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 2013.
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.
SpatialHadoop 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/"
RecDB 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 visit: "http://www-users.cs.umn.edu/~sarwat/RecDB/"
Sindbad is a location-based social networking 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 it
injects location-awareness users receive friend news feed based on their locations the spatial extents of messages posted by their friends. Currently,
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 consider social
relevance for its users, but they also consider spatial relevance. Since location-aware social networking systems have to deal with large number
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 PostgreSQL
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/"
Monitoring 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.
This 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 information.
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.