Patent Grant
12200298
This application is a related to U.S. Pat. No. 8,365,212 B1 issued on Jan. 29, 2013 entitled “SYSTEM AND METHOD FOR ANALYZING HUMAN INTERACTION WITH ELECTRONIC DEVICES THAT ACCESS A COMPUTER SYSTEM THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
This application is also related to U.S. Pat. No. 8,365,213 B1 issued on Jan. 29, 2013 entitled “SYSTEM AND METHOD FOR MEASURING TELEVISION ADVERTISING AND PROGRAM VIEWING AT A SECOND-BY-SECOND LEVEL AND FOR MEASURING EFFECTIVENESS OF TARGETED ADVERTISING” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
This application is also related to the co-pending application U.S. application Ser. No. 13/360,704 filed on Jan. 28, 2012 entitled “SYSTEM AND METHOD FOR MEASURING LONGITUDINAL VIDEO ASSET VIEWING AT A SECOND-BY-SECOND LEVEL TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES THAT ACCESS A COMPUTER SYSTEM THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
This application is also related to the co-pending application U.S. application Ser. No. 13/567,073 filed on Aug. 5, 2012 entitled “SYSTEM AND METHOD FOR MEASURING LINEAR, DVR, AND VOD VIDEO PROGRAM VIEWING AT A SECOND-BY-SECOND LEVEL TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES DELIVERING CONTENT THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
This application is also related to the co-pending application U.S. application Ser. No. 13/740,199 filed on Jan. 13, 2013 entitled “SYSTEM AND METHOD FOR MEASURING DEMOGRAPHIC-BASED HOUSEHOLD ADVERTISING REACH; IMPRESSIONS, SHARE, HUT, RATING, AND CUMULATIVE AUDIENCE; AND VIDEO PROGRAM VIEWING, BASED ON SECOND-BY-SECOND HOUSE LEVEL VIEWING ACTIVITY, TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES DELIVERING CONTENT THROUGH A NETWORK″ by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
This application is also related to the co-pending application U.S. application Ser. No. 14/013,031 filed on Aug. 29, 2013 entitled “SYSTEM AND METHOD FOR USING THE HADOOP MAPREDUCE FRAMEWORK TO MEASURE VIDEO CONTENT VIEWING ON SECOND-BY-SECOND LEVEL TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES DELIVERING CONTENT THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
I have not found any relevant prior art at the present time.
With the ever increasing number of consumer choices for television viewing, it is important for advertisers, content producers, and service providers such as cable television and satellite television and internet protocol television companies to be able to accurately measure audience viewership. I have discussed this problem extensively in my prior applications. This application teaches how to analyze video viewing activity related to sessionized or program-based linear viewing, digital video recording playback viewing, and video on demand viewing using the Hadoop MapReduce distributed computing framework. The reader will readily recognize that video on demand encompasses a wide range of video content including, but not limited to, cable and/or satellite television video on demand, all variety of video content delivered electronically across a network, and online educational videos.
Existing Tools for Data Analysis
In my prior application U.S. application Ser. No. 13/567,073 filed on Aug. 5, 2012 I taught how to analyze sessionized or program-based linear viewing, digital video recording playback viewing, and video on demand viewing by loading the viewing activity data into arrays in the memory of a computer and then running algorithms against that data. In certain cases, an analyst may wish to use the Hadoop MapReduce distributed computing framework to analyze sessionized or program-based linear viewing, digital video recording playback viewing, and video on demand viewing. I have not identified any patents that teach how to use MapReduce to solve this problem.
In accordance with one embodiment, I disclose a computer-implemented method of aggregating sessionized or program-based linear viewing, digital video recording playback viewing, and video on demand viewing using the Hadoop MapReduce distributed computing framework. This will allow an analyst to aggregate second-by-second video viewing activity for this kind of viewing. Once this data has been aggregated, it can be used in any number of downstream analytic processes to provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.
By using the Hadoop MapReduce distributed computing framework to aggregate sessionized or program-based linear viewing, digital video recording playback viewing, and video on demand viewing, an analyst can harness the power of hundreds or even thousands of processors working in parallel to solve the problem of aggregating this video viewing activity data. This will allow an analyst to work with data sets of all sizes including extremely large data sets. The resulting files can be loaded to a relational database for various analytics similar to what I have taught in my other patent applications referenced previously. Additionally, the resulting files can be used in other Hadoop processes to correlate video viewing activity with other social media activity, with weather, with other programming content, and similar uses.
The following are definitions that will aid in understanding one or more of the embodiments presented herein:
Aggregated Video viewing activity means any measurements or aggregations produced by the MapReduce distributed computing framework as it aggregates video program viewing detail records or any value calculated by a Data Analysis Program as part of this process.
Computer readable format means any data format that can be read by a computer program or a human being as necessary. Nonlimiting examples include:
Data analysis computer system means a combination of one or more computers on which a Data Analysis Program or Programs or Hadoop or MapReduce processes can be executed. Nonlimiting examples include:
Data analysis computer of known type means any commonly available computer system running a commonly known operating system. Nonlimiting examples include:
Data analysis program means a computer program or programs that are able to execute on a Data analysis computer of known type. Nonlimiting examples include:
Demographic information means any data item that can describe a characteristic of a viewer or a subscriber or a household associated with a viewer who is operating the video asset viewing device. Nonlimiting examples include income, ethnicity, gender, age, marital status, location, geographic area, postal code, census data, occupation, social grouping, family status, any proprietary demographic grouping, segmentation, credit score, dwelling type, homeownership status, property ownership status, rental status, vehicle ownership, tax rolls, credit card usage, religious affiliation, sports interest, political party affiliation, cable television subscriber type, cable television subscriber package level, and cell phone service level.
