The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform
Marty Sirkin - One of the best experts on this subject based on the ideXlab platform.
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SIGSOFT FSE - Reengineering a complex application using a scalable Data Structure compiler
Proceedings of the 2nd ACM SIGSOFT symposium on Foundations of software engineering - SIGSOFT '94, 1994Co-Authors: Don Batory, Jeff Thomas, Marty SirkinAbstract:P2 is a scalable compiler for collection Data Structures. High-level abstractions insulate P2 users from Data Structure implementation details. By specifying a Target Data Structure as a composition of components from a reuse library, the P2 compiler replaces abstract operations with their concrete implementations.LEAPS is a production system compiler that produces the fastest sequential executables of OPS5 rule sets. LEAPS is a hand-written, highly-tuned, performance-driven application that relies on complex Data Structures. Reengineering LEAPS using P2 was an acid test to evaluate P2's scalability, productivity benefits, and generated code performance.In this paper, we present some of our experimental results and experience in this reengineering exercise. We show that P2 scaled to this complex application, substantially increased productivity, and provided unexpected performance gains.
Xiangyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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Dimsum: Discovering semantic Data of interest from un-mappable with confidence
2012Co-Authors: Zhiqiang Lin, Junghwan Rhee, Xiangyu ZhangAbstract:Uncovering semantic Data of interest in memory pages without memory mapping information is an important capability in computer forensics. Existing memory mappingguided techniques do not work in that scenario as pointers in the un-mappable memory cannot be resolved and navigated. To address this problem, we present a probabilistic inference-based approach called DIMSUM to enable the recognition of Data Structure instances from un-mappable memory. Given a set of memory pages and the specification of a Target Data Structure, DIMSUM will identify instances of the Data Structure in those pages with quantifiable confidence. More specifically, it builds graphical models based on boolean constraints generated from the Data Structure and the memory page contents. Probabilistic inference is performed on the graphical models to generate results ranked with probabilities. Our experiments with realworld applications on both Linux and Android platforms show that DIMSUM achieves higher effectiveness than nonprobabilistic approaches without memory mapping information.
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NDSS - Discovering Semantic Data of Interest from Un-mappable Memory with Confidence.
2012Co-Authors: Zhiqiang Lin, Junghwan Rhee, Xiangyu ZhangAbstract:Uncovering semantic Data of interest in memory pages without memory mapping information is an important capability in computer forensics. Existing memory mappingguided techniques do not work in that scenario as pointers in the un-mappable memory cannot be resolved and navigated. To address this problem, we present a probabilistic inference-based approach called DIMSUM to enable the recognition of Data Structure instances from un-mappable memory. Given a set of memory pages and the specification of a Target Data Structure, DIMSUM will identify instances of the Data Structure in those pages with quantifiable confidence. More specifically, it builds graphical models based on boolean constraints generated from the Data Structure and the memory page contents. Probabilistic inference is performed on the graphical models to generate results ranked with probabilities. Our experiments with realworld applications on both Linux and Android platforms show that DIMSUM achieves higher effectiveness than nonprobabilistic approaches without memory mapping information.
Sirkinmarty - One of the best experts on this subject based on the ideXlab platform.
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Reengineering a complex application using a scalable Data Structure compiler
ACM SIGSOFT Software Engineering Notes, 1994Co-Authors: Batorydon, Thomasjeff, SirkinmartyAbstract:P2 is a scalable compiler for collection Data Structures. High-level abstractions insulate P2 users from Data Structure implementation details. By specifying a Target Data Structure as a compositio...
Don Batory - One of the best experts on this subject based on the ideXlab platform.
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SIGSOFT FSE - Reengineering a complex application using a scalable Data Structure compiler
Proceedings of the 2nd ACM SIGSOFT symposium on Foundations of software engineering - SIGSOFT '94, 1994Co-Authors: Don Batory, Jeff Thomas, Marty SirkinAbstract:P2 is a scalable compiler for collection Data Structures. High-level abstractions insulate P2 users from Data Structure implementation details. By specifying a Target Data Structure as a composition of components from a reuse library, the P2 compiler replaces abstract operations with their concrete implementations.LEAPS is a production system compiler that produces the fastest sequential executables of OPS5 rule sets. LEAPS is a hand-written, highly-tuned, performance-driven application that relies on complex Data Structures. Reengineering LEAPS using P2 was an acid test to evaluate P2's scalability, productivity benefits, and generated code performance.In this paper, we present some of our experimental results and experience in this reengineering exercise. We show that P2 scaled to this complex application, substantially increased productivity, and provided unexpected performance gains.
Zhiqiang Lin - One of the best experts on this subject based on the ideXlab platform.
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Dimsum: Discovering semantic Data of interest from un-mappable with confidence
2012Co-Authors: Zhiqiang Lin, Junghwan Rhee, Xiangyu ZhangAbstract:Uncovering semantic Data of interest in memory pages without memory mapping information is an important capability in computer forensics. Existing memory mappingguided techniques do not work in that scenario as pointers in the un-mappable memory cannot be resolved and navigated. To address this problem, we present a probabilistic inference-based approach called DIMSUM to enable the recognition of Data Structure instances from un-mappable memory. Given a set of memory pages and the specification of a Target Data Structure, DIMSUM will identify instances of the Data Structure in those pages with quantifiable confidence. More specifically, it builds graphical models based on boolean constraints generated from the Data Structure and the memory page contents. Probabilistic inference is performed on the graphical models to generate results ranked with probabilities. Our experiments with realworld applications on both Linux and Android platforms show that DIMSUM achieves higher effectiveness than nonprobabilistic approaches without memory mapping information.
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NDSS - Discovering Semantic Data of Interest from Un-mappable Memory with Confidence.
2012Co-Authors: Zhiqiang Lin, Junghwan Rhee, Xiangyu ZhangAbstract:Uncovering semantic Data of interest in memory pages without memory mapping information is an important capability in computer forensics. Existing memory mappingguided techniques do not work in that scenario as pointers in the un-mappable memory cannot be resolved and navigated. To address this problem, we present a probabilistic inference-based approach called DIMSUM to enable the recognition of Data Structure instances from un-mappable memory. Given a set of memory pages and the specification of a Target Data Structure, DIMSUM will identify instances of the Data Structure in those pages with quantifiable confidence. More specifically, it builds graphical models based on boolean constraints generated from the Data Structure and the memory page contents. Probabilistic inference is performed on the graphical models to generate results ranked with probabilities. Our experiments with realworld applications on both Linux and Android platforms show that DIMSUM achieves higher effectiveness than nonprobabilistic approaches without memory mapping information.
