Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
CA 02744580 2011-06-27
METHODS AND APPARATUS TO OBTAIN ANONYMOUS AUDIENCE MEASUREMENT
DATA FROM NETWORK SERVER DATA FOR PARTICULAR DEMOGRAPHIC AND
USAGE PROFILES
FIELD OF THE DISCLOSURE
[0001] This disclosure relates generally to audience measurement and, more
particularly, to methods and apparatus to obtain anonymous audience
measurement data from
network server data for particular demographic and usage profiles.
BACKGROUND
[0002] Media content is provided to audiences using a variety of non-
traditional
techniques, such as via the Internet and various mobile telephone networks.
Accordingly,
content providers and advertisers are eager to extend audience measurement of
media content
consumption beyond traditional broadcast television markets. However,
conventional panel-
based techniques for audience measurement in traditional television markets
can be expensive to
implement due to challenges encountered in recruiting a panel that yields a
representative sample
of the desired demographic profile. Additionally, because such panels
typically include only a
small subset of all audience members, the conventional panel-based techniques
often do not
capture content accessed by relatively few audience members (e.g., such as
niche content).
Although allowing an audience measurement entity to access gateway and other
network server
logs tracking data traffic (including access to media content), as well as
customer relationship
databases storing customer data that may be used to determine customer
demographics, would
avoid requiring a panel, such access is generally not feasible due to privacy
concerns.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. I is block diagram of an example environment of use in which an
example representative sampling unit can obtain anonymous audience measurement
data from
network server data for particular demographic and usage profiles.
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[00041 FIG. 2 is a block diagram of an example implementation of the
representative
sampling unit of FIG. 1.
[00051 FIG. 3 illustrates an example demographic profile and an example usage
profile that may be processed by the representative sampling unit of FIGS. I
and/or 2.
[00061 FIG. 4 is a flowchart representative of example machine readable
instructions
that may be executed to implement the representative sampling unit of FIGS. 1
and/or 2.
[00071 FIG. 5 is a flowchart representative of example machine readable
instructions
that may be used to implement the example machine readable instructions of
FIG. 4 and/or
executed to perform a customer sample generation process to implement the
representative
sampling unit of FIGS. I and/or 2.
[00081 FIG. 6 is a flowchart representative of example machine readable
instructions
that may be used to implement the example machine readable instructions of
FIG. 4 and/or
executed to perform a measurement data sampling process to implement the
representative
sampling unit of FIGS. I and/or 2.
[00091 FIG. 7 is a block diagram of an example processing system that may
execute
the example machine readable instructions of FIGS. 4-6 to implement the
representative
sampling unit of FIGS. I and/or 2, and/or the example environment of use of
FIG. 1.
DETAILED DESCRIPTION
[00101 Methods and apparatus to obtain anonymous audience measurement data
from
network server data for particular demographic and usage profiles are
disclosed herein.
Although the following discloses example methods and apparatus including,
among other
components, software executed on hardware, it should be noted that such
methods and apparatus
are merely illustrative and should not be considered as limiting. For example,
it is contemplated
that any or all of these hardware and software components could be implemented
exclusively in
hardware, exclusively in software, exclusively in firmware, or in any
combination of hardware,
software, and/or firmware. Additionally, though described in connection with
example
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implementations using mobile/wireless networks, access points and other
network structures and
devices, the example methods and apparatus described herein are not limited
thereto.
Accordingly, while the following describes example methods and apparatus,
persons having
ordinary skill in the art will readily appreciate that the examples provided
are not the only way to
implement such methods and apparatus.
[0011] As used herein, the term customer generally refers to any person or
entity able
to consume media content provided by any provider, source, technology, etc. As
such, a
customer can be an audience member, a subscriber, a user, a viewer, a
listener, etc. Furthermore,
a customer as referred to herein is not limited to a paying customer and
includes a customer able
to access content without any exchange of payment or without having any other
relationship with
the provider of the content.
[0012] In an example disclosed technique to provide anonymous audience
measurement data to an audience measurement entity, an example representative
sampling unit is
included in a service provider's network and obtains a demographic profile and
a network usage
profile from the audience measurement entity, which is separate from the
service provider. In an
example implementation, the demographic profile includes a set of demographic
categories, with
each demographic category including a set of target segments associated
respectively with a set
of target population percentages. Similarly, the network usage profile in such
an example
implementation includes a set of usage categories associated respectively with
another set of
target population percentages.
[0013] Given the demographic profile and the network usage profile specified
by the
audience measurement entity, the representative sampling unit then samples
customer data stored
in a customer database not accessible by the audience measurement entity to
generate, without
customer intervention, a customer sample representative of the demographic
profile and the
network usage profile. In an example implementation, the generated customer
sample includes
customer identification information, such as phone numbers, Internet protocol
(IP) addresses,
usernames, personal identification numbers (PINs), cookie identifiers, etc.,
as well as other
demographic information, for a subset of customers representative of the
demographic profile
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and the network usage profile. Using the customer identification information
included in the
generated customer sample, the representative sampling unit is able to
retrieve and process log
data from a network server, such as a gateway or other network server, not
accessible by the
audience measurement entity to determine audience measurement data associated
with customers
in the customer sample. To render the audience measurement data anonymous, the
representative sampling unit scrubs the audience measurement data to remove
any customer
identification information (e.g., and to replace such removed information with
anonymous
identifiers incapable of identifying particular customers) before providing
the data to the
audience measurement entity. However, the anonymous measurement data retains
other
demographic information to enable classification of the data according to the
specified
demographic and network usage profiles.
100141 Unlike many conventional audience measurement techniques, the example
anonymous server sampling techniques described herein do not utilize
customer/audience panels.
