http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=9972883 This is an integrated research project in multimedia. Areas of research are a multimedia research testbed, QoS management for networked multimedia, distributed multimedia database management, integrated security at both user and network levels, and media capture and presentation. Motivating applications in veterinary medicine, nuclear engineering and Purdue OnLine, a course delivery system are an integral part of the project. http://www.cs.purdue.edu/msi/ Combining the functionality of personal locator technologies, wireless and cellular telephone technologies, and information technologies enables new environments where virtually all objects of interest can determine their locations. The PLACE (Pervasive Location Aware Computing Environments) project focuses on the efficient support of location-aware services in such a new environment. Projects in this area include: http://www.cs.purdue.edu/place/ This is a multidisciplinary research effort at Purdue University for advancing multimedia database technology. This initiative has successfully developed a video-enhanced database system that supports comprehensive and efficient database management for digital video databases, including: feature-based preprocessing for video content representation and indexing, video and meta-data storage, video query processing, buffer and storage management, and continuous video streaming. Our fundamental concept is to provide a full range of functionality for video as a fundamental database object. Projects in this area include: http://www.cs.purdue.edu/vdbms/ http://www.cs.purdue.edu/homes/aref/dbsystems.html This project supports an integrated research and education effort in the area of database technologies for emerging applications. Its goal is to develop database technologies that address the demands of data-intensive applications that handle distributed, spatial, and multimedia data sources. We continue to make progress toward our targeted objectives in both research and education. Projects in this area include: http://www.cs.purdue.edu/Nile http://www.cs.purdue.edu/spgist Data mining relies on the collection of massive amounts of data - but this often collides with privacy considerations. How do we mine data when privacy concerns limit access to the data? We are developing technology to address this in the distributed case: the data to be mined is contained at multiple sites, but the sites are unable to release the data. The solutions involve algorithms that share some information to calculate correct results, where the shared information can be shown not to disclose private data. Privacy issues in distributed data mining is only one area where data mining and security interact. Other areas of research include security concerns posed by data mining results (the data isn't private, but what might be learned from it is) and applications of data mining to security (e.g., intrusion detection). Projects in this area include: https://www.fastlane.nsf.gov/servlet/showaward?award=0428168 https://www.fastlane.nsf.gov/servlet/showaward?award=0312357 http://www.21fund.org/pdfs/Round_6_Summary.pdf Purdue is exploring new challenges in data mining: Streaming data, new types of mining (such as combining spatial and temporal data with more "conventional" mining), ... Projects in this area include: https://www.fastlane.nsf.gov/servlet/showaward?award=0312357 http://www.cs.purdue.edu/homes/clifton/projects/PEEC.pdf ICDS is building on a history of research in heterogenous databases to address new challenges in this area, particularly data integration in an environment where privacy and security concerns limit the free exchange of data. Projects in this area include: https://www.fastlane.nsf.gov/servlet/showaward?award=0428168 The last two decades have witnessed a dramatic shift in life sciences research. Life sciences are moving towards data-rich fields; the volume of data being produced daily is enormous. Unfortunately, this tremendous data growth and diversification has not been sustained by similar advances in data management and massive data analysis. ICDS has engaged in several projects in bioinformatics to address different issues where database support is needed. Projects in this area include: http://www.cs.purdue.edu/~mourad/proteomics http://www.cs.purdue.edu/~mourad/annotation http://www.cs.purdue.edu/~mourad/ecoli http://www.cs.purdue.edu/homes/sunil/cerias/research.html Is it possible to leverage the power of today's commercial RDBMS packages to simplfy the tast of managing and searching intrusion detection data? Can we make it easier for the administrator to archive and manipulate this information, make it easier for the security personel to audit and mine this data, and easier for the manager to interpret this data? If so, can we do it in a way that is inexpensive and performs well? http://www.cs.purdue.edu/homes/sunil/career.html The goal of this project is to develop a broad class of innovative techniques to alleviate the I/O bottleneck for modern database applications. The project focuses on data-intensive applications that handle multi-dimensional and multimedia data. The research has two major directions. The first is the development of declustering schemes for the efficient execution of range and nearest-neighbor queries over large multi-dimensional datasets under realistic assumptions such as non-constant disk I/O times, and non-uniform data and query distributions. The second addresses the storage and content-based retrieval of multiple-quality, multimedia documents. http://www.cs.purdue.edu/hpkb/ The KPP aims to revolutionize maintenance operations for Navy vessels. It is an excellent example of the impact a university can have on bringing new ideas to practice, and how practical issues from government and industry can impact university education and research. http://www.cs.purdue.edu/homes/sion/wm/ With the notable exception of software watermarking, the overwhelming majority of research efforts have been invested in the framework of multimedia data (e.g. images, video and audio). In this effort , we analyze digital watermarking from a higher level, domain-independent perspective. We propose a theoretical model and ask: are there any limitations to what watermarking can do? What are these and when can they be reached? We then propose, design and analyze watermarking solutions for (i) numeric sets, (ii) numeric relational data, (iii) categorical data, (iv) streams and (v) semi-structures. We are working on space-efficient data structures that provably require roughly the same space as the optimally compressed text, while still allowing fast and powerful searches. The original text is not needed for the searching and can be reconstructed from the index. In other words, the index combines the storage efficiency of compressed text with the index capability of powerful data structures like suffix trees. Empirical studies show superior performance in practice compared with other methods. http://www.cs.purdue.edu/graduate_program/projects/When.htm http://www.cs.purdue.edu/homes/singh35/udbms/ In many systems, sensors are used to acquire information from external environments such as temperature, pressure and locations. Due to continuous changes in these values, and limited resources (e.g., network bandwidth and battery power), it is often infeasible for the database to store the exact values at all times. Queries that uses these old values can produce invalid results. In order to manage the uncertainty between the actual sensor value and the database value, we propose a system called U-DBMS. U-DBMS extends the database system with uncertainty management functionalities. In particular, each data value is represented as an interval and a probability distribution function, and it can be processed with probabilistic query operators to produce imprecise (but correct) answers. We are developing a PostgreSQL-based system that handles uncertainty and probabilistic queries for constantly-evolving data.