The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform
Anastasia Ailamaki - One of the best experts on this subject based on the ideXlab platform.
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nodb efficient query execution on raw Data files
Communications of The ACM, 2015Co-Authors: Ioannis Alagiannis, Miguel Branco, Stratos Idreos, Renata Borovicagajic, Anastasia AilamakiAbstract:As Data collections become larger and larger, users are faced with increasing bottlenecks in their Data analysis. More Data means more time to prepare and to load the Data into the Database before executing the desired queries. Many applications already avoid using Database systems, for example, scientific Data analysis and social networks, due to the complexity and the increased Data-to-query time, that is, the time between getting the Data and retrieving its first useful results. For many applications Data collections keep growing fast, even on a daily basis, and this Data deluge will only increase in the future, where it is expected to have much more Data than what we can move or store, let alone analyze. We here present the design and roadmap of a new paradigm in Database systems, called NoDB, which do not require Data loading while still maintaining the whole feature set of a modern Database system. In particular, we show how to make raw Data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern Database Management Systems (DBMS), we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive Data Type Conversion. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw Data files, together with a flexible caching structure. We conclude that NoDB systems are feasible to design and implement over modern DBMS, bringing an unprecedented positive effect in usability and performance.
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nodb efficient query execution on raw Data files
International Conference on Management of Data, 2012Co-Authors: Ioannis Alagiannis, Renata Borovica, Miguel Branco, Stratos Idreos, Anastasia AilamakiAbstract:As Data collections become larger and larger, Data loading evolves to a major bottleneck. Many applications already avoid using Database systems, e.g., scientific Data analysis and social networks, due to the complexity and the increased Data-to-query time. For such applications Data collections keep growing fast, even on a daily basis, and we are already in the era of Data deluge where we have much more Data than what we can move, store, let alone analyze. Our contribution in this paper is the design and roadmap of a new paradigm in Database systems, called NoDB, which do not require Data loading while still maintaining the whole feature set of a modern Database system. In particular, we show how to make raw Data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern DBMS, we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive Data Type Conversion costs. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw Data files, together with a flexible caching structure. Our implementation over PostgreSQL, called PostgresRaw, is able to avoid the loading cost completely, while matching the query performance of plain PostgreSQL and even outperforming it in many cases. We conclude that NoDB systems are feasible to design and implement over modern Database architectures, bringing an unprecedented positive effect in usability and performance.
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SIGMOD Conference - NoDB: efficient query execution on raw Data files
Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012Co-Authors: Ioannis Alagiannis, Renata Borovica, Miguel Branco, Stratos Idreos, Anastasia AilamakiAbstract:As Data collections become larger and larger, Data loading evolves to a major bottleneck. Many applications already avoid using Database systems, e.g., scientific Data analysis and social networks, due to the complexity and the increased Data-to-query time. For such applications Data collections keep growing fast, even on a daily basis, and we are already in the era of Data deluge where we have much more Data than what we can move, store, let alone analyze. Our contribution in this paper is the design and roadmap of a new paradigm in Database systems, called NoDB, which do not require Data loading while still maintaining the whole feature set of a modern Database system. In particular, we show how to make raw Data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern DBMS, we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive Data Type Conversion costs. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw Data files, together with a flexible caching structure. Our implementation over PostgreSQL, called PostgresRaw, is able to avoid the loading cost completely, while matching the query performance of plain PostgreSQL and even outperforming it in many cases. We conclude that NoDB systems are feasible to design and implement over modern Database architectures, bringing an unprecedented positive effect in usability and performance.
P. Chan - One of the best experts on this subject based on the ideXlab platform.
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Toolkit support for multiuser audio/video applications
Computer Communications, 1992Co-Authors: David P. Anderson, P. ChanAbstract:Abstract Comet is a Unix/C++ toolkit for writing programs that involve multiple users, and which use digital audio and video. Comet provides a simple programming interface: the application builds a graph of objects representing speakers and microphones, mixers, files, and so on. Comet then realizes the graph by creating processes to handle mixing and file I/O if needed, and linking them by network connections to audio/video I/O servers. In addition, Comet addresses the interrelated issues of client requirements and resource management. It determines delay and throughput requirements, process placement, and Data Type Conversion; it deals with resource managers on the application's behalf. These mechanisms are based on a negotiation protocol among the components of the object graph.