Instead, the example techniques described herein determine anonymous audience
measurement
data directly from (1) a service provider's customer relationship database(s)
storing customer
information records/data that include identification and demographic data, and
(2) the service
provider's network server logs that track data traffic/events associated with,
for example, media
server and/or media content access. Additionally, the anonymous audience
measurement data is
determined by the representative sampling unit to be representative of
demographic and usage
profiles initially specified by an audience measurement entity, unlike many
conventional
techniques in which the demographic composition is unknown until after the
measurement data
is processed. Furthermore, in the disclosed example anonymous server sampling
techniques, the
audience measurement entity is separate from the service provider, in contrast
with other
measurement techniques in which the service provider also acts as the
measurement entity.
However, because the audience measurement data is anonymous when exported to
the audience
measurement entity, privacy is maintained despite the fact that the audience
measurement entity
is separate from the service provider.
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[0015] Turning to the figures, a block diagram of an example environment of
use 100
in which an example representative sampling unit 105 may obtain anonymous
audience
measurement data from network server data for particular (e.g., specified)
demographic and
usage profiles is illustrated in FIG. 1. The environment of use 1 00 includes
an example provider
network 110 operated by a service provider to provide media content and other
services to one or
more customers (not shown). The provider network 110 can be implemented by any
type of
service provider, such as, for example, a mobile communications service
provider, an Internet
service provider, a cable television service provider, a satellite television
service provider, a
satellite radio service provider, etc.
[0016] The provider network 110 includes one or more example customer
databases
115 storing customer records containing customer data associated with
customers of the service
provider. A customer database 115 can correspond to, for example, a customer
relationship
management (CRM) database, a RADIUS server, etc., or any other type of
database or server
storing customer-related information to enable the service provider to provide
media content and
other communication services to its customers. In examples in which the
provider network 110
includes multiple customer databases 115, some or all of the multiple customer
databases 115
may be co-located or reside in separate locations. In the illustrated example,
the customer data
stored in the customer records of the customer database(s) 115 includes
customer identification
and other demographic information. Examples of the customer identification
information stored
in the customer database(s) 115 can include, but is not limited, customer
device identification
information, such as any, some or all of phone numbers of mobile and/or other
phones used by
customers to access the provider network 110, IP addresses, medium access
control (MAC)
addresses and/or other device identifying information for customer devices
used to access the
provider network 110, etc. Customer identification information can also
include personal
identification information, such as any, some or all of customer names,
addresses, identification
numbers, account numbers, etc. Examples of other demographic information
stored in the
customer database(s) 115 can include, but is not limited, information
regarding any, some or all
of a customer's age, ethnicity, income, education, etc., (e.g., provided
voluntarily by customers
in applications for service, in response to one or more customer surveys,
etc.) as well as
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information concerning services, products, subscriptions, etc., purchased by
the customer from
the service provider.
[00171 The provider network 110 also includes one or more example networks
servers
120 to route and otherwise process data traffic within the provider network
110. A network
server 120 can correspond to, for example, a gateway, such as a wireless
access point (WAP)
gateway, a router, a customer access server (CAS), an IP probe, a proxy
server, a content
adaptation server, etc. In examples in which the provider network 110 includes
multiple
networks servers 120, some or all of the multiple networks servers 120 may be
co-located or
reside in separate locations. Additionally, the networks server(s) 120 may be
co-located with or
reside in locations separate from the customer database(s) 115. The network
server(s) 120
maintain server logs that track data traffic and other network events
associated with customer use
of the provider network 1 10. For example, the server logs may track the
addresses of particular
media content and/or other content servers, hosts, etc., accessed by customer
devices, the names
of particular media or other content accessed, the times when the
servers/hosts and/or the content
was accessed, etc. Additionally, the server log data is indexed by customer
device identification
information (e.g., such as device phone numbers, IP addresses, etc.) to enable
association of data
traffic and network events with particular customer devices and, thus,
particular customers.
[00181 The provider network 110 is a secure and private network protected by
an
example firewall 125, which may be implemented by any type of firewall device
or application.
Because the provider network is secure and private, the representative
sampling unit 105 is
included in the provider network 110 to allow an audience measurement entity
that is separate
from the service provider to obtain audience measurement data derived from the
customer data
stored in the customer database(s) 115 and the server logs stored by the
network server(s) 120,
even though the customer database(s) 115 and the network server(s) 120 are
inaccessible by the
audience measurement entity. Furthermore, to maintain customer privacy, the
audience
measurement data provided by the representative sampling unit 105 to the
audience measurement
entity is anonymous and, thus, does not contain personal identification
information, but can
include other demographic information.
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[00191 In the illustrated example, the representative sampling unit 105
generates the
anonymous measurement data for a subset of customers having a particular
demographic profile
and a particular network usage profile specified by the audience measurement
entity. For
example, given specified demographic and network usage profiles, the
representative sampling
unit 105 samples (e.g., once or via several iterations) the customer data
stored in the customer
database(s) 115 to generate, without customer intervention, a customer sample
containing a
subset of customers representative of the specified demographic and network
usage profiles.
Additionally or alternatively, the customer database(s) 115 may already
determine and track the
demographics and/or network usage of the customers of the provider network
110. In such an
example, the representative sampling unit 105 may interrogate the customer
database(s) 115 to
obtain the demographic and/or network usage profiles as determined and tracked
by the customer
database(s) 115 (e.g., instead of receiving the demographic and/or usage
profiles from the
audience measurement entity). The representative sampling unit 105 may also
interrogate the
customer database(s) 115 to obtain a customer sample representative of these
demographic
and/or network usage profiles as determined and tracked by the customer
database(s) 115. Then,
in any of these examples, using customer identification information (e.g.,
customer device
identification information) included in the generated customer sample (e.g.,
generated from the
demographic and/or usage profiles provided by the audience measurement entity
or determined
and tracked by the customer database(s) 115), the representative sampling unit
105 retrieves and
processes log data from the network server(s) 120 to determine audience
measurement data
associated with the customers in the customer sample. The representative
sampling unit 105
removes any customer identification information from the audience measurement
data
determined from the server logs to maintain privacy (e.g., and replaces such
removed
information with anonymous identifiers), but retains other demographic
information to enable
classification of the anonymous audience measurement data according to the
specified
demographic and network usage profiles. An example implementation of the
representative
sampling unit 105 is illustrated in FIG. 2 and described in greater detail
below.