Device Characteristic means any feature or capability or aspect or descriptive qualifier or identifier of a video viewing device. Nonlimiting examples include that this may identify the type of device such as a set-top box, a tablet, a smart phone; a capability of the device such as the ability to record video or to support multiple viewing windows, or a manufacturer identifier.
Device Type is a subset of Device Characteristic where device type may, as a nonlimiting example, identify the type of device such as a set-top box, a tablet, a smart phone.
Digital Video Recorder means a device that records video content from a network for later playback. This includes but is not limited to set-top box DVR, network DVR, and cloud DVR.
DVR—see Digital Video Recorder.
Digital Video Recording (DVR) Playback is when the viewer plays back content that was previously recorded on their DVR. DVR content can be viewed using various Trick Play features.
Geographic information means any service area or any network hierarchy designation or marketing area or other designated area used by a cable television company or a satellite television company or IP Television delivery company or educational service provider or video asset delivery system. The boundary or description of a geographic area is defined based on the needs of the service provider. Nonlimiting examples include a Market in a cable company network, a Headend in a cable company network, a Hub in a cable company network, a census tract, a cell tower identifier, a service area for satellite TV, advertising zone, a zip code, or some other geographic identifier. The geographic information may then be used to identify the location of a video asset viewing device or geographic information about the about the house associated with the device or the location of the device at the time of the viewer interaction in the event that the viewer interaction occurs in a location different than the location of the house associated with the device.
Network means any computer network. Nonlimiting examples include:
Pipe delimited text files means data files where the fields are separated by the “I” character.
Sessionized Linear Viewing is linear tuning activity which has been transformed into program based viewing. A simple linear tuning event may cross program boundaries. That simple linear tuning event can be split into multiple program based linear viewing activity records by creating separate tuning records for each program that is viewed during the linear tuning event. The viewer may use “trick plays” when viewing this content. Additionally, the sessionized linear viewing may represent live viewing activity or time shifted viewing activity.
Set-top box means a video asset viewing device that receives external signals and decodes those signals into content that can be viewed on a television screen or similar display device.
The signals may come from a cable television system, a satellite television system, a network, or any other suitable means. A set-top box may have one or more tuners. The set-top box allows the user to interact with it to control what is displayed on the television screen. The set-top box is able to capture the commands given by the user and then transmit those commands to another computer system. For purposes of this application, stating that a set-top box tunes to a channel is equivalent to stating that a tuner in a set-top box has tuned to a channel. A set-top box may also play back previously recorded video content.
STB means Set-top box.
Trick Play means using features of the video viewing device to execute operations such as Play, Fast Forward at various speeds (1×, 2×, 3×, 4×), Pause, Skip, Reverse at various speeds (1×, 2×, 3×, 4×), Slow play, slow reverse, and similar activities.
Tuner means a tuner in a Set-top box.
Tuner index means an identifier of a tuner in a Set-top box.
Video On Demand (VOD) a video service whereby previously recorded video content is made available for viewing. VOD content can be viewed using various Trick Play features. The content may include, but is not limited to, cable and/or satellite television video on demand, all variety of video content delivered electronically across a network, and online educational videos.
Video asset means any programming content that may be viewed and/or heard. A Video Program may contain multiple Video Assets. Nonlimiting examples of Video Asset include:
Video asset viewing device means any electronic device that may be used either directly or indirectly by a human being to interact with video content where the video content is provided by a cable television system or a satellite television system or a computer system accessed through a network. Nonlimiting examples include: Gaming station, web browser, MP3 Player, Internet Protocol phone, Internet Protocol television, mobile device, mobile smart phone, set-top box, satellite television receiver, set-top box in a cable television network, set-top box in a satellite television system, cell phone, personal communication device, personal video recorder, personal video player, two-way interactive service platforms, personal computer, tablet device.
Video server delivering video content through a network means any computer system, any individual piece of computer equipment or electronic gear, or any combination of computer equipment or electronic gear which enables or facilitates the viewer interaction with the video asset viewing device. Nonlimiting examples include:
Video viewing activity means any identifiable activity that a Video asset viewing device operator may do in regard to a Video asset viewing device and where such activity can be captured by the video asset viewing device or by the video server delivering video content through a network that supports the device. Nonlimiting examples include:
Viewer means the human being causing a Viewer interaction; the user of a Set-top box or a Video asset viewing device.
When reading the information below, it can be appreciated that these are merely samples of table layouts, format and content, and many aspects of these tables may be varied or expanded within the scope of the embodiment. The table layouts, field formats and content, algorithms, and other aspects are what I presently contemplate for this embodiment, but other table layouts, field formats and content, algorithms, etc. can be used. The algorithms are samples and various aspects of the algorithms may be varied or expanded within the scope of the embodiment.