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NOSSDAV - Toolkit Support fro Multiuser Audio/Video Applications
Network and Operating System Support for Digital Audio and Video, 1992Co-Authors: David P. Anderson, P. ChanAbstract:Comet is a UNIX/C++ toolkit for writing programs that involve multiple users and that use digital audio and video. Comet provides a simple programming interface: the application builds a graph of objects representing speakers and microphones, mixers, files, and so on. Comet then realizes the graph by creating processes to handle mixing and file I/O if needed, and linking them by network connections to audio/video I/O servers. In addition, Comet addresses the interrelated issues of client requirements and resource management. It determines delay and throughput requirements, process placement, and Data Type Conversion; it deals with resource managers on the application's behalf. These mechanisms are based on a negotiation protocol among the components of the object graph.
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ICDCS - Comet: a toolkit for multiuser audio/video applications
[1992] Proceedings of the 12th International Conference on Distributed Computing Systems, 1Co-Authors: David P. Anderson, P. ChanAbstract:Comet, a UNIX/C++ toolkit for writing programs, such as teleconferencing systems and collaborative editors, that involve multiple users and use digital audio video, is described. Comet provides a simple programming interface: the application builds a graph of objects representing speakers and microphones, mixers, files, etc. Comet realizes the graph by creating and interconnecting processes and audio/video I/O servers. It handles implementation details such as process placement, Data Type Conversion, and resource reservation. >
Wolfgang Karl - One of the best experts on this subject based on the ideXlab platform.
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reducing energy consumption of Data transfers using runtime Data Type Conversion
Automation Robotics and Control Systems, 2016Co-Authors: Michael Bromberger, Vincent Heuveline, Wolfgang KarlAbstract:Reducing the energy consumption of today's microprocessors, for which Approximate Computing AC is a promising candidate, is an important and challenging task. AC comprises approaches to relax the accuracy of computations in order to achieve a trade-off between energy efficiency and an acceptable remaining quality of the results. A high amount of energy is consumed by memory transfers. Therefore, we present an approach in this paper that saves energy by converting Data before transferring it to memory. We introduce a static approach that can reduce the energy upi¾źto a factor of 4. We evaluate different methods to get the highest possible accuracy for a given Data width. Extending this approach by a dynamic selection of different storage Data Types improves the accuracy for a 2D Fast Fourier Transformation by two orders of magnitude compared to the static approach using 16-bit Data Types, while still retaining the reduction in energy consumption. First results show that such a Conversion unit can be integrated in low power processors with negligible impact on the power consumption.
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ARCS - Reducing Energy Consumption of Data Transfers Using Runtime Data Type Conversion
Architecture of Computing Systems – ARCS 2016, 2016Co-Authors: Michael Bromberger, Vincent Heuveline, Wolfgang KarlAbstract:Reducing the energy consumption of today's microprocessors, for which Approximate Computing AC is a promising candidate, is an important and challenging task. AC comprises approaches to relax the accuracy of computations in order to achieve a trade-off between energy efficiency and an acceptable remaining quality of the results. A high amount of energy is consumed by memory transfers. Therefore, we present an approach in this paper that saves energy by converting Data before transferring it to memory. We introduce a static approach that can reduce the energy upi¾źto a factor of 4. We evaluate different methods to get the highest possible accuracy for a given Data width. Extending this approach by a dynamic selection of different storage Data Types improves the accuracy for a 2D Fast Fourier Transformation by two orders of magnitude compared to the static approach using 16-bit Data Types, while still retaining the reduction in energy consumption. First results show that such a Conversion unit can be integrated in low power processors with negligible impact on the power consumption.
Ioannis Alagiannis - One of the best experts on this subject based on the ideXlab platform.
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nodb efficient query execution on raw Data files
Communications of The ACM, 2015Co-Authors: Ioannis Alagiannis, Miguel Branco, Stratos Idreos, Renata Borovicagajic, Anastasia AilamakiAbstract:As Data collections become larger and larger, users are faced with increasing bottlenecks in their Data analysis. More Data means more time to prepare and to load the Data into the Database before executing the desired queries. Many applications already avoid using Database systems, for example, scientific Data analysis and social networks, due to the complexity and the increased Data-to-query time, that is, the time between getting the Data and retrieving its first useful results. For many applications Data collections keep growing fast, even on a daily basis, and this Data deluge will only increase in the future, where it is expected to have much more Data than what we can move or store, let alone analyze. We here present the design and roadmap of a new paradigm in Database systems, called NoDB, which do not require Data loading while still maintaining the whole feature set of a modern Database system. In particular, we show how to make raw Data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern Database Management Systems (DBMS), we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive Data Type Conversion. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw Data files, together with a flexible caching structure. We conclude that NoDB systems are feasible to design and implement over modern DBMS, bringing an unprecedented positive effect in usability and performance.