[00201 In the illustrated example, the representative sampling unit 105
communicates
with one or more example measurement servers 130 included in an example
measurement entity
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network 135 using an example secure communication link 140 carried via an
example
communication network 145, such as the Internet, a dedicated network, or any
other type of
communication network. The measurement entity network 135 is also a secure,
private network,
and is protected by an example firewall 150, which may be implemented by any
type of firewall
device or application. The secure communication link 140 can be implemented
by, for example,
a virtual private network (VPN), a secure file transfer protocol (FTP)
session, etc.
[0021] The measurement server(s) 130 accept profile configuration file(s) 155
specifying a particular demographic profile and a particular network usage
profile for which
audience measurement data is to be determined by the representative sampling
unit 105. The
measurement server(s) 130 convey the demographic and network usage profiles
specified via the
profile configuration file(s) 155 to the representative sampling unit 105 via
the secure
communication link 140. Examples of a demographic profile and a network usage
profile that
could be specified using the profile configuration file(s) 155 are illustrated
in FIG. 3 and
described in greater detail below.
[0022] The measurement server(s) 130 also generate report(s) 160 from the
anonymous audience measurement data determined by the representative sample
unit 105 and
downloaded to the measurement server(s) 130 via the secure communication link
140. The
report(s) 160 utilize any appropriate reporting format and include, for
example, audience ratings,
media content access metrics (e.g., such as popularity rankings). Furthermore,
the report(s) 160
can report the audience measurement data for the entire specified demographic
and network
usage profiles, or some subset (e.g., classification stratum or strata) of the
specified demographic
and/or network usage profiles.
[0023] While an example manner of implementing the environment of use 100 has
been illustrated in FIG. 1, one or more of the elements, processes and/or
devices illustrated in
FIG. I may be combined, divided, re-arranged, omitted, eliminated and/or
implemented in any
other way. Further, the example representative sampling unit 105, the example
provider network
110, the example customer database(s) 115, the example networks server(s) 120,
the example
firewall 125, the example measurement server(s) 130, the example measurement
entity network
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135, the example secure communication link 140, the example communication
network 145, the
example firewall 150 and/or, more generally, the example environment of use
100 of FIG. I may
be implemented by hardware, software, firmware and/or any combination of
hardware, software
and/or firmware. Thus, for example, any of the example representative sampling
unit 105, the
example provider network 110, the example customer database(s) 115, the
example networks
server(s) 120, the example firewall 125, the example measurement server(s)
130, the example
measurement entity network 135, the example secure communication link 140, the
example
communication network 145, the example firewall 150 and/or, more generally,
the example
environment of use 100 could be implemented by one or more circuit(s),
programmable
processor(s), application specific integrated circuit(s) (ASIC(s)),
programmable logic device(s)
(PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of
the appended
claims are read to cover a purely software and/or firmware implementation, at
least one of the
example environment of use 100, the example representative sampling unit 105,
the example
provider network 110, the example customer database(s) 115, the example
networks server(s)
120, the example firewall 125, the example measurement server(s) 130, the
example
measurement entity network 135, the example secure communication link 140, the
example
communication network 145 and/or the example firewall 150 are hereby expressly
defined to
include a tangible medium such as a memory, digital versatile disk (DVD),
compact disk (CD),
etc., storing such software and/or firmware. Further still, the example
environment of use 100 of
FIG. I may include one or more elements, processes and/or devices in addition
to, or instead of,
those illustrated in FIG. 1, and/or may include more than one of any or all of
the illustrated
elements, processes and devices.
[00241 A block diagram of an example implementation of the representative
sampling
unit 105 of FIG. I is illustrated in FIG. 2. The representative sampling unit
105 of FIG. 2
includes an example profile specifier 205 to obtain one or more demographic
profiles and one or
more network usage profiles specified by an audience measurement entity.
Generally, a
demographic profile includes a set of demographic categories, with each
demographic category
including a set of target segments (e.g., also referred to as target strata)
associated respectively
with a set of target population percentages. Similarly, a network usage
profile generally includes
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a set of usage categories associated respectively with another set of target
population
percentages. An example demographic profile 305 and an example network usage
profile 310
that could be obtained by the profile specifier 205 are illustrated in FIG. 3.
[00251 Turning to FIG. 3, the example demographic profile 305 includes two (2)
categories, an age category 312 and an income category 314 (although other
categories could be
included instead of, or in addition to, either or both of these two
categories). The age category
312 includes a set of target age segments (or strata) 316-320. For example, a
first target age
segment (or stratum) 316 may correspond to the population segment of people
under 18 years
old, a second target age segment 318 may correspond to people from 18 to 34
years old, and a
third target age segment 320 may correspond to people greater than 34 years
old. Each target
segment 316-320 is associated with a respective target population percentage
326-330. Each
target population percentage 326-330 can be a particular percentage value
(e.g., such as 5%,
10%, etc.) or a range of percentage values (e.g., such as 5-10%, 10-15%,
etc.). Similarly, the
income category 314 includes a set of target segments 336-340. For example, a
first target
income segment 336 may correspond to the population segment of people having
an annual
income under $40,000, a second target income segment 338 may correspond to
people having an
annual income from $40,000 to $100,000, and a third target income segment 340
may
correspond to people having an annual income over $100,000. Each target
segment 336-340 is
associated with a respective target population percentage 346-350. As noted
above, each target
population percentage 346-350 can be a particular percentage value or a range
of percentage
values.