In one embodiment the MapReduce Aggregation Engine 200 can be implemented on computer clusters running a standard Hadoop distribution from Apache under the Linux operating system. The MapReduce Aggregation Engine 200 can be implemented in JAVA or Pig. The reader may find more information about various Apache open source projects from The Apache Software Foundation at http://apache.org. Pig is a dataflow scripting language used to run data flows on Hadoop. Pig uses the Hadoop Distributed File System and the Hadoop processing system which is MapReduce. Pig is an Apache open source project. The reader may find more information about Pig at http://pig.apache.org. Those skilled in the art will readily recognize these tools.
Note on Media Measurement Data Model
Cable Television Laboratories, Inc. has published an “Audience Data Measurement Specification” as “OpenCable™ Specifications, Audience Measurement, Audience Measurement Data Specification” having Document Control Number “OC-SP-AMD-I01-130502” copyright© Cable Television Laboratories, Inc. 2013 which describes a Media Measurement Data Model (MMDM) database design which can be used as a source of data for the MapReduce Aggregation Engine 200 which I teach how to build in this Application. The teaching in my present application can be implemented in conjunction with that Media Measurement Data Model or with any number of data models as long as the required input data is provided as described herein.
Additionally, my MapReduce Aggregation Engine 200 creates files which may be used to load additional tables in a Media Measurement Data Model such as the one published by Cable Television Laboratories, Inc. These files are described in
Note: Numbering in the Drawings—The numbers in the drawings are usually, but not always, in sequential order.
In this nonlimiting example, the purpose is not to describe in detail the operations of a video content delivery network or a data collection process, but simply to show how the data that is collected from that system can be made available to my MapReduce Aggregation Engine 200.
It begins with Viewer Viewing Linear Content 9200 who is interacting with a set-top box 9210 and television 9220 as he views linear content. The set-top box 9210 interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
It continues with Viewer Viewing DVR Content 9202 who is interacting with a set-top box 9210 and television 9220 as he interacts with DVR content, recording content and playing back recorded content using various modes including trick plays. The set-top box 9210 interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
It continues with Viewer Viewing VOD Content 9203 who is interacting with a set-top box 9210 and television 9220 as he interacts with VOD content, playing the content using various modes including trick plays. The set-top box 9210 interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
It continues with Viewer viewing video content using tablet, smart phone, IP TV, or other video video viewing device 9204 who is interacting with a variety of Video Viewing Devices 9212, including but not limited to tablet, smart phone, IP TV, PC, etc. The video viewing device interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
Video Content Delivery System 9250 then interacts with a Viewer Interaction Data, Data Collection System 9260 which collects all manner of viewer interaction data including Linear viewing including time-shifted linear viewing, Digital Video Recorder recording and playback/viewing, and Video on Demand viewing. The Viewer Interaction Data, Data Collection System 9260 then processes the data as needed to load it to a Media Measurement Data Base 100. The data in the Media Measurement Data Base 100 can then be used as input to my Aggregation Engine 200 as described in
As noted previously, the video program viewing activity may be sourced from a Media Measurement Database such as the one described in the Cable Television Laboratories, Inc. specification. The populating of the Media Measurement Database 100 is beyond the scope of this application and so only brief remarks will be made in reference to that. There are video viewing data collection systems that are commonly used in the industry for collecting channel tuning or video viewing activity data including switched digital video systems, set top box applications, internet protocol video viewing applications, and other video viewing applications. I have discussed these in detail in my prior applications. These systems enable the collection of the video viewing events which can be loaded to a Media Measurement Database 100. From such a database, Video Program Viewing Activity Data can be extracted in a format similar to that shown in
Proceeding with the review of
This data file may contain three types of viewing activity depending on the query defined by the analyst. The three types are:
Sessionized Linear Viewing Activity (LVA)—Sessionized linear viewing activity is derived from linear tuning events. For linear tuning events which fit within program boundaries, the tuning event may be enriched as described in the Cable Television Laboratories, Inc. specification. For linear tuning events which cross program boundaries, the tuning event is divided into shorter duration tuning events as needed to fit within program boundaries as described in the specification; they are also enriched with additional fields. Because the viewing activity has been transformed to fit within program boundaries, I refer to it as program-based viewing. Linear viewing activity may be extracted from the Cable Television Laboratories, Inc. MMDM or from any source that is able to provide the data in a format suitable for this process.
A non-limiting example will help to explain this:
Device 100 has a linear tuning event consisting of a Tune to ABC at 6:55:00 PM and tune away from ABC at 8:07:59 PM. Assume a program schedule on ABC of News from 6:30:00 PM to 6:59:59 PM, followed by Action Show from 7:00:00 PM to 7:59:59 PM, followed by Sports Show from 8:00:00 PM to 8:29:59 PM. Using this sample data set, we see that the linear tuning event from Device 100 can be sessionized as follows:
This is defined in more detail in the specification that Cable Television Laboratories, Inc. has published as “Audience Data Measurement Specification” referred to previously.
The same video content may air multiple times, so when viewing is being measured it is necessary to track the Program information, Program Airing information, and Channel information along with other fields.
