A Fast and Incremental Development Life Cycle for Data Analytics as a Service

2018 IEEE International Congress on Big Data (BigData Congress) Pub Date : 2018-07-01 DOI:10.1109/BigDataCongress.2018.00030

C. Ardagna, V. Bellandi, P. Ceravolo, E. Damiani, B. D. Martino, Salvatore D'Angelo, A. Esposito

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引用次数: 3

Abstract

Big Data does not only refer to a huge amount of diverse and heterogeneous data. It also points to the management of procedures, technologies, and competencies associated with the analysis of such data, with the aim of supporting high-quality decision making. There are, however, several obstacles to the effective management of a Big Data computation, such as data velocity, variety, and veracity, and technological complexity, which represent the main barriers towards the full adoption of the Big Data paradigm. The goal of this work is to define a new software Development Life Cycle for the design and implementation of a Big Data computation. Our proposal integrates two model-driven methods: a first method based on pre-configured services that reduces the cost of deployment and a second method based on custom component development that provides an incremental process of refinement and customization. The proposal is experimentally evaluated by clustering a data set of the distribution of the population in the United States based on contextual criteria.

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数据分析即服务的快速增量开发生命周期

大数据不仅仅指数量庞大、种类繁多、异构的数据。它还指出了与这些数据分析相关的程序、技术和能力的管理，目的是支持高质量的决策制定。然而，对大数据计算的有效管理存在一些障碍，例如数据速度、多样性和准确性以及技术复杂性，这些都是全面采用大数据范式的主要障碍。这项工作的目标是为设计和实现大数据计算定义一个新的软件开发生命周期。我们的建议集成了两种模型驱动的方法:第一种方法基于预配置的服务，减少了部署成本;第二种方法基于定制组件开发，提供了细化和定制的增量过程。该建议是通过基于上下文标准的美国人口分布的数据集聚类实验评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 IEEE International Congress on Big Data (BigData Congress)

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