[00261 The example network usage profile 310 includes three (3) usage
categories (or
strata) 352-356 (although more or fewer categories could be included in an
example
implementation). For example, the first usage category (or stratum) 352
corresponds to
customers that exhibit low network usage, the second usage category 354
corresponds to
customers that exhibit medium network usage, and the third usage category 352
corresponds to
customers that exhibit high network usage. Network usage can be characterized
in terms of, for
example, network accesses during a time period, bandwidth used during a time
period,
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bandwidth purchased during a time period, etc. For example, a low network user
could be a
customer who accesses content via the provider network 110 approximately one
time per week, a
medium network user could be a customer who accesses content from one to five
times per
week, and a high network user could be a customer who accesses content greater
than five times
per week. As another example, a low network user could be a customer who
accesses or
purchases less than one megabyte of content per week, a medium network user
could a customer
who accesses or purchase from one to five megabytes of content per week, and a
high network
user could be a customer who accesses or purchases greater than five megabytes
of content per
week. These preceding values are exemplary and not meant to be limiting.
Similar to the
demographic profile 305, each category (or stratum) 352-356 in the network
usage profile 310 is
associated with a respective target population percentage 362-366, which can
be a particular
percentage value or a range of percentage values.
[00271 Returning to FIG. 2, the illustrated representative sampling unit 105
includes
an example customer sample generator 210 to sample customer data stored in
records of, for
example, the customer database(s) 115 of FIG. 1 to generate a customer sample
(e.g., such as a
single customer sample) containing a subset of customers representative of,
for example, a
particular demographic profile and a particular network usage profile obtained
by the profile
specifier 205. The customer sample generator 210 employs an example customer
database
interface 215 to query the customer database(s) 115 and retrieve query results
from the customer
database(s) 115. In an example implementation, the customer sample generator
210 uses the
customer database interface 215 to index the customer data stored in the
customer database
according to the demographic categories included in the obtained demographic
profile to
determine indexed customer data. For example, with reference to the example
demographic
profile 305 of FIG. 3, the customer sample generator 210 can use the customer
database interface
215 to index (e.g., via sorting) the customer data in the customer database(s)
115 into: (i) a first
group corresponding to customers included in both the first target age segment
316 and the first
target income segment 336; (ii) a second group corresponding to customers
included in both the
first target age segment 316 and the second target income segment 338; (iii) a
third group
corresponding to customers included in both the first target age segment 316
and the third target
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income segment 338, etc., until the customers are indexed into all possible
groupings of target
age and income segments. Then, in such an example implementation, the customer
sample
generator 210 randomly samples (e.g., selects) the indexed customer data
according to the target
population percentages included in the obtained demographic profile to
randomly select a subset
of customers representative of the demographic profile. For example, with
reference to the
preceding example based on the demographic profile 305 of FIG. 3, the customer
sample
generator 210 could randomly select customers from the first group (e.g.,
corresponding to
customers included in both the first target age segment 316 and the first
target income segment
336) such that the number of customers selected relative to the total number
of customers in the
customer sample is determined by multiplying the target population percentages
326 and 346.
[00281 In at least some example implementations, customer network usage
information is also stored in the customer database(s) 115 (e.g., such as when
network usage
corresponds to purchased network bandwidth). In such examples, the customer
sample generator
210 can use the customer database interface 215 as described above to generate
another customer
sample representative of the obtained network usage profile by indexing the
customer data stored
in the customer database according to the set of network categories included
in the obtained
network usage profile, and then randomly sampling (e.g., selecting) the
indexed customer data
according to the target population percentages included in the obtained
network usage profile to
randomly select a subset of customers representative of the network usage
profile. Additionally
or alternatively, the customer sample generator 210 can use the customer
database interface 215
to generate a single customer sample representative of both the obtained
demographic and
network usage profiles. With reference to the example demographic profile 305
and the example
network usage profile 310 of FIG. 3, in such an example, the customer sample
generator 210 can
treat the network usage profile 310 as another dimension (e.g., category) of
the demographic
profile 305. In other words, the customer sample generator 210 can use the
customer database
interface 215 to index (e.g., via sorting) the customer data in the customer
database(s) 115 into:
(i) a first group corresponding to customers included in a combination of the
first target age
segment 316, the first target income segment 336, and the low network usage
category 352; (ii) a
second group corresponding to customers included in a combination of the first
target age
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segment 316, the first target income segment 336 and the medium network usage
category 354,
etc., until the customers are indexed into all possible groupings of target
age and income
segments, as well as network usage categories. Then, the customer sample
generator 210
randomly samples (e.g., selects) the indexed customer data according to the
target population
percentages included in the obtained demographic and network usage profiles to
randomly select
a subset of customers representative of both the demographic and network usage
profiles (e.g.,
such that each indexed group includes a number of randomly selected customers
whose
percentage of the entire selected subset of customers corresponds to the
multiplication of the
individual target population percentages of the population segments making up
the group).
100291 The customer sample generator 210 can employ any type of random or
pseudorandom sampling technique to sample the customer data included in the
customer
database(s) 115. After generating customer sample(s) representative of the
obtained
demographic profile (and/or the obtained network usage profile if network
usage information is
stored in the customer database(s) 115), the customer sample generator 210
stores the generated
customer sample(s) in a customer sample storage 220. The customer sample
storage 220 may be
implemented by any type or memory or storage device or technology, such as the
mass storage
device 730 and/or the volatile memory 718 included in the example processing
system 700
illustrated in FIG. 7 and described in greater detail below.
