| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 27, 2014 |
| Latest Amendment Date: | August 27, 2014 |
| Award Number: | 1447793 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2014 |
| End Date: | August 31, 2018 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3 RUTGERS PLZ NEW BRUNSWICK NJ US 08901-8559 (848)932-0150 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
110 Frelinghuysen Road Piscataway NJ US 08854-8072 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Algorithmic Foundations, Big Data Science &Engineering |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The past decade has seen dramatic growth in systems that collect data from human activities. Online social networks record not just friendships, but interactions, messages, photos, and interests. Mobile devices track location via GPS information. Online stores monitor millions of customers as they explore and transact. Sensors, wearable and otherwise, produce detailed behavioral data. Collectively, this provides ever-larger collections of human social-activity information -- we refer to this as Big Social Data. While Big Social Data is growing rapidly, the available processing resources -- CPU, memory, communication -- are growing at a slower pace. To realize the promise of big social data, we need algorithms that use only sublinear resources, that is, resources growing much less than the growth of the data in suitable parameters. Designing these algorithms will be the core activity of this research project. This work will be in consultation with practitioners handling Big Social Data, leading to many opportunities for technology transfer. The research program both enables and benefits from an education and outreach program that will help develop the new breed of algorithmically-trained data scientists for Big Social Data.
Emerging systems -- MapReduce, Hadoop, Spark, Storm, etc. -- use large scale distributed computation: clusters of machines not only gathering and storing data in parallel, but also working together to perform computations. Often, these systems and applications work via incremental processing, storing and returning only approximate solutions, trading off quality and certainty for efficiency. In addition, these systems take a data-centric view, wherein the data is stored as
For further information, see the project web site at http://www.stanford.edu/~ashishg/socialdata.html .
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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