Abstract: Various of the disclosed embodiments contemplate television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In some embodiments, a group of set top boxes can be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, some embodiments can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, e.g. based on a weekly or an approximately monthly frequency. In some embodiments, the aberrant viewership can be detected by calculating a minimum expected number of viewers for a day and comparing it to the actual number of households that reported viewers for that day.

Abstract: A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.

Abstract: A system and methods to calculate ratings of television advertisements based on viewing data and to assess the effectiveness of the advertisements. In some embodiments, consumer viewing data is consolidated across a plurality of disparate channels through which content and advertisements are presented to viewers. Some of the channels may provide information directly to the system about which advertisements were presented to viewers. In some embodiments, the system may instead determine whether an advertisement was likely viewed based on scheduling data associated with the presentation of content and advertisements. The system may provide additional algorithms for rating advertisements based on the consolidated information. That is, the system may allow performance of advertisements to be compared against other advertisements across a desired population of viewers. Embodiments may further provide information for a graphical user interface to assess the ratings and results.

Abstract: A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.

Abstract: Various of the disclosed embodiments contemplate television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In some embodiments, a group of set top boxes can be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, some embodiments can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, e.g. based on a weekly or an approximately monthly frequency. In some embodiments, the aberrant viewership can be detected by calculating a minimum expected number of viewers for a day and comparing it to the actual number of households that reported viewers for that day.

Abstract: A system and methods to calculate ratings of television advertisements based on viewing data and to assess the effectiveness of the advertisements. In some embodiments, consumer viewing data is consolidated across a plurality of disparate channels through which content and advertisements are presented to viewers. Some of the channels may provide information directly to the system about which advertisements were presented to viewers. In some embodiments, the system may instead determine whether an advertisement was likely viewed based on scheduling data associated with the presentation of content and advertisements. The system may provide additional algorithms for rating advertisements based on the consolidated information. That is, the system may allow performance of advertisements to be compared against other advertisements across a desired population of viewers. Embodiments may further provide information for a graphical user interface to assess the ratings and results.

Abstract: A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.

Abstract: A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.

Abstract: A method cleans television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In various aspects, the group of set top boxes may be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, the method can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, that is based on a weekly or an approximately monthly frequency. The aberrant viewership can be detected by calculating a minimum expected number of viewers for a day, and comparing it to the actual number of households that reported viewers for that day.