[00301 The representative sampling unit 105 also includes an example
measurement
data sampler 225 to obtain and process server log data for the customers
included in the customer
sample(s) stored in the customer sample storage 220. For example, the
measurement data
sampler 225 uses an example server log interface 230 to retrieve server log
data from the
network server(s) 120 for customers included in a customer sample generated by
the customer
sample generator 210, but not for customers not included in the customer
sample. Alternatively,
in an example real-time sampling implementation, the measurement data sampler
225 configures
the network server(s) 120 to automatically provide (e.g., via a push
mechanism) the server log
data for customers included in the customer sample generated by the customer
sample generator
210, but not for customers not included in the customer sample. In an example
implementation,
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customer identification information and, in particular, customer device
identification information
(e.g., such as phone numbers, IP addresses, usernames, PINs, cookie
identifiers, etc.) included in
the customer sample is used to retrieve the server log data for those
customers included in the
customer sample, or configure the network server(s) 120 to automatically
provide (e.g., push) the
server log data for those customers included in the customer sample. Thus, for
each customer in
the customer sample, server log data that includes or is otherwise associated
with customer
identification information (e.g., such as a phone numbers IP address, etc.)
representative of that
particular customer is retrieved or otherwise obtained (e.g., automatically
via a push mechanism)
by the measurement data sampler 225 from the network server(s) 120.
[00311 To generate audience measurement data (e.g., offline using the
retrieved server
log data or in real-time using the automatically provided/pushed server log
data), the
measurement data sampler 225 then classifies the server log data for each
customer in the
customer sample into the particular demographic category or categories into
which the particular
customer belongs (as well as into the particular customer's network usage
category if known
from the customer data stored in the customer database(s) 115). Additionally
or alternatively,
such as in example implementations in which customer network usage information
is not stored
in the customer database(s) 115, the measurement data sampler 225 processes
the server log data
to determine the network usage category for each customer in the customer
sample, thereby
allowing the measurement data sampler 225 to classify the particular
customer's server log data
into a particular network usage category. For example, the measurement data
sampler 225 may
analyze the server log data to determine the network accesses during a time
period, bandwidth
used during a time period, etc., to determine the network usage category for
each customer
included in the customer sample and, thus, the actual network usage profile of
the customer
sample.
[00321 The representative sampling unit 105 further includes an example
profile
verifier 240 to determine whether the server log data retrieved and processed
by the
measurement data sampler 225 corresponds to (e.g., is representative of) the
demographic and
network usage profiles obtained by the profile specifier 205 as specified by
the audience
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measurement entity. For example, in operating scenarios in which the customer
databases(s) 115
do not store network usage information for each customer, the customer sample
generated by the
customer sample generator 210 will be representative of the obtained
demographic profile, but
may or may not be representative of the obtained network usage profile. In
such operating
scenarios, the profile verifier 240 compares the actual network usage profile
for the customer
sample (e.g., as determined by the measurement data sampler 225 from the
server log data) with
the obtained network usage profile to determine whether the profiles match or
substantially
match within some tolerance limit for each network usage category. If the
profiles do not match,
the profile verifier 240 causes the customer sample generator 210 to update
the customer sample
by, for example, (1) randomly removing customers from the sample belonging to
each network
usage category whose actual percentage of customers exceeds the specified
percentage, and (2)
replacing the removed customers with new, randomly sampled customers belonging
to the same
demographic categories as the removed customers. The measurement data sampler
225 then
obtains server log data for these newly sampled customers and recomputes the
actual network
usage profile for the updated customer sample. This profile verifier 240
iteratively repeats this
procedure until the actual network usage profile of the customer sample
matches the specified
network usage and demographic profiles and/or a specified number of iterations
is performed.
[00331 To render the resulting audience measurement data determined by the
measurement data sampler 225 and verified by the profile verifier 240 private,
the representative
sampling unit 105 includes an example privacy unit 245. The privacy unit 245
removes any
customer identification information that could be used to identify particular
customers, such as
customer device identification information (e.g., phone numbers, IP addresses,
etc.) included in
the audience measurement data (e.g., as part of the retrieved server log
data). In some examples,
the privacy unit 245 replaces the removed customer identification information
with anonymous
identifiers that can be used to group associated data without actually
identifying any of the
customers. However, the privacy unit 245 retains any demographic and usage
classification
information included in the audience measurement data (e.g., as determined by
the measurement
data sampler 225). The representative sampling unit 105 includes a data
transmission unit 250 to
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transmit the anonymous audience measurement data to the measurement server(s)
of the
audience measurement entity for subsequent processing.
[00341 While an example manner of implementing the representative sampling
unit
105 of FIG. I has been illustrated in FIG. 2, one or more of the elements,
processes and/or
devices illustrated in FIG. 2 may be combined, divided, re-arranged, omitted,
eliminated and/or
implemented in any other way. Further, the example profile specifier 205, the
example customer
sample generator 210, the example customer database interface 215, the example
customer
sample storage 220, the example measurement data sampler 225, the example
server log
interface 230, the example profile verifier 240, the example privacy unit 245,
the example data
transmission unit 250 and/or, more generally, the example representative
sampling unit 105 of
FIG. 2 may be implemented by hardware, software, firmware and/or any
combination of
hardware, software and/or firmware. Thus, for example, any of the example
profile specifier
205, the example customer sample generator 210, the example customer database
interface 215,
the example customer sample storage 220, the example measurement data sampler
225, the
example server log interface 230, the example profile verifier 240, the
example privacy unit 245,
the example data transmission unit 250 and/or, more generally, the example
representative
sampling unit 105 could be implemented by one or more circuit(s), programmable
processor(s),
application ASIC(s), PLD(s) and/or FPLD(s), etc. When any of the appended
claims are read to
cover a purely software and/or firmware implementation, at least one of the
example
representative sampling unit 105, the example profile specifier 205, the
example customer
sample generator 210, the example customer database interface 215, the example
customer
sample storage 220, the example measurement data sampler 225, the example
server log
interface 230, the example profile verifier 240, the example privacy unit 245
and/or the example
data transmission unit 250 are hereby expressly defined to include a tangible
medium such as a
memory, DVD, CD, etc., storing such software and/or firmware. Further still,
the example
representative sampling unit 105 of FIG. 2 may include one or more elements,
processes and/or
devices in addition to, or instead of, those illustrated in FIG. 2, and/or may
include more than
one of any or all of the illustrated elements, processes and devices.
