The proceedings contain 32 papers. The topics discussed include: describing component behavior in a distributed system;evaluating software security risks using fuzzy rule based expert system;weakest termination pre-co...
ISBN:
(纸本)9781880843864
The proceedings contain 32 papers. The topics discussed include: describing component behavior in a distributed system;evaluating software security risks using fuzzy rule based expert system;weakest termination pre-conditions for two categories of loop programs;verification of parallel asynchronous load scheduling protocols using domain specific language approach;a model transformation approach towards refactoring use case models based on antipatterns;deduplication algorithms for databases and data warehouses;unifying data relations to enable end-users to navigate over relational data;on consolidating many tenants into a shared B-tree index;tasks scheduling on multiprocessing systems;implementation of data privacy and security in an online student health records system;interplay of logical verification and performance testing in software assurance;and towards forensic readiness and homogeneity of operating system logs.
The advent of affordable, shared-nothing computing systems portends a new class of parallel database management systems (DBMS) for on-line transaction processing (OLTP) applications that scale without sacrificing ACID...
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With the rapid growth of emerging applications like social network analysis, semantic Web analysis and bioinformatics network analysis, a variety of data to be processed continues to witness a quick increase. Effectiv...
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ISBN:
(纸本)9780769549309
With the rapid growth of emerging applications like social network analysis, semantic Web analysis and bioinformatics network analysis, a variety of data to be processed continues to witness a quick increase. Effective management and analysis of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing technics from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including cloud computing platform, cloud architecture, cloud database and data storage scheme. Following the MapReduce parallel processing framework, we then introduce MapReduce optimization strategies and applications reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.
In software engineering, design patterns are commonly used and represent robust solution templates to frequently occurring problems in software design and implementation. In this paper, we consider performance simulat...
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Routine operations of emergency first responders are usually well managed. The situation is different for mass casualty emergencies where more people and properties are threatened. In such situations there are no pred...
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The Hydra project offers version control for distributed case files in alpha-Flow. Available version control systems lack support for independent versioning of multiple logical units within a single repository each wi...
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The Hydra project offers version control for distributed case files in alpha-Flow. Available version control systems lack support for independent versioning of multiple logical units within a single repository each with its own version history and head. Our use case also requires mechanisms for labeling versions by their validity and for validity-based navigational access. Hydra is a multi-module and validity-aware version control system.
The exponential growth in user and application data entails new means for providing fault tolerance and protection against data loss. High Performance Computing (HPC) storage systems, which are at the forefront of han...
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Over the past 15 years, data warehousing and OLAP technologies have matured to the point whereby they have become a cornerstone for the decision making process in organizations of all sizes. With the underlying databa...
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ISBN:
(纸本)9781467309752
Over the past 15 years, data warehousing and OLAP technologies have matured to the point whereby they have become a cornerstone for the decision making process in organizations of all sizes. With the underlying databases growing enormously in size, parallel DBM systems have become a popular target platform. Perhaps the most ``obvious'' approach to scalable warehousing is to combine a small collection of conventional relational DBMSs into a loosely connected parallel DBMS. Such systems, however, benefit little, if at all, from advances in OLAP indexing, storage, compression, modeling, or query optimization. In the current paper, we discuss a parallel analytics server that has been designed from the ground up as a high performance OLAP query engine. Moreover, its indexing and query processing model directly exploits an OLAP-specific algebra that enables performance optimizations beyond the reach of simple relational DBMS clusters. Taken together, the server provides class-leading query performance with the scalability of shared nothing databases and, perhaps most importantly, achieves this balance with a modest physical architecture.
Many transient simulations spend a significant portion of the overall runtime solving a linear system. A wide variety of preconditioned linear solvers have been developed to quickly and accurately solve different type...
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ISBN:
(纸本)9781467309745
Many transient simulations spend a significant portion of the overall runtime solving a linear system. A wide variety of preconditioned linear solvers have been developed to quickly and accurately solve different types of linear systems, each having options to customize the preconditioned solver for a given linear system. Transient simulations may produce significantly different linear systems as the simulation progresses due to special events occurring that make the linear systems more difficult to solve or the model moving closer to a state of equilibrium where the linear systems are easier to solve. Machine learning algorithms provide the ability to dynamically select the preconditioned linear solver for each linear system produced by a simulation. We can generate databases by computing attributes for each linear system, physical attributes for the transient simulation, computational attributes, and running times for a set of preconditioned solvers on each linear system. Machine learning algorithms can then use these databases to generate classifiers capable of dynamically selecting a preconditioned solver for each linear system given a set of attributes. This allows us to quickly and accurately compute each transient simulation using different preconditioned solvers throughout the simulation. This also provides the potential to produce speedups in comparison with using a single preconditioned solver for an entire transient simulation.
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