Database Reference
In-Depth Information
time as an
absolute reference
: your connecting flight leaves at 10:42 am, your meeting be-
gins at 1:00 pm, etc. As you travel, time may also represent a
sequence
. Those people who
arrive earlier than you in the taxi line are in front of you and catch a cab while you are still
waiting.
Time as interval, as an ordering principle for a sequence, as absolute reference—all of these
ways of thinking about time can also be useful in different contexts. Data collected as a time
series is likely more useful than a single measurement when you are concerned with the ab-
solute time at which a thing occurred or with the order in which particular events happened
or with determining rates of change. But note that time series data tells you
when something
happened
, not necessarily
when you learned about it
, because data may be recorded long
after it is measured. (To tell when you knew certain information, you would need a bi-tem-
poral database, which is beyond the scope of this topic.) With time series data, not only can
you determine the sequence in which events happened, you also can correlate different types
of events or conditions that co-occur. You might want to know the temperature and vibra-
tions in a piece of equipment on an airplane as well as the setting of specific controls at the
time the measurements were made. By correlating different time series, you may be able to
determine how these conditions correspond.
The basis of a time series is the repeated measurement of parameters over time together with
the times at which the measurements were made.
Time series often consist of measurements
made at regular intervals, but the regularity of time intervals between measurements is not a
requirement. Also, the data collected is very commonly a number, but again, that is not es-
sential. Time series datasets are typically used in situations in which measurements, once
made, are not revised or updated, but rather, where the mass of measurements accumulates,
with new data added for each parameter being measured at each new time point. These char-
acteristics of time series limit the demands we put on the technology we use to store time
series and thus affect how we design that technology. Although some approaches for how
best to store, access, and analyze this type of data are relatively new, the idea of time series
data is actually quite an old one.
Time Series Data Is an Old Idea
It may surprise you to know that one of the great examples of the advantages to be reaped
from collecting data as a time series—and doing it as a crowdsourced, open source, big data
project—comes from the mid-19th century. The story starts with a sailor named Matthew
Fontaine Maury, who came to be known as the Pathfinder of the Seas. When a leg injury
forced him to quit ocean voyages in his thirties, he turned to scientific research in meteoro-
