to Jie Bao and Mohamed Sarwat for successfully defending their PhD. Jie
is now a Researcher at Microsoft Research Asia, while Sarwat is
Assistant Professor at Arizona State University.
Aug. 2014:Two papers and one Demos accepted at ACM SIGSPATIAL 2014
Ahmed Eldawy, Yuan Li, Mohamed
Mokbel and Ravi Janardan. " CG_Hadoop:
Computational Geometry in MapReduce". In
Proceedings of the ACM SIGSPATIAL International Conference on Advances
in Geographic Information Systems, SIGSPATIAL GIS 2013,
Orlando, Florida, November 2013 (acceptance rate 17%).
A Paper accepted in SSTD 2013
Mohamed F. Mokbel, Louai
Alarabi, Jie Bao, Ahmed Eldawy, Amr Magdy, Mohamed Sarwat, Ethan
Waytas, and Steven Yackel. "MNTG:
An Extensible Web-based Traffic Generator". In
Proceedings of the 13th International Symposium on Spatial and Temporal
Databases, SSTD 2013, Munich, Germany, August
PI- National Science Foundation (NSF)- IIS-1218168:
III: Small: Towards Spatial Database Management Systems for Flash
Memory Storage. PI: Mohamed Mokbel, Co-PI: Shashi Shekhar. $500,000,
09/01/12 - 8/31/15.
Two Papers in ACM SIGSPATIAL GIS 2012
Abdeltawab Hendawi and Mohamed
F. Mokbel. "Panda: A Predictive Spatio-Temporal Query
Processor". In Proceedings of the ACM SIGSPATIAL
International Conference on Advances in Geographic Information Systems,
SIGSPATIAL GIS 2012, Redondo Beach,
California, November 2012.
NSF Workshop on Social Networks
and Mobility in the Cloud, Arlington, VA, Feb 23-24, 2012.
is an open source MapReduce framework with built-in
support for spatial data. It employs the MapReduce programming paradigm
for distributed processing to build a general purpose tool for large
scale analysis of spatial data on large clusters. Users can interact
easily with SpatialHadoop through a high level language with built-in
support for spatial data types and spatial operations. Existing spatial
data sets can be loaded in SpatialHadoop with the built in spatial data
types point, polygon and rectangle. SpatialHadoop is also extensible
and more data types can be added by users. In addition, the data sets
are stored efficiently using built-in indexes (Grid file or R-tree)
which speed up the retrieval and processing of these data sets. Users
can build an index of their choice with a single command that runs in
parallel on the machines in the cluster. Once the index is built, users
can start analyzing their data sets using the built in spatial
operations (range query, k nearest neighbor and spatial join). The
extensibility of SpatialHadoop allows users to implement more spatial
operations as MapReduce programs. For more information, please visit: "http://spatialhadoop.cs.umn.edu/"
is an open source recommendation engine built entirely inside
PostgreSQL 9.2. RecDB allows application developers to build
recommendation applications in a heartbeat through a wide variety of
built-in recommendation algorithms like user-user collaborative
filtering, item-item collaborative filtering, singular value
decomposition. Applications powered by RecDB can produce online and
flexible personalized recommendations to end-users. An out-of-the-box
tool for web and mobile developers to implement a myriad of
recommendation applications. The system is easily used and configured
so that a novice developer can define a variety of recommenders that
fits the application needs in few lines of SQL. Crafted inside
PostgreSQL database engine, RecDB is able to seamlessly integrate the
recommendation functionality with traditional database operations,
i.e., SELECT, PROJECT, JOIN, in the query pipeline to execute ad-hoc
recommendation queries. The system optimizes incoming recommendation
queries (written in SQL) and hence provides near real-time personalized
recommendation to a high number of end-users who expressed their
opionions over a large pool of items. For more information, please
Sindbad is a location-based
system. Sindbad distinguishes itself from existing social networking
within every aspect of social interaction and functionality in the
system. For example,
posted messages in Sindbad have inherent spatial extents (i.e., spatial
location and spatial range) and systems (e.g., Facebook and Twitter) as
injects location-awareness users receive friend news feed based on
their locations the spatial extents of messages posted by their
Sindbad supports three new services beyond traditional social
networking services, namely, location-aware news feed, location-aware
recommendation, and location-aware ranking. These new services not only
relevance for its users, but they also consider spatial relevance.
Since location-aware social networking systems have to deal with large
of users, large number of messages, and user mobility, efficiency and
scalability are important issues. To this end, Sindbad encapsulates its
three main services inside the query processing engine of PostgreSQL.
Usage and internal functionality of Sindbad, implemented with
and Google Maps API, are demonstrated through a web interface. For more
information, please visit: "http://sindbad.cs.umn.edu/"
MinnesotaTG is a project developed at the
University of Minnesota. MinnesotaTG is built based on two existing
traffic generators: (1) BerlinMod and (2) Thomas-Brinkhoff. The purpose
of MinnesotaTG is to take an arbitrary region in the United States and
generate traffic data from that region. Without this tool, generating
this traffic is a complicated and drawn out process because of the
number of configuration steps necessary to get either Thomas-Brinkhoff
or BerlinMod both up and running, and able to work on a user specified
region. The generation of the traffic is not done by the tool itself,
but rather it is performed by these two different traffic generators.
For more information, please visit: "http://mntg.cs.umn.edu/"
personal locations with a potentially untrusted server poses
privacy threats to the monitored individuals. To this end, we propose a
privacy-preserving location monitoring system for wireless sensor
networks. In our system, we design two in-network location
anonymization algorithms, namely, resource- and quality-aware
algorithms, that aim to enable the system to provide high quality
location monitoring services for system users, while preserving
personal location privacy. Both algorithms rely on the well established
k-anonymity privacy concept to enable trusted sensor nodes to provide
the aggregate location information of monitored persons for our system.
Each aggregate location is in a form of a monitored area A along with
the number of monitored persons residing in A, where A contains at
least k persons. The resource-aware algorithm aims to minimize
communication and computational cost, while the quality-aware algorithm
aims to maximize the accuracy of the aggregate locations by minimizing
their monitored areas. To utilize the aggregate location information to
provide location monitoring services, we use a spatial histogram
approach that estimates the distribution of the monitored persons based
on the gathered aggregate location information. The estimated
distribution is used to provide location monitoring services through
answering range queries.
project tackles a major privacy concern in current location-based
services where users have to continuously report their locations to the
database server in order to obtain the service. For example, a user
asking about the nearest gas station has to report her exact location.
With untrusted servers, reporting the location information may lead to
several privacy threats. In this paper, we present Casper1; a new
framework in which mobile and stationary users can entertain
location-based services without revealing their location information.
Casper consists of two main components, the location anonymizer and the
privacy-aware query processor. The location anonymizer blurs the users�
exact location information into cloaked spatial regions based on
user-specified privacy requirements. The privacy-aware query processor
is embedded inside the location-based database server in order to deal
with the cloaked spatial areas rather than the exact location
The views and opinions expressed in this page are strictly those of the page author. The contents of this page have not been reviewed or approved by the University of Minnesota.