The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Giovanna Guerrini - One of the best experts on this subject based on the ideXlab platform.
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25 years of query processing from a single Stored Data Set to big Data and beyond
A Comprehensive Guide Through the Italian Database Research, 2018Co-Authors: Barbara Catania, Giovanna GuerriniAbstract:From the late 1970s, the Database research community has devoted huge efforts in devising more and more effective and efficient solutions for processing queries against Data. In this chapter, we retrace some of challenges that have been faced in the last 25 years to enable Data to be effectively and efficiently queried and how the notions of queries and query processing themselves have evolved during these five lusters.
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A Comprehensive Guide Through the Italian Database Research - 25\(+\) Years of Query Processing - From a Single, Stored Data Set to Big Data (and Beyond)
Studies in Big Data, 2017Co-Authors: Barbara Catania, Giovanna GuerriniAbstract:From the late 1970s, the Database research community has devoted huge efforts in devising more and more effective and efficient solutions for processing queries against Data. In this chapter, we retrace some of challenges that have been faced in the last 25 years to enable Data to be effectively and efficiently queried and how the notions of queries and query processing themselves have evolved during these five lusters.
Barbara Catania - One of the best experts on this subject based on the ideXlab platform.
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25 years of query processing from a single Stored Data Set to big Data and beyond
A Comprehensive Guide Through the Italian Database Research, 2018Co-Authors: Barbara Catania, Giovanna GuerriniAbstract:From the late 1970s, the Database research community has devoted huge efforts in devising more and more effective and efficient solutions for processing queries against Data. In this chapter, we retrace some of challenges that have been faced in the last 25 years to enable Data to be effectively and efficiently queried and how the notions of queries and query processing themselves have evolved during these five lusters.
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A Comprehensive Guide Through the Italian Database Research - 25\(+\) Years of Query Processing - From a Single, Stored Data Set to Big Data (and Beyond)
Studies in Big Data, 2017Co-Authors: Barbara Catania, Giovanna GuerriniAbstract:From the late 1970s, the Database research community has devoted huge efforts in devising more and more effective and efficient solutions for processing queries against Data. In this chapter, we retrace some of challenges that have been faced in the last 25 years to enable Data to be effectively and efficiently queried and how the notions of queries and query processing themselves have evolved during these five lusters.
D. Fraser - One of the best experts on this subject based on the ideXlab platform.
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Baseline performance analysis of the LSD/DOA ATR against MSTAR Data
Algorithms for Synthetic Aperture Radar Imagery V, 1998Co-Authors: D. Cyganski, James C. Kilian, D. FraserAbstract:Large computational complexity arises in model-based ATR systems because an object's image is typically a function of several degrees of freedom, such as target class, pose, articulation, configuration and sensor geometry. Most model- based ATR systems treat this dependency by incorporating an exhaustive search through a library of image views. This approach, however, requires enormous storage and extensive search processing. Some ATR systems reduce the size of the library by forming composite averaged images at the expense of reducing the captured pose specific information, usually resulting in a decrease in performance. The Linear Signal Decomposition/Direction of Arrival (LSD/DOA) system, on the other hand, forms a reduced-size, essential-information object Data Set which implicitly incorporates target and sensor variation specific Data. This reduces ATR processing by providing a low computational-cost indexing function with little loss of discrimination and pose estimation performance. The LSD/DOA system consists of two independent components: a computationally expensive off-line component which forms the object representation and a computationally inexpensive on-line object recognition component. The size of the Stored Data Set may also be adjusted, providing a means to trade off complexity versus performance. The focus of this paper will be the performance of the LSD/DOA ATR against the MSTAR (public) Data Set.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Performance of an ATR index module against MSTAR Data
Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No.99CH36249), 1Co-Authors: D. Cyganski, J. Kilian, D. FraserAbstract:We present a performance analysis of an indexing module contained in an automatic target recognition (ATR) system developed at Worcester Polytechnic Institute (WPI). The linear signal decomposition/direction of arrival (LSD/DOA) technique provides a low computational-cost indexing function for pose in ATR applications. The LSD/DOA technique forms a reduced-size, essential-information object Data Set which implicitly incorporates target and sensor variation specific Data. To control the computational costs, the system consists of two independent components: a computationally expensive off-line component which forms the object representation and a computationally inexpensive on-line object recognition component. The size of the Stored Data Set may also be adjusted providing a means to trade off complexity versus performance. Synthetic aperture radar (SAR) Data collected as part of the Moving and Stationary Target Acquisition and Recognition (MSTAR) program has been released to the public, providing an opportunity for ATR performance assessment against a standard high quality Data Set. We present the results of a number of simulated tests against the MSTAR public Database to demonstrate the performance of the LSD/DOA index module on a given target along with the performance of the WPI ATR itself as a function of the depression angle and target configuration.
