※ 引述《matt0730 (要上台讲话还真有点紧张)》之铭言：
: 政府机关，公股银行，厂商等
: 整天放砲要搞大数据，社群
: 不搞就没话题，落后，不创新
: 明明一堆行业，核心价值的是面对面的专业服务，不是所谓的虚实整合数位行销
: 长官们连ptt, mobile01, fb都没帐号
: 其实压根心里觉得年轻人的言论行为是屁
: 只会公开讲一堆重视大数据，顷听社群声音的言论，每次都还要别人拟稿
: 当然长官一定也是被长官盯 才不情愿搞这些
: 奇怪长官的长官的长官...是谁?
: 有长官脑残 长官的长官脑残 长官的...也脑残 的挂吗?
: Big Data Is Big Shit
^^^^^^^^^^^^^^^^^^^^
各位小妹、pavone、30cm、E cup、温拿、胜利组、高富帅、真强者，大家好！
打给后！胎嘎侯！AV8D！
根据本鲁的朋友表示，big data不是big shit，它是很值得研究der！只是不管本来
是做什么领域，都被要求改成做big data，才是big shit der。
本pollo从来都搞不懂什么是big data，直到朋友开示，才有略懂der感觉，以下文章供
参考，并附上中文大意：
http://cacm.acm.org/blogs/blog-cacm/155468-what-does-big-data-mean/fulltext
... big data can mean one of four things:
... big data有以下四类
Big volumes of data, but "small analytics." Here the idea is to support SQL
on very large data sets. Nobody runs "Select*" from something big as this
would overwhelm the recipient with terabytes of data. Instead, the focus is
on running SQL analytics (count, sum, max, min, and avg with an optional
group_by) on large amounts of data. I term this "small analytics" to
distinguish this use case from the one which follows.
第一种是大量资料配上小量分析，也就是要在大量资料上支援SQL（数据库中
的查询语言）的查询，例如求总和、最大值、最小值、某部分当中的平均等。
Big analytics on big volumes of data. By big analytics, I mean data
clustering, regressions, machine learning, and other much more complex
analytics on very large amounts of data. At the present time users tend to
run big analytics using statistical packages, such as R, SPSS and SAS.
Alternately, they use linear algebra packages such as ScalaPack or Arpack.
Lastly, there is a fair amount of custom code (roll your own) used here.
大二种是大量资料配上大量分析，也就是要在大量资料上进行资料分群、回归
、各种机器学习、跑统计软件等。
Big velocity. By this I mean being able to absorb and process a fire hose of
incoming data for applications like electronic trading, real-time ad
placement on Web pages, real-time customer targeting, and mobile social
networking. This use case is most prevalent in large Web properties and on
Wall Street, both of whom tend to roll their own.
第三种是处理快速灌进来的资料，最好能即时处理。
Big variety. Many enterprises are faced with integrating a larger and larger
number of data sources with diverse data (spreadsheets, Web sources, XML,
traditional DBMSs). Many enterprises view this as their number one headache.
Historically, the extract, transform, and load (ETL) vendors serviced this
market on modest numbers of data sources.
第四种是指资料来源或种类多样，很多企业对此十分头痛。
In summary, big data can mean big volume, big velocity, or big variety. In
the remainder of this post, I talk about small analytics on big volumes of
data. In three subsequent posts, I will discuss the other three problem
areas.
以下进入重点，但本鲁的朋友还没告诉本鲁这在讲什么，所以以下省略。
本鲁的朋友承认他是因为这篇文章的作者是2014年Turing award得主，才好奇点进去
看。本鲁绝不承认本鲁是本鲁的朋友。