Digital Video Recording Viewing Activity (DVR)—DVR viewing activity may be extracted from the Cable Television Laboratories, Inc. MMDM or from any source that is able to provide the data in a format suitable for this process. As a non-limiting example, a PROGRAM may be a baseball game and a PROGRAM_AIRING may be the initial airing of the game followed by a replay later that day (thus two airings). Because a DVR asset airs on a certain channel at a certain time, DVR assets are also linked with Channel.
When the viewer records a program, they may record any of several airings. Also the program my air on different channels for the original and the replay. For these reasons, the DVR recording and subsequent playback must identify the Program information, Program Airing information, and Channel information along with other fields.
Video on Demand Viewing Activity (VOD)—VOD viewing activity is extracted from the Cable Television Laboratories, Inc. MMDM or from any source that is able to provided the data in a format suitable for this process. As a non-limiting example, a program may be any VOD asset, a movie, a sporting event, an online class, etc. The concept of a PROGRAM_AIRING does not apply to VOD. As to channel, because a VOD Program airs on the preassigned VOD channel, the channel information is not particularly informative in the context of VOD, so it is ignored for purposes of this Application.
These three types of viewing, LVA, DVR, and VOD, all share the characteristic that for measurement purposes, the measuring activity must be based on position in the content rather than a time element such as UTC time or local time. Measuring viewing activity based on position in the content enables creation of metrics having the same basis across LVA, DVR, and VOD viewing. Furthermore, when measuring based on position in the content, it is necessary to use a fixed reference point such as the actual start of the content. To illustrate, for a DVR recording, not every viewer will record the content beginning at the start of the program. So using position in the recording for measurement would not yield comparable results. Instead it is required to use a fixed reference point (the start of the content), in the actual content, not the start of the recording, and measure viewing from that fixed reference point. By using this fixed reference point, we can measure consistently regardless of whether the user begins recording at the beginning or some other point in the content. The same concept is applicable to LVA and VOD viewing.
Resuming with the review of
Proceeding with the review of
The computer algorithm that the Data Explosion Process 140 runs to create the Video Program Viewing Detail File 150 is as follows:
Looping Process to Create the Video Program Viewing Detail Records:
The explosion process can be run in an alternative manner to achieve the same result. I have included this alternative embodiment.
If the activity duration is provided, the looping construct can be done as follows:
Note: In each case the Video Program Viewing Detail File 150 records can be written directly to the Hadoop Distributed File System (HDFS) so that the video program viewing detail records are ready for use by the MapReduce distributed computing framework.
Note:
The Video Program Viewing Activity Data File 130 can be provided by the Extract 120 process in any computer readable format including, but not limited to, database tables, flat files, JSON messages, and XML messages. Alternatively, such video viewing events can be collected directly from the source without the need for a Media Measurement Database 100. In such a case, those events can still be provided as video viewing activity in a format similar to that shown in
For each of these embodiments, at the completion of Data Explosion Process 140, one record has been written to the Video Program Viewing Detail File 150 for each second of the viewing duration represented in the video program viewing activity record. The Sample Data in
Those skilled in the art will readily recognize that the Data Explosion Process 140 is suitable for running in parallel on multiple computers simultaneously with each process creating Video Program Viewing Detail File records that can be fed into the MapReduce Aggregation Engine 200.
Proceeding with the review of
The MapReduce process can be coded in JAVA or in Pig. I have coded this in Pig. The code below can be used to create the four output files reviewed in the Drawings (
Using these outputs, the reader will have a comprehensive set of aggregated video viewing metrics. The reader should recognize that the aggregation logic shown below provides several illustrations of what can be done. Additional aggregation combinations will be obvious to those skilled in the art.
Creating the Aggregated Sessionized Linear Viewing Activity (LVA) File 220
The Pig Latin coding to create the Aggregated Sessionized Linear Viewing Activity (LVA) File 220 is shown next.
This summarization aggregates Sessionized Linear Viewing Activity for each combination of Program, Program Airing, Channel, Geographic, Demographic, and Date by the Playback Mode. The result provides a summary of Sessionized Linear viewing for each second of the program content with the viewing activity broken out by the playback mode (PL, F1, F2, R1, R2, etc.).
As a nonlimiting example, if a program was 1800 seconds (30 minutes) duration, the analyst would have counts of how many devices viewed second 0 in PL mode, then second 1 in PL mode, etc. to second 1799. Additionally, he would have similar data for each of the other playback modes (F1, F2, R1, R2, etc.) for each of the 1800 seconds of the program. By matching this data with the content being shown, the analyst can now chart how many devices saw each second of the content in the respective playback modes.
Note: VIEWING_TYPE 1290 is carried forward to identify the type of viewing when files are combined downstream.
Note: A sample of the file created by the aggregation is shown in
LVA Alternative Query 1—Aggregate Across Demographic Groups.
By changing LVA_D statement above to the following, the analyst can aggregate across demographic groups. As a non-limiting example, this may omit any details related to demographic breakouts such as age or income.
LVA Alternative Query 2—Aggregate Across Geographic Groups.
By changing LVA_D statement above to the following, the analyst can aggregate across geographic groups. As a non-limiting example, this may omit any details related to geographic breakouts such as zip code or marketing area.
LVA Alternative Query 3—Aggregate Across Demographic and Geographic Groups.
By changing LVA_D statement above to the following, the analyst can aggregate across demographic and geographic groups.
LVA Alternative Query 4—Aggregate Across Demographic and Geographic Groups and Dates.