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[00351 Flowcharts representative of example machine readable instructions that
may
be executed to implement the example environment of use 100, the example
representative
sampling unit 105, the example provider network 110, example customer
database(s) 115, the
example networks server(s) 120, the example firewall 125, the example
measurement server(s)
130, the example measurement entity network 135, the example secure
communication link 140,
the example communication network 145, the example firewall 150, the example
profile
specifier 205, the example customer sample generator 210, the example customer
database
interface 215, the example customer sample storage 220, the example
measurement data sampler
225, the example server log interface 230, the example profile verifier 240,
the example privacy
unit 245 and/or the example data transmission unit 250 are shown in FIGS. 4-6.
In these
examples, the machine readable instructions represented by each flowchart may
comprise one or
more programs for execution by: (a) a processor, such as the processor 712
shown in the
example processing system 700 discussed below in connection with FIG. 7, (b) a
controller,
and/or (c) any other suitable device. The one or more programs may be embodied
in software
stored on a tangible medium such as, for example, a flash memory, a CD-ROM, a
floppy disk, a
hard drive, a DVD, or a memory associated with the processor 712, but the
entire program or
programs and/or portions thereof could alternatively be executed by a device
other than the
processor 712 and/or embodied in firmware or dedicated hardware (e.g.,
implemented by an
ASIC, a PLD, an FPLD, discrete logic, etc.).
[00361 For example, any or all of the example environment of use 100, the
example
representative sampling unit 105, the example provider network l 10, example
customer
database(s) 1] 5, the example networks server(s) 120, the example firewall
125, the example
measurement server(s) 130, the example measurement entity network 135, the
example secure
communication link 140, the example communication network 145, the example
firewall 150,
the example profile specifier 205, the example customer sample generator 210,
the example
customer database interface 215, the example customer sample storage 220, the
example
measurement data sampler 225, the example server log interface 230, the
example profile verifier
240, the example privacy unit 245 and/or the example data transmission unit
250 could be
implemented by any combination of software, hardware, and/or firmware. Also,
some or all of
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the machine readable instructions represented by the flowchart of FIGS. 4-6
may be
implemented manually. Further, although the example machine readable
instructions are
described with reference to the flowcharts illustrated in FIGS. 4-6, many
other techniques for
implementing the example methods and apparatus described herein may
alternatively be used.
For example, with reference to the flowcharts illustrated in FIGS. 4-6, the
order of execution of
the blocks may be changed, and/or some of the blocks described may be changed,
eliminated,
combined and/or subdivided into multiple blocks.
[00371 Example machine readable instructions 400 that may be executed to
implement
the example representative sampling unit 105 of FIGS. I and/or 2 are
represented by the
flowchart shown in FIG. 4. The example machine readable instructions 400 may
be executed at
predetermined intervals, based on an occurrence of a predetermined event,
etc., or any
combination thereof. As illustrated in FIG. 4, the example machine readable
instructions 400
implement two processing threads, a customer sample generation thread 404 and
a measurement
data sampling thread 408. In at least some example implementations, the
measurement data
sampling thread 408 executes more frequently than the customer sample
generation thread 404.
For example, demographic and network usage profiles are expected to be updated
relatively
infrequently as customers are gained, lost, update service profiles, etc., on
a relatively infrequent
basis, such as daily, weekly, monthly, quarterly, etc. Thus, the customer
sample generation
thread 404 can be executed at a similarly infrequent rate to generate a
customer sample
corresponding to a newly updated demographic and/or network usage profiles,
with potentially
more frequent invocations to update the customer sample when its actual
network usage profile
does not correspond with the specified network usage profile, as described
below. In contrast,
the measurement data sampling thread 408 is expected to be executed more
frequently, such as
every minute, every few minutes (e.g., such as every 15 minutes), hourly,
daily, etc., depending
upon the desired temporal accuracy of the generated audience measurement data.
[00381 With reference to FIGS. 1-3, the customer sample generation thread 404
of the
machine readable instructions 400 of FIG. 4 begins execution at block 412 at
which the profile
specifier 205 included in the representative sampling unit 105 obtains a
demographic profile,
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such as the demographic profile 305, from the audience measurement entity
operating the
audience measurement server(s) 130. At block 416, the profile specifier 205
included in the
representative sampling unit 105 obtains a network usage profile, such as the
network usage
profile 310, from the audience measurement entity operating the audience
measurement server(s)
130. Then, at block 420 the customer sample generator 210 included in the
representative
sampling unit 105 samples customer data stored in records of the customer
database(s) 115 to
generate a customer sample containing a subset of customers representative of
the demographic
profile and the network usage profile obtained at block 412 and 416,
respectively. The generated
customer sample is stored in the customer sample storage 220. Example machine
readable
instructions that may be used to implement the processing at block 420 are
illustrated in FIG. 5
and described in greater detail below.
