Database Reference
In-Depth Information
Similarly, it would be possible to equip commercial waste containers, called dumpsters in the
US, with sensors to report on how full they are at different points in time. Why not just peek
into the dumpster to see if it needs to be emptied? That might be sufficient if it's just a case
of following the life of one dumpster, but waste-management companies in large cities must
consider what is happening with hundreds of thousands of dumpsters. For shared housing
such as apartments or condominiums, some cities recommend providing one dumpster for
every four families, and there are dumpsters at commercial establishments such as restaur-
ants, service stations, and shops. Periodically, the number of dumpsters at particular locations
changes, such as in the case of construction sites. Seasonal fluctuations occur for both resid-
ential and commercial waste containers—think of the extra levels of trash after holidays for
example.
Keeping a history of the rate of fill for individual dumpsters (a time series) can be useful in
scheduling pickup routes for the large waste-management trucks that empty dumpsters. This
level of management not only could improve customer service, but it also could result in fuel
savings by optimizing the pattern for truck operations.
Manufacturing is another sector in which time series data from sensor measurements is ex-
tremely valuable. Quality control is a matter of constant concern in manufacturing as much
today as it was in the past.
“Uncontrolled variation is the enemy of quality.”
— Attributed to Edward Deming—engineer and management guru in the
late 20th century
In the quest for controlling variation, it's a natural fit to take advantage of new capabilities to
collect many sensor measurements from the equipment used in manufacturing and store them
in a time series database. The exact range of movement for a mechanical arm, the temperat-
ure of an extrusion tip for a polymer flow, vibrations in an engine—the variety of measure-
ments is very broad in this use case. One of the many goals for saving this data as a time
series is to be able to correlate conditions precisely to the quality of the product being made
at specific points in time.
Talking to Towers: Time Series and Telecom
Mobile cell phone usage is now ubiquitous globally, and usage levels are increasing. In many
parts of the world, for example, there's a growing dependency on mobile phones for financial
transactions that take place constantly. While overall usage is increasing, there are big vari-
ations in the traffic loads on networks depending on residential population densities at differ-