By changing LVA_D statement above to the following, the analyst can aggregate across demographic and geographic groups and viewing dates and program airings. As a non-limiting example, this may aggregate viewing across the two airings of a baseball game that occur on different dates with no detail breakouts for demographic or geographic groupings.
Recap of Alternative Queries
Those skilled in the art will have no difficulty creating addition aggregation groupings as needed.
Creating the Aggregated DVR Viewing Activity (DVR) File 230
The Pig Latin coding to create the Aggregated DVR Viewing Activity (DVR) File 230 is shown next.
This summarization aggregates Digital Video Recording Viewing Activity for each combination of Program, Program Airing, Channel, Geographic, Demographic, and Date by the Playback Mode.
The result provides a summary of DVR viewing for each second of the program content with the viewing activity broken out by the playback mode (PL, F1, F2, R1, R2, etc.).
As a nonlimiting example, if a recorded program on the DVR was 1800 seconds (30 minutes) duration, the analyst would have counts of how many devices played back second 0 in PL mode, then second 1 in PL mode, etc. to second 1799. Additionally, he would have similar data for each of the other playback modes (F1, F2, R1, R2, etc.) for each of the 1800 seconds of the recorded program and the corresponding viewing. By matching this data with the content being shown, the analyst can now chart how many devices saw each second of the content in the respective playback modes.
Note: VIEWING_TYPE 1890 is carried forward to identify the type of viewing when files are combined downstream.
Note: A sample of the file created by the aggregation is shown in
DVR Alternative Query 1—Aggregate Across Demographic Groups.
By changing DVR_D statement above to the following, the analyst can aggregate across demographic groups. As a non-limiting example, this may omit any details related to demographic breakouts such as age or income.
DVR Alternative Query 2—Aggregate Across Geographic Groups.
By changing LVA_D statement above to the following, the analyst can aggregate across geographic groups. As a non-limiting example, this may omit any details related to geographic breakouts such as zip code or marketing area.
DVR Alternative Query 3—Aggregate Across Demographic and Geographic Groups.
By changing LVA_D statement above to the following, the analyst can aggregate across demographic and geographic groups.
DVR Alternative Query 4—Aggregate Across Demographic and Geographic Groups and Dates.
By changing LVA_D statement above to the following, the analyst can aggregate across demographic and geographic groups and viewing dates and program airings. As a non-limiting example, this may aggregate viewing across the two recorded airings of a baseball game that occur on different dates with no detail breakouts for demographic or geographic groupings.
Recap of Alternative Queries
Those skilled in the art will have no difficulty creating addition aggregation groupings as needed.
Creating the Aggregated VOD Viewing Activity (VOD) File 240
The Pig Latin coding to create the Aggregated VOD Viewing Activity (VOD) File 240 is shown next.
This summarization aggregates Video On Demand Viewing Activity for each combination of Program, Geographic, Demographic, and Date by the Playback Mode. Note: VOD does not use Program Airing or Channel in the same way that linear and DVR use this, so those fields are not included in VOD. The result provides a summary of VOD viewing for each second of the program content with the viewing activity broken out by the playback mode (PL, F1, F2, R1, R2, etc.).
As a nonlimiting example, if a Video On Demand lease was for a program that was 1800 seconds (30 minutes) duration, the analyst would have counts of how many devices played second 0 in PL mode, then second 1 in PL mode, etc. to second 1799. Additionally, he would have similar data for each of the other playback modes (F1, F2, R1, R2, etc.) for each of the 1800 seconds of the VOD program and the corresponding viewing. By matching this data with the content being shown, the analyst can now chart how many devices saw each second of the content in the respective playback modes.
Note: VIEWING_TYPE 1290 is carried forward to identify the type of viewing when files are combined downstream.
Note: A sample of the file created by the aggregation is shown in
VOD Alternative Query 1—Aggregate Across Demographic Groups.
By changing VOD_D statement above to the following, the analyst can aggregate across demographic groups. As a non-limiting example, this may omit any details related to demographic breakouts such as age or income.
VOD Alternative Query 2—Aggregate Across Geographic Groups.
By changing VOD_D statement above to the following, the analyst can aggregate across geographic groups. As a non-limiting example, this may omit any details related to geographic breakouts such as zip code or marketing area.
VOD Alternative Query 3—Aggregate Across Demographic and Geographic Groups.
By changing VOD_D statement above to the following, the analyst can aggregate across demographic and geographic groups.
VOD Alternative Query 4—Aggregate Across Demographic and Geographic Groups and Dates.
By changing VOD_D statement above to the following, the analyst can aggregate across demographic and geographic groups and viewing dates. As a non-limiting example, this will aggregate all the Program's VOD viewing activity as represented in the input file with no detail breakouts.
Recap of Alternative Queries
Those skilled in the art will have no difficulty creating addition aggregation groupings as needed.
Creating the Aggregated Video Program Viewing File 250
The Pig Latin coding to create the Aggregated Video Program Viewing File 250 is shown next. This summarization aggregates viewing activity across sessionized linear viewing activity (LVA), digital video recording viewing activity (DVR), and video on demand viewing activity (VOD) as these are provided in the input data. The viewing can then be grouped by each combination of Program, Geographic, Demographic, and Date by the Playback Mode. The result provides a summary of overall viewing from the various sources (LVA, DVR, VOD) for each second of the program content with the viewing activity broken out by the playback mode (PL, F1, F2, R1, R2, etc.) for the data as represented in the input data. As a nonlimiting example, this aggregation will provide insight into total viewing activity during each second of the program content.
