have been installed in every airport,
train station, bank, shopping mall, office building, etc. Each surveillance
camera captures every moment of the target scene with a frame rate of 30
fps. Such high-volume, continuous video streams present huge challenges
to us because it is unrealistic to rely on humans to monitor such video
streams 24 hours a day, 7 days a week without overlooking any important
events. The fact is that video streams generated by most surveillance
cameras are just recorded on video types or hard disks without being actively
monitored.
On the other hand, the advent of worldwide web and blogosphere
has dramatically lowered the threshold for ordinary people to publish their
own experiences and opinions. This development has lead to the phenomenon
that the quantity of information has exploded, while the quality of information
has deteriorated. Although Internet search engines can help users to retrieve
their desired information through keyword-based search, in general these
search engines only take good care of a small number of dominant websites
and completely ignore the data in the long tail. However, long-tail data
also contain a lot of valuable information, such as grassroots opinions,
sentiments, wisdom of crowds, etc. Such information is more valuable for
the purposes of business intelligence, better decision making, and market
strategies. We need new technologies to dig out such valuation information
from the long tail data.
In our Cupertino office, we strive to develop cutting edge technologies to dramatically
improve humans’ abilities to:
- Sift through large volumes of raw video
streams and noisy, low-quality Internet data to extract highly semantic,
value-added information
- Summarize and visualize large volumes of Internet
data to discover their overall pictures and internal structures
By developing these technologies, we want to turn raw
video streams into valuable information sources, noisy, low-quality Internet
data into knowledge-bases.
Such technologies have great potentials in a variety of areas such as intelligent
video surveillance, customer attributes and shopping behavior recognitions
for retail business, targeted advertisements, grassroots wisdom discovery,
market analysis, business intelligence,
etc.
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Data Management
In today's world the amount of data is increasing at a staggering pace. Organizations are in critical need to manage data very efficiently and effectively to stay competitive, increase efficiency, and quickly respond to market opportunities and threats. On top of the ever increasing data amounts, emerging data types, such as RFID data, click streams, application execution logs etc., create additional challenges in data management. Although traditional relational database systems have matured after decades of research and development, they only manage small percentage of current enterprise data. As a result, enterprises underutilize their data because of lack of effective data management capabilities. The Data Management Department focuses on conducting world-class research to create cutting edge technologies to address the data management challenges we are facing in today's world.
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