Database Reference
In-Depth Information
winter months of December and January are generally drier than those months in Rio de Janeiro
(or for that matter, in Seattle).
This small-scale, lighthearted analogy hints at the useful insights possible when certain types
of data are recorded as a time series—as measurements or observations of events as a func-
tion of the time at which they occurred. The variety of situations in which time series are
useful is wide ranging and growing, especially as new technologies are producing more data
of this type and as new tools are making it feasible to make use of time series data at large
scale and in novel applications. As we alluded to at the start, recording the exact time at
which a critical parameter was measured or a particular event occurred can have a big impact
on some very serious situations such as safety and risk reduction. The airline industry is one
such example.
Recording the time at which a measurement was made can greatly expand the value of the
data being collected. We have all heard of the flight data recorders used in airplane travel as a
way to reconstruct events after a malfunction or crash. Oddly enough, the public sometimes
calls them “black boxes,” although they are generally painted a bright color such as orange.
A modern aircraft is equipped with sensors to measure and report data many times per
second for dozens of parameters throughout the flight. These measurements include altitude,
flight path, engine temperature and power, indicated air speed, fuel consumption, and control
settings. Each measurement includes the time it was made. In the event of a crash or serious
accident, the events and actions leading up to the crash can be reconstructed in exquisite de-
tail from these data.
Flight sensor data is not only used to reconstruct events that precede a malfunction. Some of
this sensor data is transferred to other systems for analysis of specific aspects of flight per-
formance in order for the airline company to optimize operations and maintain safety stand-
ards and for the equipment manufacturers to track the behavior of specific components along
with their microenvironment, such as vibration, temperature, or pressure. Analysis of these
time series datasets can provide valuable insights that include how to improve fuel consump-
tion, change recommended procedures to reduce risk, and how best to schedule maintenance
and equipment replacement. Because the time of each measurement is recorded accurately,
it's possible to correlate many different conditions and events.
Figure 1-2
displays time series
data, the altitude data from flight data systems of a number of aircraft taking off from San
Jose, California.