This data can be used to create an overall picture of the viewing activity. It can also be used to determine as the denominator in calculations which compare the percentage of LVA viewing or DVR viewing or VOD viewing to overall viewing of this content.
In this example (in part TOTAL_C) viewing date is omitted to allow aggregation across all the viewing dates.
Note: A sample of the file created by the aggregation is shown in
TOTAL Alternative Query 1—Aggregate Across Demographic Groups.
By changing TOTAL_C statement above to the following, the analyst can aggregate across demographic groups. As a non-limiting example, this may omit any details related to demographic breakouts such as age or income.
VOD Alternative Query 2—Aggregate Across Geographic Groups.
By changing TOTAL_C statement above to the following, the analyst can aggregate across geographic groups. As a non-limiting example, this may omit any details related to geographic breakouts such as zip code or marketing area.
VOD Alternative Query 3—Aggregate Across Demographic and Geographic Groups.
By changing TOTAL_C statement above to the following, the analyst can aggregate across demographic and geographic groups.
Recap of Alternative Queries
Those skilled in the art will have no difficulty creating addition aggregation groupings as needed.
This Concludes Discussion on
Discussion of
The
In each case, the top row of the data table contains an abbreviated version of the field name. The second row of the chart contains the field number reference.
I have included enough sample data to clearly teach how the computer driven data explosion process will perform its task to generate the various detail records needed to represent the second-by-second viewing activity that is represented in the viewing activity data. Thus the reader will observe a variety of playback modes with varying durations.
Additionally, I have included two different program airings that are applicable to linear and DVR so that the aggregation process will then be able to aggregate across these to demonstrate what the computer is doing to achieve aggregation.
The
There is Summary Information followed by the Data Structure including field definitions, as shown in
Discussion of
The
This sample data does not contain all of the Program Viewing Detail records that are created by the Data Explosion Process based on the inputs in
Overview of
Discussion of
The
The reader will observe that for each set of aggregated viewing activity, along with the identifying fields, there are PLAYBACK_MODE 1710, PLAYBACK_POSITION 1720, and PLAYBACK_MODE COUNT 1730. As noted in the definitions, the Playback Mode identifies the type of viewing activity, the position identifies the location within the content, and the count identifies the number of devices viewing that second of content in that mode.
The reader will observe that for each Playback Mode 1710 there is a complete list of the Playback Positions 1720 or viewing seconds when that content was viewed along with the number of devices viewing the content as shown in Playback mode count 1730. Thus, the computer has aggregated the Video Program Viewing Detail data 150 to create this Aggregated Sessionized Linear Viewing Activity (LVA) File 220. This output file has value on its own, or it can be loaded to a data store for additional downstream analytics.
The reader will also observe that the Linear Viewing data can be aggregated across program airings in order to create a higher level aggregation of viewing activity. This is shown under the title Linear Program 1 Airing 1 and Linear Program 1 Airing 2 Combined. This aggregation, as a non-limiting example, provides insight into viewing of the content across multiple airings.
There is Summary Information followed by the Data Structure including field definitions, as shown in
Discussion of
The
The reader will note the sample data is organized by Program 1 Airing 1 and then Program 1 Airing 2 and then Program 1 Airing 1 and 2 combined. Within these groupings, there are sections for only playback mode of PL to conserve space (playback modes F1, F2, R1, R2 are not shown). The pattern of how to aggregate this data is very similar to that shown in
The reader will observe that for each set of aggregated viewing activity, along with the identifying fields, there are PLAYBACK_MODE 1910, PLAYBACK_POSITION 1920, and PLAYBACK_MODE COUNT 1930. As noted in the definitions, the Playback Mode identifies the type of viewing activity, the position identifies the location within the content, and the count identifies the number of devices viewing that second of content in that mode.
The Program Airing based examples (Program 1 Airing 1 and Program 1 Airing 2) illustrate how the computer would aggregate DVR viewing across multiple homes and/or devices to produce the aggregated second-by-second DVR viewing activity for a program airing for each playback mode.
The Program 1 Airing 1 and 2, Combined example illustrates how the computer will aggregate viewing activity across program airings to produce a total DVR viewing for the program for each playback mode.
This output file has value on its own, or it can be loaded to a data store for additional downstream analytics.
There is Summary Information followed by the Data Structure including field definitions, as shown in
Discussion of
The
This output file has value on its own, or it can be loaded to a data store for additional downstream analytics.
There is Summary Information followed by the Data Structure including field definitions, as shown in
Discussion of
The
This output file has value on its own, or it can be loaded to a data store for additional downstream analytics.
Although the description above contains much specificity, this should not be construed as limiting the scope of the embodiments but as merely providing illustrations of some of several embodiments. As a nonlimiting example, additional qualifiers may be added along with those provided. Additional aggregations can be done using other combinations of these identifiers.
