1,146 research outputs found

    The Mondrian Data Engine

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    The increasing demand for extracting value out of ever-growing data poses an ongoing challenge to system designers, a task only made trickier by the end of Dennard scaling. As the performance density of traditional CPU-centric architectures stagnates, advancing compute capabilities necessitates novel architectural approaches. Near-memory processing (NMP) architectures are reemerging as promising candidates to improve computing efficiency through tight coupling of logic and memory. NMP architectures are especially fitting for data analytics, as they provide immense bandwidth to memory-resident data and dramatically reduce data movement, the main source of energy consumption. Modern data analytics operators are optimized for CPU execution and hence rely on large caches and employ random memory accesses. In the context of NMP, such random accesses result in wasteful DRAM row buffer activations that account for a significant fraction of the total memory access energy. In addition, utilizing NMP’s ample bandwidth with fine-grained random accesses requires complex hardware that cannot be accommodated under NMP’s tight area and power constraints. Our thesis is that efficient NMP calls for an algorithm-hardware co-design that favors algorithms with sequential accesses to enable simple hardware that accesses memory in streams. We introduce an instance of such a co-designed NMP architecture for data analytics, the Mondrian Data Engine. Compared to a CPU-centric and a baseline NMP system, the Mondrian Data Engine improves the performance of basic data analytics operators by up to 49× and 5×, and efficiency by up to 28× and 5×, respectively

    A Comparison of Leading Database Storage Engines in Support of Online Analytical Processing in an Open Source Environment

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    Online Analytical Processing (OLAP) has become the de facto data analysis technology used in modern decision support systems. It has experienced tremendous growth, and is among the top priorities for enterprises. Open source systems have become an effective alternative to proprietary systems in terms of cost and function. The purpose of the study was to investigate the performance of two leading database storage engines in an open source OLAP environment. Despite recent upgrades in performance features for the InnoDB database engine, the MyISAM database engine is shown to outperform the InnoDB database engine under a standard benchmark. This result was demonstrated in tests that included concurrent user sessions as well as asynchronous user sessions using data sets ranging from 6GB to 12GB. Although MyISAM outperformed InnoDB in all test performed, InnoDB provides ACID compliant transaction technologies are beneficial in a hybrid OLAP/OLTP system

    PLOMO Associate Team Final Report

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    The goal of Plomo is to develop new meta tools to improve and bring synergy in the existing infrastructure of Pharo1 (for software development) and the Moose software analysis platform2 (for maintenance). PLOMO will (i) enhance the Opal open compiler infrastructure to support plugin definition, (ii) offer an infrastructure for change and event tracking as well as models to compose and manipulate them, (iii) work on a layered library of algorithms for the Mondrian visualization engine of Moose, (iv) work on new ways of profiling applications. All the efforts will be performed on Pharo and Moose, two platforms heavily used by the RMoD and Pleiad teams

    Pentaho Sebagai Solusi Masalah Pengolahan Database

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    Information is the result of data processing plays an important role in anorganization, especially in decision-making process. Pentaho application of Intelligent Business Products is one of the technologies for collecting, storing, analyzing, and providing access to data to help enterprise users make better business decisions. Pentaho has a function as reporting, analysis, dashboards, data integration (ETL) and data mining. Pentaho is better to manage large and complex data and be able to complete the functional organization

    Hume's Legacy: A Cognitive Science Perspective

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    Hume is an experimental philosopher who attempts to understand why we think, feel, and act as we do. But how should we evaluate the adequacy of his proposals? This chapter examines Hume’s account from the perspective of interdisciplinary work in cognitive science

    Interactive Graphics: Exemplified with Real Data Applications

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    Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively. Static plots do not allow interacting with graphics. User interaction, on the other hand, is crucial in exploring data. It gives flexibility and control. One can experiment with the data and the displays. One can investigate the data from different perspectives to produce views that are easily interpretable and informative. In this paper, we try to explain interactive graphics and advocate their use as a practical tool. The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets
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