IMSC Research

IMSC’s current research focus is on studying data-driven solutions for real-world applications by taking a geo-informatic approach in blending large amounts of data collected from real and virtual worlds.  In particular, we exploit the common coordinates of time and space in order to fuse heterogeneous real-time and historical data into actionable knowledge and bring them to the end-user for higher situational awareness. Consequently, our research activities can be categorized under the auspices of “Big Data”. 

Big Data is conventionally characterized by “V-starting” words such as: Volume, Velocity, Variety, Veracity, Verification, Value, etc. Volume is sometimes considered as the most challenging aspect of BigData due to the misinterpretation of the word “Big” in BigData.  However, what makes the problem challenging is the combination of large dataset with its other characteristics such as the real-time data processing requirement (Velocity) and/or multi-modality of the data (Variety).

IMSC’s research encompasses all the above aspects of BigData:
  • Storage: how to store and index a large amount of data, e.g., on Cloud
  • Queries: how to find data items effectively and efficiently
  • Machine learning and data mining: how to extract useful information from data
  • Distributed computing: end-to-end architectures for big data management
IMSC has a proven record in geo-informatics and digital media technologies, working with major IT industries. A sample of our research activities with recent selected papers is as follows:
Data Acquistion:
  • Spatial Crowdsourcing [ACM GIS 12][ACM GIS 13-2][ACM GIS 13-3]
  • Traffic Flow Data Extraction from Video [DNIS 13]
  • User-Generated Mobile Videos [MediaQ 13]
Data Storage & Access:
  • Cloud [CloudCom 10]
  • Spatio-temporal Indexing: [VLDB 12]
Data Analytics:
  • Location and Social Network [SIGMOD 13][ACM GIS 13-1]
  • Traffic Prediction [ICDM 12][ICDM 13]
  • Text Analysis [JNGC 12]
  • Visual Analytics

The above research is conducted in the context of the following BigData applications: 

Big Data in Transportation: TransDec



Big Data in Social Media: MediaQ



Big Data in Security: Janus Big Data in Health: GeoMed



[ACM GIS 13-1] Bei Pan, Yu Zheng, David Wilkie, and Cyrus Shahabi, Crowd Sensing of Traffic Anomalies based on Human Mobility and Social Media, ACM SIGSPATIAL GIS'13, Orlando, Florida, USA, November 5-8, 2013
[ACM GIS 13-2] Leyla Kazemi, Cyrus Shahabi, and Lei Chen, GeoTruCrowd: Trustworthy Query Answering with Spatial Crowdsourcing, International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2013), Orlando, Florida , November 5-8, 2013
[ACM GIS 13-3] Dingxiong Deng, Cyrus Shahabi, and Ugur Demiryurek, Maximizing the Number of Worker's Self-Selected Tasks in Spatial Crowdsourcing, ACM SIGSPATIAL GIS '13, Orlando, Florida, USA, November 5-8, 2013

[DNIS 13] Seon Ho Kim, Junyuan Shi, Abdullah Alfarrarjeh, Daru Xu, Yuwei Tan , and Cyrus Shahabi, Real-Time Traffic Video Analysis Using Intel Viewmont Coprocessor, DNIS 2013: 150-160, 2013
[SIGMOD 13] Huy Pham, Cyrus Shahabi, and Yan Liu, EBM - An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data, SIGMOD, New York, USA, June 2013
[ACM GIS 12] Leyla Kazemi and Cyrus Shahabi, GeoCrowd: Enabling Query Answering with Spatial Crowdsourcing, ACM SIGSPATIAL GIS, Redondo Beach, CA, November 2012
[ICDM 12] Bei Pan, Ugur Demiryurek, and Cyrus Shahabi, Utilizing Real-World Transportation Data for Accurate Traffic Prediction, IEEE International Conference on Data Mining (ICDM), Brussels, Belgium, December 2012
[VLDB 12] Houtan Shirani-Mehr, Farnoush Banaei-Kashani, and Cyrus Shahabi, Efficient Reachability Query Evaluation in Large Spatiotemporal Contact Datasets, 38th International Conference on Very Large Databases (VLDB), Istanbul, Turkey , August 2012
[IWGS 11] Farnoush Banaei-Kashani, Cyrus Shahabi, and Bei Pan, Discovering Traffic Patterns in Traffic Sensor Data, IWGS, ACMGIS 2011, Chicago, IL, November 2011

[CloudCom 10] Afsin Akdogan, Ugur Demiryurek, Farnoush Banaei-Kashani , and Cyrus Shahabi, Voronoi-based Geospatial Query Processing with MapReduce, IEEE Cloud Computing Technology and Science (CloudCom), Best Paper Award, Indianapolis, IN, November 2010

[MediaQ 13] Seon Ho Kim, Ying Lu, Giorgos Constantinou, Cyrus Shahabi, Guanfeng Wang, Roger Zimmermann, MediaQ: Online Mobile Media Management Framework, Submitted for publication.