Scope of Viewer Interaction Data Loaded
I presently contemplate that the MapReduce Aggregation Engine 200 will process viewer interaction data for whatever set of viewing activity is provided to it. This may be one Video Program at a time, one hour of the day, a primetime television viewing period, an entire 24 hour day of viewing, a week of viewing, a weekly program schedule time slot, or another time period decided by the analyst. Another embodiment may simply process viewing activity within the context of a single program, or a single advertisement, or some other combination.
Use of Hadoop and MapReduce
I presently contemplate that the MapReduce Aggregation Engine 200 will take advantage of Hadoop MapReduce technology. Another embodiment may load the Video Program Viewing Detail records to a relational database and run various SQL queries to produce results similar to those shown.
Data Explosion Process
I presently contemplate that the Data Explosion Process 140 will generate one record for each second of viewing activity with the field Count_of_1 1390 having a value of 1. This method provides flexibility in terms of possible data aggregation, but it does consume more resources to process all the records. In another embodiment logic could be added to the Data Explosion Process 140 so that various records end up with a count of 2 or 3 or some higher number reflecting that multiple devices were viewing that content. The outcome is that fewer records would be sent into the MapReduce Aggregation Engine 200 thus reducing the load on the system.
Identifiers for Data
I presently contemplate using a combination of numeric and mnemonics for the various fields such as program info, program airing info, channel info, house info, device info, viewer info, geographic info, demographic info, viewing type, video server identifiers, system health info, and other similar fields, but another embodiment could use only numeric values as identifiers with links to reference tables for the descriptions of the numeric identifiers or only mnemonic identifiers.
Data Explosion Process
I presently contemplate that the Data Explosion Process 140 will generate one record for each second of the tuning activity. Another embodiment may generate one record for each video frame of viewing activity. In this case, the playback begin position and playback end position would be replaced by a frame number for each of these fields.
Yet another embodiment may generate records at a one minute level with the count being the number of seconds tuned to the content during that minute (in this case there would be 1,440 possible one minute intervals during a 24 hour day).
Yet another embodiment may generate records at a 10-second level with the count being the number of seconds tuned to the content during that 10-second interval (in this case there would be 8,640 possible 10-second intervals during a 24 hour day).
Programming Algorithm Scope
I presently contemplate executing the algorithms described herein separately in some sequence, but another embodiment could combine multiple simple algorithms into fewer complex algorithms.
Receiving Date and Time Information
I presently contemplate receiving all of the date and time values in local time, but another embodiment may provide these in Coordinated Universal Time (UTC time).
General Information
I presently contemplate using variables having the data types and field sizes shown, but another embodiment may use variables with different data types and field sizes to accomplish a similar result.
I presently contemplate tracking viewing activity at the granularity of one second, but another embodiment may track viewing activity at a finer granularity, perhaps half-second, or tenth-second, or millisecond. Yet another embodiment may receive data at a granularity finer than one second and round to the nearest second for use by the MapReduce Aggregation Engine 200.
I presently contemplate using record layouts similar to those defined herein, but another embodiment may use a different record layout or record layouts to accomplish a similar result. As a nonlimiting example, another embodiment may use database tables or other objects instead of record layouts similar to those I have defined herein to accomplish a similar result while still working within the spirit and scope of this disclosure.
Implementation Information
I presently contemplate using the generic Apache Hadoop distribution, but another embodiment may use a different Hadoop distribution.
I presently contemplate using Linux operating system, but another embodiment may use a different operating system.
I presently contemplate using the Pig along with the Pig Latin dataflow language, but another embodiment may use Java or Python or some other language alone or in combination with Pig Latin.
It will be apparent to those of ordinary skill in the art that various changes and modifications may be made which clearly fall within the scope of the embodiments revealed herein. In describing an embodiment illustrated in the drawings, specific terminology has been used for the sake of clarity. However, the embodiments are not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar purpose.
In general, it will be apparent to one of ordinary skill in the art that various embodiments described herein, or components or parts thereof, may be implemented in many different embodiments of software, firmware, and/or hardware, or modules thereof. The software code or specialized control hardware used to implement some of the present embodiments is not limiting of the present embodiment. For example, the embodiments described hereinabove may be implemented in computer software using any suitable computer software language type such as, for example, Python or JAVA using, for example, conventional or object-oriented techniques. Such software may be stored on any type of suitable computer-readable medium or media such as, for example, a magnetic or optical storage medium. Thus, the operation and behavior of the embodiments are described in Pig Latin dataflow language purely as a matter of convenience. It is clearly understood that artisans of ordinary skill would be able to design software and control hardware to implement the embodiments presented in the language of their choice based on the description herein with only a reasonable effort and without undue experimentation.
The processes associated with the present embodiments may be executed by programmable equipment, such as computers. Software or other sets of instructions that may be employed to cause programmable equipment to execute the processes may be stored in any storage device, such as, for example, a computer system (non-volatile) memory, a compact disk, an optical disk, magnetic tape, or magnetic disk. Furthermore, some of the processes may be programmed when the computer system is manufactured or via a computer-readable medium.
It can also be appreciated that certain process aspects disclosed herein may be performed using instructions stored on a computer-readable memory medium or media that direct a computer or computer system to perform process steps. A computer-readable medium may include, for example, memory devices such as diskettes, compact discs of both read-only and read/write varieties, optical disk drives, memory sticks, and hard disk drives. A computer-readable medium may also include memory storage that may be physical, virtual, permanent, temporary, semi-permanent and/or semi-temporary.