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nodb efficient query execution on raw Data files
International Conference on Management of Data, 2012Co-Authors: Ioannis Alagiannis, Renata Borovica, Miguel Branco, Stratos Idreos, Anastasia AilamakiAbstract:As Data collections become larger and larger, Data loading evolves to a major bottleneck. Many applications already avoid using Database systems, e.g., scientific Data analysis and social networks, due to the complexity and the increased Data-to-query time. For such applications Data collections keep growing fast, even on a daily basis, and we are already in the era of Data deluge where we have much more Data than what we can move, store, let alone analyze. Our contribution in this paper is the design and roadmap of a new paradigm in Database systems, called NoDB, which do not require Data loading while still maintaining the whole feature set of a modern Database system. In particular, we show how to make raw Data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern DBMS, we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive Data Type Conversion costs. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw Data files, together with a flexible caching structure. Our implementation over PostgreSQL, called PostgresRaw, is able to avoid the loading cost completely, while matching the query performance of plain PostgreSQL and even outperforming it in many cases. We conclude that NoDB systems are feasible to design and implement over modern Database architectures, bringing an unprecedented positive effect in usability and performance.
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SIGMOD Conference - NoDB: efficient query execution on raw Data files
Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 2012Co-Authors: Ioannis Alagiannis, Renata Borovica, Miguel Branco, Stratos Idreos, Anastasia AilamakiAbstract:As Data collections become larger and larger, Data loading evolves to a major bottleneck. Many applications already avoid using Database systems, e.g., scientific Data analysis and social networks, due to the complexity and the increased Data-to-query time. For such applications Data collections keep growing fast, even on a daily basis, and we are already in the era of Data deluge where we have much more Data than what we can move, store, let alone analyze. Our contribution in this paper is the design and roadmap of a new paradigm in Database systems, called NoDB, which do not require Data loading while still maintaining the whole feature set of a modern Database system. In particular, we show how to make raw Data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern DBMS, we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive Data Type Conversion costs. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw Data files, together with a flexible caching structure. Our implementation over PostgreSQL, called PostgresRaw, is able to avoid the loading cost completely, while matching the query performance of plain PostgreSQL and even outperforming it in many cases. We conclude that NoDB systems are feasible to design and implement over modern Database architectures, bringing an unprecedented positive effect in usability and performance.
Wolfgang Nebel - One of the best experts on this subject based on the ideXlab platform.
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FDL (Selected Papers) - An Advanced Simulink Verification Flow Using SystemC
Lecture Notes in Electrical Engineering, 2009Co-Authors: Kai Hylla, Jan-hendrik Oetjens, Wolfgang NebelAbstract:Functional verification is a major part of today’s system design task. Several approaches are available for verification on a high level of abstraction, where designs are often modeled using MATLAB/Simulink, as well as for RT-level verification. Different approaches are a barrier to a unified verification flow. For simulation based RT-level verification, an extended test bench concept has been developed at Robert Bosch GmbH. This chapter describes how this SystemC-based test bench concept can be applied to Simulink models. The implementation of the resulting verification flow addresses the required synchronization of both simulation environments, as well as Data Type Conversion. An example is used to evaluate the implementation and the whole verification flow. It is shown that using the extended verification flow saves a significant amount of time during development. Reusing test bench modules and test cases preserves consistency of the test bench. Verification is done automatically rather than by inspecting the waveform manually. The extended verification flow unifies system-level and RT-level verification, yielding a holistic verification flow.
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FDL - Using SystemC for an extended MATLAB/Simulink verification flow
2008 Forum on Specification Verification and Design Languages, 2008Co-Authors: Kai Hylla, Jan-hendrik Oetjens, Wolfgang NebelAbstract:Functional verification is a major part of todaypsilas system design task. Several approaches are available for verification on a high abstraction level, where designs are often modeled using MATLAB/Simulink, as well as for RT-level verification. Different approaches are a barrier to a unified verification flow. For simulation based RT-level verification, an extended test bench concept has been developed at Robert Bosch GmbH. This paper describes how this SystemC-based concept can be applied to Simulink models. The implementation of the resulting verification flow addresses the required synchronization of both simulation environments, as well as Data Type Conversion. An example is used to evaluate the implementation and the whole verification flow. It is shown that using the extended verification flow saves a significant amount of time during development. Reusing test bench modules and test cases preserves consistency of the test bench. Verification is done automatically rather than by inspecting the waveform manually. The extended verification flow unifies system-level and RT-level verification, yielding a holistic verification flow.
