What's Inside Web Templates 2.0.

 

 

 

 

 

 

 



   Current Projects

ST-Hadoop is a MapReduce framework that acknowledges the fact that space and time play a crucial role in query processing. ST-Hadoop is an open-source extension of a Hadoop framework that injects the spatiotemporal awareness in the code base of four layers inside SpatialHadoop, namely, language, indexing, MapReduce, and operations layers. The spatio-temporal indexing techniques inside ST-Hadoop primarily tuned to provide the accommodation of new updated dataset efficiently without the need to rebuild its index. The key point behind the performance gain of ST-Hadoop is the idea of indexing, where data are temporary loaded and divided across computation nodes. For more information, please visit: "http://st-hadoop.cs.umn.edu/"
 
Kite is an open-source system to index and query Twitter-like data (Microblogs data). Microblogs in general are the micro-length posts that are generated by hundreds of millions of web users everyday, like tweets, online reviews for products and movies, user comments on news media or social media, and user check-ins on location-aware web services. This data is easy-to-produce by users and so it comes literally in thousands of records every single second, carrying very rich user-generated contents such as news, opinions, discussions, as well as meta data including location information, language information, and personal information. The rich content and the popularity of microblogging platforms results in Microblogs being exploited in a wide variety of important applications including disseminating news and citizen journalism, events detection and analysis, rescue services during natural disasters, and geo-targeted advertising. Kite provides the scalable infrastructure to query this data efficiently without worrying about all the complications of managing the data under the hood. Using Kite, one can build a very efficient application on top of Microblogs in just few minutes. Kite is implemented as a distributed system on top of Apache Ignite system and Hadoop Distributed File System (HDFS). It is scalable to digest more than 10,000 Microblog/second on each machine with tunable memory resources usage. It could organize billions of historical data in efficient temporal index structures to be queried very fast. Kite also provides real-time query response in the order of few milliseconds for a variety of queries on spatial and non-spatial attributes. For more information, pleease visit: "http://kite.cs.umn.edu/"
 
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/"
 
Several space agencies such as NASA are continuously collecting datasets of earth dynamics—e.g., temperature, vegetation, and cloud coverage—through satellites. This data is stored in a publicly available archive for scientists and researchers and is very useful for studying climate, desertification, and land use change. The benefit of this data comes from its richness as it provides an archived history for over 15 years of satellite observations. Unfortunately, the use of such data is very limited due to the huge size of archives (> 500TB) and the limited capabilities of traditional applications. In this project, we present Shahed, an interactive system which provides an efficient way to index, query, and visualize satellite datasets available in NASA archive. Shahed is composed of four main modules. The uncertainty module resolves data uncertainty imposed by the satellites. The indexing module organizes the data in a novel multi-resolution spatio-temporal index designed for satellite data. The querying module uses the indexes to answer both spatiotemporal selection and aggregate queries provided by the user. The visualization module generates images, videos, and multilevel images which gives an insight of data distribution and dynamics over time. This demo gives users a hands-on experience with Shahed through a map-based web interface in which users can browse the available datasets using the map, issue spatiotemporal queries, and visualize the results as images or videos.
 
Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not really available in most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. This project presents TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap dataset; the most com- prehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. TAREEG is accessible via http://www.tareeg.org/
 
 
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/"
 



   Past Projects

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/"
 
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.