Yannis Kotidis - One of the best experts on this subject based on the ideXlab platform.
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SIGMOD Conference - Branch-and-bound algorithm for reverse top-k queries
Proceedings of the 2013 international conference on Management of data - SIGMOD '13, 2013Co-Authors: Akrivi Vlachou, Christos Doulkeridis, Kjetil Nørvåg, Yannis KotidisAbstract:Top-k queries return to the user only the k best objects based on the individual user preferences and comprise an essential tool for rank-aware query processing. Assuming a Stored Data Set of user preferences, reverse top-k queries have been introduced for retrieving the users that deem a given Database object as one of their top-k results. Reverse top-k queries have already attracted significant interest in research, due to numerous real-life applications such as market analysis and product placement. Currently, the most efficient algorithm for computing the reverse top-k Set is RTA. RTA has two main drawbacks when processing a reverse top-k query: (i) it needs to access all Stored user preferences, and (ii) it cannot avoid executing a top-k query for each user preference that belongs to the result Set. To address these limitations, in this paper, we identify useful properties for processing reverse top-k queries without accessing each user's individual preferences nor executing the top-k query. We propose an intuitive branch-and-bound algorithm for processing reverse top-k queries efficiently and discuss novel optimizations to boost its performance. Our experimental evaluation demonstrates the efficiency of the proposed algorithm that outperforms RTA by a large margin.
D. Cyganski - One of the best experts on this subject based on the ideXlab platform.
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Baseline performance analysis of the LSD/DOA ATR against MSTAR Data
Algorithms for Synthetic Aperture Radar Imagery V, 1998Co-Authors: D. Cyganski, James C. Kilian, D. FraserAbstract:Large computational complexity arises in model-based ATR systems because an object's image is typically a function of several degrees of freedom, such as target class, pose, articulation, configuration and sensor geometry. Most model- based ATR systems treat this dependency by incorporating an exhaustive search through a library of image views. This approach, however, requires enormous storage and extensive search processing. Some ATR systems reduce the size of the library by forming composite averaged images at the expense of reducing the captured pose specific information, usually resulting in a decrease in performance. The Linear Signal Decomposition/Direction of Arrival (LSD/DOA) system, on the other hand, forms a reduced-size, essential-information object Data Set which implicitly incorporates target and sensor variation specific Data. This reduces ATR processing by providing a low computational-cost indexing function with little loss of discrimination and pose estimation performance. The LSD/DOA system consists of two independent components: a computationally expensive off-line component which forms the object representation and a computationally inexpensive on-line object recognition component. The size of the Stored Data Set may also be adjusted, providing a means to trade off complexity versus performance. The focus of this paper will be the performance of the LSD/DOA ATR against the MSTAR (public) Data Set.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Performance of an ATR index module against MSTAR Data
Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No.99CH36249), 1Co-Authors: D. Cyganski, J. Kilian, D. FraserAbstract:We present a performance analysis of an indexing module contained in an automatic target recognition (ATR) system developed at Worcester Polytechnic Institute (WPI). The linear signal decomposition/direction of arrival (LSD/DOA) technique provides a low computational-cost indexing function for pose in ATR applications. The LSD/DOA technique forms a reduced-size, essential-information object Data Set which implicitly incorporates target and sensor variation specific Data. To control the computational costs, the system consists of two independent components: a computationally expensive off-line component which forms the object representation and a computationally inexpensive on-line object recognition component. The size of the Stored Data Set may also be adjusted providing a means to trade off complexity versus performance. Synthetic aperture radar (SAR) Data collected as part of the Moving and Stationary Target Acquisition and Recognition (MSTAR) program has been released to the public, providing an opportunity for ATR performance assessment against a standard high quality Data Set. We present the results of a number of simulated tests against the MSTAR public Database to demonstrate the performance of the LSD/DOA index module on a given target along with the performance of the WPI ATR itself as a function of the depression angle and target configuration.