Database Reference
In-Depth Information
ent times of the day, on temporary crowds, and on special events that encourage phone use.
Some of these special events are scheduled, such as the individual matches during the World
Cup competition. Other special events that result in a spike in cell phone usage are not sched-
uled. These include earthquakes and fires or sudden political upheavals. Life events happen,
and people use their phones to investigate or comment on them.
All of these situations that mean an increase in business are great news for telecommunica-
tion companies, but they also present some huge challenges in maintaining good customer
service through reliable performance of the mobile networks. When in use, each mobile
phone is constantly “talking” to the nearest cell phone tower, sending and receiving data.
Now multiply that level of data exchange by the millions of phones in use, and you begin to
see the size of the problem. Monitoring the data rates to and from cell towers is important in
being able to recognize what constitutes a normal pattern of usage versus unusual fluctu-
ations that could impair quality of service for some customers trying to share a tower. A situ-
ation that could cause this type of surge in cell phone traffic is shown in the illustration in
Figure 2-3
. A temporary influx of extra cell phone usage at key points during a sports event
could overwhelm a network and cause poor connectivity for regular residential or commer-
cial customers in the neighborhood. To accommodate this short-term swell in traffic, the tele-
com provider may be able to activate mini-towers installed near the stadium to handle the ex-
tra load. This activation can take time, and it is likely not cost-effective to use these micro-
towers at low-traffic loads. Careful monitoring of the moment-to-moment patterns of usage
is the basis for developing adaptive systems that respond appropriately to changes.
In order to monitor usage patterns, consider the traffic for each small geographical region
nearby to a cell tower to be a separate time series. There are strong correlations between dif-
ferent time series during normal operation and specific patterns of correlation that arise dur-
ing these flash crowd events that can be used to provide early warning. Not surprisingly, this
analysis requires some pretty heavy time series lifting.
