Analytic database technologies for a new kind of user: the data enthusiast
Analytic database technologies for a new kind of user: the data enthusiast
复制标题
面向新型用户的分析数据库技术：数据爱好者
DOI：
10.1145/2213836.2213902
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
P. Hanrahan
中科院分区：
文献类型：
--
作者：
P. Hanrahan
分析使企业能够提高其活动的效率，并最终提高其盈利能力。因此，它是数据库行业增长最快的部分之一。分析这个词有两种用法。第一个是指一组算法和技术，灵感来自数据挖掘，计算统计和机器学习，用于支持统计推断和预测。第二点同样重要：分析思维。分析思维是一种基于事实和数据进行推理和决策的结构化方法。数据库社区最近的大部分工作都集中在第一个问题上，即算法和系统问题。这些进步背后的人包括新一代的数据科学家，他们要么拥有开发高级统计模型的数学技能，要么拥有开发或实施可扩展系统以处理大型复杂数据集的计算机技能。分析的第二个方面--支持分析型思考者--虽然同样重要和具有挑战性，但受到的关注要少得多。在这次演讲中，我将描述最近在使更广泛的人（我称之为数据爱好者）可以使用这两种形式的分析方面取得的进展。数据爱好者是受过良好教育的人，他们相信数据可以用来回答问题或解决问题。这些人不是数学家或程序员，只知道一点统计学。我将回顾最近在构建易于使用但功能强大的可视化界面以处理数据方面的工作;以及支持这些界面所需的分析数据库技术。
Analytics enables businesses to increase the efficiency of their activities and ultimately increase their profitability. As a result, it is one of the fastest growing segments of the database industry. There are two usages of the word analytics. The first refers to a set of algorithms and technologies, inspired by data mining, computational statistics, and machine learning, for supporting statistical inference and prediction. The second is equally important: analytical thinking. Analytical thinking is a structured approach to reasoning and decision making based on facts and data. Most of the recent work in the database community has focused on the first, the algorithmic and systems problems. The people behind these advances comprise a new generation of data scientists who have either the mathematical skills to develop advanced statistical models, or the computer skills to develop or implement scalable systems for processing large, complex datasets. The second aspect of analytics -- supporting the analytical thinker -- although equally important and challenging, has received much less attention. In this talk, I will describe recent advances in in making both forms of analytics accessible to a broader range of people, who I call data enthusiasts. A data enthusiast is an educated person who believes that data can be used to answer a question or solve a problem. These people are not mathematicians or programmers, and only know a bit of statistics. I'll review recent work on building easy-to-use, yet powerful, visual interfaces for working with data; and the analytical database technology needed to support these interfaces.