The point is that in fast moving markets, one needs something a little better than simple historical moving averages of daily closing prices. This is better, and extending the idea of 'volume time' vs. 'chronological time' is an intriguing direction. But one can also look at bid-ask spreads directly, or the VIX futures, or its etf, the VXX, and combinations, to gauge intraday volatility as well. Further, one can better estimate 'buy volume' using the transaction price relative to the then extant bid-ask spread, rather than if the price was weakly increasing, though this then involves syncing the trade information with quote information, and for academics such data are often hard to come by (further, quote information is often 10 times as large).
Classic database systems offer crisp answers for a relatively small amount of data. These systems hold their data in one or a relatively small number of computers. With a tightly defined schema and transactional consistency, the results returned from queries are crisp and accurate.
New systems have humongous amounts of data content, change rates, and querying rates and take lots of computers to hold and process. The data quality and meaning are fuzzy. The schema, if present, is likely to vary across the data. The origin of the data may be suspect, and its staleness may vary.
Today's data systems coalesce data from many sources. The Internet, B2B, and enterprise application integration (EAI) combine data from different places. No computer is an island. This large amount of interconnectivity and interdependency has led to a relaxation of many database principles. Let's consider the ways in which today's answers differ from what we used to expect.
links for 2011-05-27