Collaborative Research: CRI - Scalable Benchmarks, Software and Data for Data Mining, Analytics and Scientific Discoveries

这个协作项目开发了一套广泛的数据挖掘基准，为重要的数据挖掘内核定义了基准数据集和高效算法，为数据挖掘应用程序建立了一套全面的基准测试套件。总的来说，使用数据挖掘算法的应用程序现在形成了足够大的百分比，以保证对数据挖掘基准的开发进行研究，该基准可用于评估新的处理器架构，并在测试新的数据挖掘算法时进行比较。为应用程序开发基准，测试套件和数据集，可用于驱动从处理器到应用程序级别的系统的设计，实现和增长，该项目迈出了初步的，重要的一步，具体追求以下目标：-开发一个基准套件，用于了解高性能数据挖掘的瓶颈，并指导下一代处理器的开发；设计可以在现有和未来的处理器上有效执行的数据挖掘内核。基准测试在推进体系结构、软件可伸缩性、网络和其他IT规程方面发挥着重要作用。它们不仅在度量不同系统的相对性能方面发挥作用，而且还在质量、可伸缩性、成本、执行时间和其他度量方面帮助研究和开发应用程序的体系结构。为数据访问和使用建立基准和附带的工具，对套件中的应用程序执行详细分析，并开发一个测试平台来执行这些分析，这项工作提供了一个社区资源，可以帮助设计评估、比较和改进处理器体系结构、算法和可伸缩系统。更广泛的影响：虽然提供了一种评估和比较算法、应用程序、设计和产品的标准化方法，但该项目的结果有可能直接影响各个领域的进步，包括数据挖掘算法和应用程序、更新的体系结构和数据密集型计算的系统设计。该项目为解决数据密集型计算的新行业领域的发展开辟了道路，类似于媒体、网络和信号处理应用的结果。此外，该资源通过向社区提供可在课堂上使用的软件、工具和数据来促进教育。

This collaborative project, developing a broad suite of data mining benchmarks, defines benchmark data sets and efficient algorithms for important data mining kernels establishing a comprehensive benchmark suite for data mining applications. Overall, applications using data mining algorithms now form a large enough percentage to warrant research into the development of a data mining benchmark that can be used to evaluate new processor architecture and serve for comparison in testing new data mining algorithms. Taking an initial, and significant step towards developing benchmarks, test suites and datasets for applications which can be used to drive the design, implementation, and growth of systems from processor to application levels, the project specifically pursues the following goals:-Develop a benchmarking suite that will be used to understand the bottlenecks in high performance data mining and guide in the development of next-generation processors, and-Devise data mining kernels that can be efficiently executed on existing and future processors.Benchmarks play a major role in advancing architectures, software scalability, networks, and other IT disciplines. They not only play a role in measuring the relative performance of different systems, but also aid in the research and development of architectures to applications in terms of quality, scalability, cost, execution time, and other measures. Establishing a benchmark and accompanying tools for data access and usage, performing a detailed analysis of applications in the suite, and developing a testbed to perform these analyses, the work contributes a community resource that can help in design evaluation, comparison, and improvement for processor architecture, algorithms, and scalable systems.Broader Impact: While providing a standardize way of evaluating and comparing algorithms, applications, designs, and products, the results from this project have the potential to directly impact the advancement of various fields including data mining algorithms and applications, newer architectures, and system design for data intensive computing. The project opens the way to the development of a new industry segment addressing data intensive computing, similar to what resulted from media, networking, and signal processing applications. Moreover, the resource contributes to education by providing the community with software, tools, and data that can be used in the classroom.