In various embodiments disclosed herein, a single component or algorithm may be replaced by multiple components or algorithms, and multiple components or algorithms may be replaced by a single component or algorithm, to perform a given function or functions. Except where such substitution would not be operative to implement the embodiments disclosed herein, such substitution is within the scope presented herein. Thus any element expressed herein as a means or a method for performing a specified function is intended to encompass any way of performing that function including, for example, a combination of elements that performs that function. Therefore, any means or method that can provide such functionalities may be considered equivalents to the means or methods shown herein.
It can be appreciated that the “data analysis computer system” may be, for example, any computer system capable of running MapReduce, whether it be a one node system or a system with thousands of nodes. In an alternative embodiment, it may be a relational database server.
While various embodiments have been described herein, it should be apparent, however, that various modifications, alterations and adaptations to those embodiments may occur to persons skilled in the art with the attainment of some or all of the advantages described herein. The disclosed embodiments are therefore intended to include all such modifications, alterations and adaptations without departing from the scope and spirit of the embodiments presented herein as set forth in the appended claims.
Accordingly, the scope should be determined not by the embodiments illustrated, but by the appended claims and their legal equivalents.
In my previous Applications, I have identified numerous Conclusions, Ramifications, and Scope items. Many of those are similar for this application. The Conclusions, Ramifications, and Scope items from my U.S. Pat. No. 8,365,212 B1 issued on Jan. 29, 2013, and my U.S. Pat. No. 8,365,213 B1 issued on Jan. 29, 2013, and my U.S. application Ser. No. 13/360,704 filed on Jan. 28, 2012, and my U.S. application Ser. No. 13/567,073 filed on Aug. 5, 2012 and my U.S. application Ser. No. 13/740,199 filed on Jan. 13, 2013 are included herein by reference but not admitted to be prior art.
In this discussion below, I will focus on new ramifications introduced by this application.
From the description above, a number of advantages of some embodiments of my MapReduce Aggregation Engine 200 and its supporting processes become evident:
In this specification I have taught how to measure or analyze video program viewing activity at a second-by-second level using the Hadoop MapReduce framework. This teaching allows measurement of linear program viewing, DVR viewing, and VOD viewing with the ability to understand how the viewer uses “trick plays” when viewing this content. This insight gained from this can be valuable to advertisers, content producers, and content providers. As a non-limiting example, this provides detailed measurements into the amount of fast forward activity that occurs during commercials. Additionally, by teaching how to include numerous identifiers and then aggregate the detailed viewing activity to various combinations of those identifiers, an analyst will now be able to slice-and-dice the data in numerous ways to understand how different content is consumed by various people in various geographic locations.
Once the metrics are calculated, the resulting files can be loaded to a database for longitudinal analysis. As a nonlimiting example, the program level metrics can be tracked to identify week-to-week activity such as, for example, total seconds viewed in Play mode. Then the more detailed metrics can provide additional insight into the causes behind the overall trends.
The ability to produce these metrics using the Hadoop MapReduce framework provides a new tool for data analysts to use in understanding viewing behavior.
This method of using the Hadoop MapReduce framework to calculate second-by-second viewing activity for program based viewing such as linear, DVR, and VOD by aggregating individual viewing records that were created by exploding the viewing period into individual records where each record represents one second of viewing activity is contrary to the teaching of those who work with start time and duration (seconds viewed). Thus I am able to solve problems previously found insolvable when limited to using the existing techniques. I am able to provide metrics that could not be produced using existing techniques.
Additionally, I have shown how this teaching is application to online education for use in analyzing video viewing in that context.
Subsequent Usage of the Metrics
The metrics produced by the MapReduce Aggregation Engine 200 readily lend themselves to dimensional analysis using contemporary data warehouse methods. I have reviewed this extensively in my prior applications.
The metrics produced by the MapReduce Aggregation Engine 200 can be loaded to a data warehouse to support additional longitudinal analysis beyond what is done by the Engine 200. Thus we can readily envision a myriad of uses for the metrics produced by the MapReduce Aggregation Engine 200.
Numerous additional metrics can readily be identified by those skilled in the art. Additionally, numerous additional uses for the metrics identified herein will be readily evident to those skilled in the art.
In accordance with one embodiment, I have disclosed a computer-implemented method of using video program viewing activity data as input to an aggregation engine built on the Hadoop MapReduce framework which calculates second-by-second video viewing activity for Linear, DVR, and VOD program level viewing including measuring trick play activity with results aggregated to the analyst's choice of program, program airing, channel, house attributes, device attributes, geographic area, demographic attributes, viewing date, or any combination of these fields, for each second of program content represented in the video program viewing data. The source data may be extracted from a database defined according to the Cable Television Laboratories, Inc. Media Measurement Data Model defined in “Audience Data Measurement Specification” as “OpenCable™ Specifications, Audience Measurement, Audience Measurement Data Specification” document OC-SP-AMD-I01-130502 or any similar format. These metrics provide detailed data needed to calculate information on customer viewing behavior that can drive business decisions for service providers, advertisers, and content producers. The ability to use the Hadoop MapReduce framework to aggregate this data will meet pressing needs for detailed audience viewership information that is not presently available and thus the metrics will be of great value to the industry.
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