Project Title: CAREER: Extensible Personalization of Spatial and Spatio-temporal Database Management Systems Project Award Number: IIS-0952977 PI Name: Mohamed Mokbel Department: Computer Science and Engineering Institution: University of Minnesota Address: 200 Union ST SE, Minneapolis, MN, 55455, USA Email: mokbel@cs.umn.edu URL: www.cs.umn.edu/~mokbel |
PhD. 2016; Assistant Professor at University of California Riverside | |
Title: SpatialHadoop: A Map-Reduce framework for Big Spatial Data | |
PhD. 2015; Post-Doc at University of Virginia | |
Title: Scalable Spatial Predictive Query Processing for Moving Objects | |
PhD. 2014; Assistant Professor at Arizona State University | |
Title: Database Systems Support for Collaborative Filtering Recommender Systems | |
PhD. 2014; Researcher at Microsoft Research Asia | |
Title: Towards Location-aware News Feeds and Recommendations | |
PhD. 2011; Researcher at Microsoft Research | |
Title: Extensible preference evaluation in database systems | |
PhD 2011; Assistant Professor at Alexandria University, Egypt | |
Title: Preference Queries Processing over Imprecise Data |
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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/" | ||
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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/" | ||
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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/" | ||
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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/" | ||