Abstract: A computer-implemented method of using video viewer interaction data as input to a measurement engine which then calculates: demographic level and aggregate level (a) household reach for advertisements and video content based on user defined reach periods, (b) One-minute, Thirty-minute, and Sixty-minute channel viewing metrics, and (c) One-minute, Thirty-minute, and Sixty-minute aggregate viewing metrics, all based on second-by-second household level viewing activity. Also shows creation of Commercial Schedule Cumulative Audience, Impressions for Reach Periods, Demographic Group Impressions, Average Audience, Average Audience Share, Average Rating, Households Using Television, and Households Using Television Percent using household level data. Works with IP and linear video delivery. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: Methods, systems and apparatuses are described for using Linear, DVR, and VOD video viewing activity data for more efficient downstream processing to create analytical studies of second-by-second viewing activity for program, channel, house, device, viewer, demographic, and geographic attributes. Such attributes may be determined for one or more viewing devices. Attributes associated with different viewing devices may be replaced with a common substitute value. Video viewing metrics associated with a common substitute value may be determined.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: A computer-implemented method of using channel tuning data from a video asset viewing device connected to a network to measure video asset viewing at a second-by-second level during one or more user defined lead-in periods, and then correlating that with video asset viewing during a user defined target period, for the purpose of analyzing how viewing activity during the lead-in period(s) correlates with viewing activity during the target period, thus producing longitudinal viewing metrics; all while maintaining viewer anonymity. Additionally, viewing metrics can be categorized based on user defined demographic, geographic, and histogram groupings representing the percentage of video asset viewing with the result that the analyst is able to gain detailed insight into customer viewing behavior. The lead-in video asset may be any video asset or assets. The target may be any subsequent video asset. The metrics produced are useful to service providers, advertisers, and content producers.

Abstract: A computer-implemented method of using Linear, DVR, and VOD video viewing activity data as input to a data translation processor which prepares that viewing activity for more efficient downstream processing by translating detailed values to aggregated values according to analyst defined translation rules in preparation for ingestion by a MapReduce Framework with the result that the MapReduce Framework needs to process less data in order to create analytical studies of second-by-second viewing activity for program, channel, house, device, viewer, demographic, and geographic attributes. 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-101-130502 or any similar format.

Abstract: A computer-implemented method of using channel tuning data from a video asset viewing device connected to a network to measure video asset viewing at a second-by-second level during one or more user defined lead-in periods, and then correlating that with video asset viewing during a user defined target period, for the purpose of analyzing how viewing activity during the lead-in period(s) correlates with viewing activity during the target period, thus producing longitudinal viewing metrics; all while maintaining viewer anonymity. Additionally, viewing metrics can be categorized based on user defined demographic, geographic, and histogram groupings representing the percentage of video asset viewing with the result that the analyst is able to gain detailed insight into customer viewing behavior. The lead-in video asset may be any video asset or assets. The target may be any subsequent video asset. The metrics produced are useful to service providers, advertisers, and content producers.

Abstract: A computer-implemented method of using video viewer interaction data as input to a measurement engine which then calculates: demographic level and aggregate level (a) household reach for advertisements and video content based on user defined reach periods, (b) One-minute, Thirty-minute, and Sixty-minute channel viewing metrics, and (c) One-minute, Thirty-minute, and Sixty-minute aggregate viewing metrics, all based on second-by-second household level viewing activity. Also shows creation of Commercial Schedule Cumulative Audience, Impressions for Reach Periods, Demographic Group Impressions, Average Audience, Average Audience Share, Average Rating, Households Using Television, and Households Using Television Percent using household level data. Works with IP and linear video delivery. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.

Abstract: A computer-implemented method of using video viewer interaction data as input to a measurement engine which then calculates: demographic level and aggregate level (a) household reach for advertisements and video content based on user defined reach periods, (b) One-minute, Thirty-minute, and Sixty-minute channel viewing metrics, and (c) One-minute, Thirty-minute, and Sixty-minute aggregate viewing metrics, all based on second-by-second household level viewing activity. Also shows creation of Commercial Schedule Cumulative Audience, Impressions for Reach Periods, Demographic Group Impressions, Average Audience, Average Audience Share, Average Rating, Households Using Television, and Households Using Television Percent using household level data. Works with IP and linear video delivery. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: System and methods for determining second-by-second viewing activity and viewing metrics associated with video assets output by a plurality of video-viewing devices. The viewing activity may include a viewing mode by which one or more video viewing devices output a segment of a video asset. The viewing metrics may include a viewing count for a segment of the video asset that is associated with a viewing mode by which the one or more video viewing devices output the video segment. These metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.

Abstract: A computer-implemented method of using video viewing activity data as input to an aggregation engine built on the Hadoop MapReduce framework which calculates second-by-second video viewing activity aggregated to the analyst's choice of (a) geographic area, (b) video server, (c) video content (channel call sign, video program, etc.), or (d) viewer demographic, or any combination of these fields, for each second of the day represented in the video viewing activity data. Also calculates overall viewing for use as a denominator in calculations. 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-101-130502 or any similar format.

Abstract: A computer-implemented method of using video viewing activity data as input to an aggregation engine built on the Hadoop MapReduce framework which calculates second-by-second video viewing activity aggregated to the analyst's choice of (a) geographic area, (b) video server, (c) video content (channel call sign, video program, etc.), or (d) viewer demographic, or any combination of these fields, for each second of the day represented in the video viewing activity data. Also calculates overall viewing for use as a denominator in calculations. 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-101-130502 or any similar format.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: A computer-implemented method of using video viewing activity data as input to an aggregation engine built on the Hadoop MapReduce framework which calculates second-by-second video viewing activity aggregated to the analyst's choice of (a) geographic area, (b) video server, (c) video content (channel call sign, video program, etc.), or (d) viewer demographic, or any combination of these fields, for each second of the day represented in the video viewing activity data. Also calculates overall viewing for use as a denominator in calculations. 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.

Abstract: A computer-implemented method of using video program viewer interaction data that has been loaded to a media measurement database as input to a measurement engine which then calculates Linear, DVR, and VOD asset viewing activity at three levels: (a) Video Program, (b) Video Program Airing, (c) Video Program Airing Segment, where each level provides summary metrics for groupings of Demographic, Geographic, and/or Device Characteristic, and also second-by-second viewing metrics, including counting advertising impressions, within the Demographic, Geographic, Device groupings. System also accounts for reduced value of ad viewing when viewer is using trick plays or when viewer delays viewing recorded content. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers. Additionally, a viewing histogram analysis is produced.

Abstract: A computer-implemented method of using video viewing activity data as input to an aggregation engine built on the Hadoop MapReduce framework which calculates second-by-second video viewing activity aggregated to the analyst's choice of (a) geographic area, (b) video server, (c) video content (channel call sign, video program, etc.), or (d) viewer demographic, or any combination of these fields, for each second of the day represented in the video viewing activity data. Also calculates overall viewing for use as a denominator in calculations. 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.