[0039] After customer sample generation is performed at block 420, at block
424 the
customer sample generator 210 determines whether the customer sample generated
at block 420
needs to be updated. For example, the profile verifier 240 included in the
representative
sampling unit 105 may indicate that that the customer sample needs to be
updated if an actual
network usage profile for the customer sample (e.g., as determined from sample
server log data)
and the specified network usage profile obtained at block 416 fail to match or
substantially
match within a specified tolerance. If the customer sample needs to be updated
(block 424),
processing returns to block 420 at which the customer sample is updated.
However, if the
customer sample does not need to be updated (block 424), at block 428 the
profile specifier 205
determines whether there has been an update to the specified demographic
and/or network usage
profiles. If one or both of the profiles are to be updated (block 428),
processing returns to block
412. Otherwise, execution of the customer sample generation thread 404 ends
until it is time to
be invoked to generate a new customer sample.
[0040] The measurement data sampling thread 408 of the machine readable
instructions 400 of FIG. 4 begins execution at block 432 at which the
measurement data sampler
225 included in the representative sampling unit 105 retrieves server log data
from the network
server(s) 120 for the customers contained in the customer sample stored in the
customer sample
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storage 220. Alternatively, in an example real-time sampling implementation,
the network
server(s) 120 can automatically provide (e.g., via a push mechanism) their
server log data to the
representative sampling unit 105 for sampling as the data becomes available in
real-time.
Additionally, at block 432 the measurement data sampler 225 determines
audience measurement
data from the retrieved (or provided/pushed) server log data. Example machine
readable
instructions that may be used to implement the processing at block 432 are
illustrated in FIG. 6
and described in greater detail below.
[0041] After measurement data sampling is performed at block 432, at block 436
the
profile verifier 240 determines whether the actual network usage profile
determined by the
measurement data sampler 225 from the retrieved server logs corresponds to the
specified
network usage profile obtained at block 416. If the actual and specified
network usage profiles
do not correspond (block 436), the profile verifier 240 invokes block 424 of
the customer sample
generation thread 404 with an indication that the customer sample needs to be
updated.
However, if the actual and specified network usage profiles do correspond
(block 436), then at
block 440 the privacy unit 245 included in the representative sampling unit
105 scrubs the
audience measurement data determined at block 432 to remove any customer
identification
information, but to retain any other demographic and/or network usage
classifications. Then, at
block 444 the data transmission unit 250 included in the representative
sampling unit 105
transmits the resulting anonymous measurement data to the audience measurement
entity's
measurement server(s) 130. Then, at block 448 the measurement data sampler 225
determines
whether it is time to update the measurement data sample. If it is time to
update the
measurement data (block 448), then processing returns to block 432 at which
the measurement
data sampler 225 retrieves and processes new server log data to determine
updated audience
measurement data. Otherwise, execution of the measurement data sampling thread
408 ends
until it is time to be invoked to generate new anonymous audience measurement
data.
[0042] Example machine readable instructions 420 that may be used to implement
the
customer sample generation processing at block 420 of FIG. 4 are illustrated
in FIG. 5. With
reference to FIGS. 1-3, the machine readable instructions 420 of FIG. 5 begin
execution at block
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504 at which the customer sample generator 210 included in the representative
sampling unit 105
retrieves the demographic profile obtained by the profile specifier 205 from
the audience
measurement entity. At block 508, the customer sample generator 210 accesses
the customer
database(s) 115, and at block 512 the customer sample generator 210 uses the
customer data
stored in the customer database(s) 115 to index the customers into groups
based on the
demographic categories included in the demographic profile retrieved at block
504. For
example, at block 512 the customer sample generator 210 can generate groups
for each possible
permutation of selecting a particular demographic segment for each category
across all the
different categories included in the demographic profile. The customer sample
generator 210
then places each customer in the appropriate demographic group based on the
customer's
identification and demographic data stored in the customer database(s) 115.
[00431 Next, at block 516 the customer sample generator 210 generates a random
customer sample matching the target population percentage specified for each
category in the
demographic profile. For example, at block 516 the customer sample generator
210 determines
an effective population percentage for each index group determined at block
512 by multiplying
the target population percentages for each category's constituent population
segment included in
the particular index group. Then, for each index group, the customer sample
generator 210
randomly samples (e.g., selects) a number of customers from each index group
such that the ratio
of the number of customers sampled from each index group to the total number
of customer
included in the customer sample corresponds to the determined effective
population percentage
for that particular item group. The result is a subset of customers whose
actual demographic
profile corresponds to the specified demographic profile obtained at block
504.
[00441 Next, at block 520 the customer sample generator 210 determines whether
the
customer database(s) 115 contain network usage information. If so, at block
524 the customer
sample generator 210 begins generating another customer sample having the
specified network
usage profile obtained by the profile specifier 205. In particular, at block
524 the customer
sample generator 210 uses the network usage data and associated customer
identification
information stored in the customer database(s) 115 to index (e.g., sort) the
customers the
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different network usage categories included in the demographic profile
retrieved at block 504.
Then, at block 528 the customer sample generator 210 generates a random
customer sample
matching the target population percentage specified for each category in the
network usage
profile. For example, the customer sample generator 210 randomly samples
(e.g., selects) a
number of customers from each network usage category such that the ratio of
the number of
customers sampled from each network usage category to the total number of
customer included
in the customer sample corresponds to the target population for that
particular network usage
category. The result is a subset of customers whose actual network usage
profile corresponds to
the specified network usage profile obtained at block 504.
