The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
William R. Cook - One of the best experts on this subject based on the ideXlab platform.
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ICOODB - Unifying remote data, remote procedures, and web services
Objects and Databases, 2010Co-Authors: William R. CookAbstract:Most large-scale applications integrate remote services and/ or transactional databases. Yet building software that efficiently invokes distributed service or accesses relational databases is still quite difficult. Existing approaches to these problems are based on the Remote Procedure Call (RPC), Object-Relational Mapping (ORM), or Web Services (WS). RPCs have been generalized to support distributed object systems. ORM tools generally support a form of query sublanguage for efficient object selection, but it is not well-integrated with the host language. Web Services may seems to be a step backwards, yet document-oriented services and REST are gaining popularity. The last 20 years have produced a long litany of technologies based on these concepts, including ODBC, CORBA, DCE, DCOM, RMI, DAO, OLEDB, SQLJ, JDBC, EJB, JDO, Hibernate, XML-RPC, WSDL, Axis and LINQ. Even with these technologies, complex design patterns for service facades and/or bulk data transfers must be followed to optimize communication between client and server or client and database, leading to programs that are difficult to modify and maintain. While significant progress has been made, there is no widely accepted solution or even agreement about what the solution should look like. In this talk I present a new unified approach to invocation of distributed services and data access. The solution involves a novel control flow Construct that partitions a program block into remote and local computations, while efficiently managing the communication between them. The solution does not require proxies, an embedded query language, or Constructions/decoding of service requests. The end result is a natural unified interface to distributed services and data, which can be added to any programming language.
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ISEC - Breaking through to remote data and services
Proceedings of the 3rd India software engineering conference on India software engineering conference - ISEC '10, 2010Co-Authors: William R. CookAbstract:Most large-scale applications integrate remote services and/or transactional databases. Yet building software that efficiently invokes distributed service and accesses relational databases is still quite difficult. Existing approaches to these problems are based on the Remote Procedure Call (RPC) and Object-Relational Mapping (ORM). RPCs have been generalized to distributed object systems with remote proxies, a kind of remote object reference. ORM tools generally support a form of query sub-language for efficient object selection. The last 20 years have produced a long litany of technologies based on these concepts, including ODBC, CORBA, DCE, DCOM, RMI, DAO, OLEDB, SQLJ, JDBC, EJB, JDO, Hibernate, XML-RPC, Web Services and LINQ. Even with these technologies, complex design patterns for service facades and/or bulk data transfers must be followed to optimize communication between client and server or client and database, leading to programs that are difficult to modify and maintain. While significant progress has been made, there is no widely accepted solution or even agreement about what the solution should look like. In this talk I present a new unified approach to invocation of distributed services and data access. The solution involves a novel control flow Construct that partitions a program block into remote and local computations, while efficiently managing the communication between them. The solution does not require proxies or an embedded query language. Although the result itself is elegant and useful, what is more significant is the realization that the original problems cannot be solved using existing programming language Constructs and libraries. This work calls into question our assumption that general-purpose programming languages are truly general-purpose.
Jinrong Bai - One of the best experts on this subject based on the ideXlab platform.
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Malware detection method based on the Control-Flow Construct feature of software
Information Security, IET, 2014Co-Authors: Zongqu Zhao, Junfeng Wang, Jinrong BaiAbstract:The existing anti-virus methods extract signatures of software by manual analysis. It is inefficient when they deal with a large number of malware. Meanwhile, the limitation of unknown malware detection often is found in them too. By the research on software structure, it has been found that the control flow of software can be divided into many basic blocks by the interior cross-references, and a feature-selection approach based on this phenomenon is proposed. It can extract opcode sequences from the disassembled program, and translate them into features by vector space model. The algorithms of data mining are employed to find the classify rules from the software features, and then the rules can be applied to the malware detection. Experimental results illustrate that the proposed method can achieve the 97.0% malware detection accuracy and 3.2% false positive rate with the Random Forest classifier. Furthermore, as high as 94.5% overall accuracy can be achieved when only 5% experimental data are used as training data.
Zongqu Zhao - One of the best experts on this subject based on the ideXlab platform.
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Malware detection method based on the Control-Flow Construct feature of software
Information Security, IET, 2014Co-Authors: Zongqu Zhao, Junfeng Wang, Jinrong BaiAbstract:The existing anti-virus methods extract signatures of software by manual analysis. It is inefficient when they deal with a large number of malware. Meanwhile, the limitation of unknown malware detection often is found in them too. By the research on software structure, it has been found that the control flow of software can be divided into many basic blocks by the interior cross-references, and a feature-selection approach based on this phenomenon is proposed. It can extract opcode sequences from the disassembled program, and translate them into features by vector space model. The algorithms of data mining are employed to find the classify rules from the software features, and then the rules can be applied to the malware detection. Experimental results illustrate that the proposed method can achieve the 97.0% malware detection accuracy and 3.2% false positive rate with the Random Forest classifier. Furthermore, as high as 94.5% overall accuracy can be achieved when only 5% experimental data are used as training data.
Don Syme - One of the best experts on this subject based on the ideXlab platform.
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PADL - Joinads: a retargetable Control-Flow Construct for reactive, parallel and concurrent programming
Practical Aspects of Declarative Languages, 2011Co-Authors: Tomas Petricek, Don SymeAbstract:Modern challenges led to a design of a wide range of programming models for reactive, parallel and concurrent programming, but these are often difficult to encode in general purpose languages. We present an abstract type of computations called joinads together with a syntactic language extension that aims to make it easier to use joinads in modern functional languages. Our extension generalizes pattern matching to work on abstract computations. It keeps a familiar syntax and semantics of pattern matching making it easy to reason about code, even in a non-standard programming model. We demonstrate our extension using three important programming models - a reactive model based on events; a concurrent model based on join calculus and a parallel model using futures. All three models are implemented as libraries that benefit from our syntactic extension. This makes them easier to use and also opens space for exploring new useful programming models.
Junfeng Wang - One of the best experts on this subject based on the ideXlab platform.
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Malware detection method based on the Control-Flow Construct feature of software
Information Security, IET, 2014Co-Authors: Zongqu Zhao, Junfeng Wang, Jinrong BaiAbstract:The existing anti-virus methods extract signatures of software by manual analysis. It is inefficient when they deal with a large number of malware. Meanwhile, the limitation of unknown malware detection often is found in them too. By the research on software structure, it has been found that the control flow of software can be divided into many basic blocks by the interior cross-references, and a feature-selection approach based on this phenomenon is proposed. It can extract opcode sequences from the disassembled program, and translate them into features by vector space model. The algorithms of data mining are employed to find the classify rules from the software features, and then the rules can be applied to the malware detection. Experimental results illustrate that the proposed method can achieve the 97.0% malware detection accuracy and 3.2% false positive rate with the Random Forest classifier. Furthermore, as high as 94.5% overall accuracy can be achieved when only 5% experimental data are used as training data.