[00451 Alternatively, if the customer database(s) 115 contain network usage
information, the processing at blocks 512 through 528 can be combined to
generate a customer
sample representative of both the obtained demographic and network usage
profiles. In such an
example, the set of network usage categories in the specified network usage
profile is treated as
another dimension (e.g., as another demographic category) of the specified
demographic profile,
as described above, when indexing and sampling the customers (e.g., at blocks
512 and 516) to
generate the customer sample.
[00461 Next, at block 532 the customer sample generator 210 stores the
customer
sample or samples generated at blocks 516 and 528 in the customer sample
storage 220.
Execution of the example machine readable instructions 420 then ends.
[00471 Example machine readable instructions 432 that may be used to implement
the
measurement data sampling processing at block 432 of FIG. 4 are illustrated in
FIG. 6. With
reference to FIGS. 1-3, execution of the machine readable instructions 432 of
FIG. 6 begins at
block 604 at which the measurement data sampler 225 retrieves a customer
sample generated by
the customer sample generator 210 from customer sample storage 220. At block
608, the
measurement data sampler 225 accesses the network server logs maintained by
the network
server(s) 120, and at block 612 the measurement data sampler 225 retrieves the
server log data
for customers included in the customer sample retrieved at block 604. For
example, at block 612
the measurement data sampler 225 can use customer identification information,
such as customer
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device identification information (e.g., phone numbers, IP addresses, etc.) to
retrieve server log
data for customers included in the customer sample, but not for other
customers (e.g., by
matching device identification information included in the network server
logs).
[0048] Next, at block 616 the measurement data sampler 225 determines whether
the
customers included in the customer sample need to be classified into the
network usage
categories of the obtained network usage profile (e.g., such as when network
usage information
is not included in the customer database(s) 115 and, thus, a customer sample
having the specified
network usage profile cannot be determined a priori). If network usage
classification is needed
(block 616), at block 620 the measurement data sampler 225 processes the
server log data
retrieved at block 612 to classify each customer in the customer sample into a
particular network
usage category, as described above. Then, at block 624 the measurement data
sampler 225
associates (e.g., classifies) each customer's server log data (e.g., which is
already associated with
the customer's identification information) with the demographic classification
and network usage
classification (e.g., the latter if known from customer data stored in the
customer database(s))
into which the particular customer belongs. The measurement data sampler 225
stores the
retrieved server log data and associated customer identification information,
demographic
classifications and network usage classifications as audience measurement data
at block 628.
Execution of the machine readable instructions 432 then ends.
[0049] FIG. 7 is a block diagram of an example processing system 700 capable
of
implementing the apparatus and methods disclosed herein. The processing system
700 can be,
for example, a server, a personal computer, a personal digital assistant
(PDA), an Internet
appliance, a DVD player, a CD player, a digital video recorder, a personal
video recorder, a set
top box, or any other type of computing device.
[0050] The system 700 of the instant example includes a processor 712 such as
a
general purpose programmable processor. The processor 712 includes a local
memory 714, and
executes coded instructions 716 present in the local memory 714 and/or in
another memory
device. The processor 712 may execute, among other things, the machine
readable instructions
represented in FIGS. 4-6. The processor 712 may be any type of processing
unit, such as one or
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more microprocessors from the Intel Centrino family of microprocessors, the
Intel
Pentium family of microprocessors, the Intel Itanium family of
microprocessors, and/or the
Intel XScale family of processors. Of course, other processors from other
families are also
appropriate.
[0051] The processor 712 is in communication with a main memory including a
volatile memory 718 and a non-volatile memory 720 via a bus 722. The volatile
memory 718
may be implemented by Static Random Access Memory (SRAM), Synchronous Dynamic
Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS
Dynamic Random Access Memory (RDRAM) and/or any other type of random access
memory
device. The non-volatile memory 720 may be implemented by flash memory and/or
any other
desired type of memory device. Access to the main memory 718, 720 is typically
controlled by a
memory controller (not shown).
[0052] The processing system 700 also includes an interface circuit 724. The
interface circuit 724 may be implemented by any type of interface standard,
such as an Ethernet
interface, a universal serial bus (USB), and/or a third generation
input/output (3GIO) interface.
[0053] One or more input devices 726 are connected to the interface circuit
724. The
input device(s) 726 permit a user to enter data and commands into the
processor 712. The input
device(s) can be implemented by, for example, a keyboard, a mouse, a
touchscreen, a track-pad,
a trackball, an isopoint and/or a voice recognition system.
[0054] One or more output devices 728 are also connected to the interface
circuit 724.
The output devices 728 can be implemented, for example, by display devices
(e.g., a liquid
crystal display, a cathode ray tube display (CRT)), by a printer and/or by
speakers. The interface
circuit 724, thus, typically includes a graphics driver card.
[0055] The interface circuit 724 also includes a communication device such as
a
modem or network interface card to facilitate exchange of data with external
computers via a
network (e.g., an Ethernet connection, a digital subscriber line (DSL), a
telephone line, coaxial
cable, a cellular telephone system, etc.).
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[00561 The processing system 700 also includes one or more mass storage
devices 730
for storing software and data. Examples of such mass storage devices 730
include floppy disk
drives, hard drive disks, compact disk drives and digital versatile disk (DVD)
drives. The mass
storage device 730 may implement the customer sample storage 220.
Alternatively, the volatile
memory 718 may implement the customer sample storage 220.
[00571 As an alternative to implementing the methods and/or apparatus
described
herein in a system such as the processing system of FIG. 7, the methods and or
apparatus
described herein may be embedded in a structure such as a processor and/or an
ASIC
(application specific integrated circuit).
[00581 Finally, although certain example methods, apparatus and articles of
manufacture have been described herein, the scope of coverage of this patent
is not limited
thereto. On the contrary, this patent covers all methods, apparatus and
articles of manufacture
fairly falling within the scope of the appended claims either literally or
under the doctrine of
equivalents.
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