NON-ADDICTIVE SAMPLE BASED FACTS - Deetc

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• Semi-additive, can be summed up only for a subset of the dimensions; • Non-additive, cannot be summed for any of the dimensions. Non-additive does not directly relates with the measure being numerical; it relates with the usefulness (and accuracy) of the summed value. It may simply not make sense to the business. Ratios are an example of a non-addictive fact. The audience analysis domain shared with OLAP and Datawarehouse, all of the characteristics listed above. The volume of data are too similar in size. However, audience analysis world tends to be closed and proprietary oriented, and it’s applications do not apply, directly, generic OLAP techniques. Programs like [10] and [13] use pivot tables like interfaces, typical in OLAP, but do not use more of the OLAP reporting facilities. The former, [13], is developed by one of the leading software houses in audience analysis; it uses a proprietary text file format to store information, not an OLAP engine. In this paper, the authors evaluate the feasibility of general OLAP techniques applied to audience analysis domain. The purpose is to determine if it’s possible to use general, non proprietary solutions to achieve the same degree of freedom in audience analysis, as the referred tools. Particularly, we discuss a dimension model that addresses the singularity of the audience analysis performance indicators, almost all of them quota sample based and nonaddictive. Some of the necessary values to compute these indicators, mainly related to representativeness, are only known at runtime, after the users finished the analysis’ restrictions. One couldn’t find any related work, regarding dimension model and sample quota based facts. The main datawarehouse and olap literature [6, 9] do not address this kind of facts. And being a relative closed world, audience analysis are not an ease domain to build a state of the art [14]. The few articles publicly available are related to datamining and audience patterns analysis and not to datawarehouse and olap techniques appliance. This paper is organised as follows: section 2 describes the data, the requirements and some performance indicators used in audience analysis; section 3 present the dimensional model, using Kimball’s approach, for a generic TV content analysis datamart; finally, in section 4 the authors draw some conclusions of their work. 2 PROBLEM DOMAIN 2.1 The Data The meter system seldom produces the data for the entire viewers’ panel viewers, since several practical problems may occur which range from meter misuse, to data communication problems, which endanger the desired panel representativeness. In order to adjust the panel representativeness, a weighting procedure is used which attempts to correct undesirable non-representative data tendencies. This is the Rim Weighting algorithm [4] (also known as Iterative Proportional Fitting [1]), which provides a daily weight for each viewer trying to recover the panel sample representativeness. People meter data have three distinct components: (i) socio-demographic information; (ii) TV content data; (iii) visualisation data. The socio-demographic, which include characteristics such as age, occupation and social class, provide important information to determine the panel representativity, but may also be used for other purposes (mostly for advertisement). The TV content is characterized by their type of contents, duration and corresponding TV channels. Finally, meter data measures the viewing behavior of each one of the viewers, represented by a sequence of watch/non watch indicators referred to each second of the day, as illustrated by figure 1. These indicators are registered independently for each channel. Figure 1: Representation of a daily viewing pattern for an individual. 2.2 Requirements The requirements were gathered through the analysis of several audience reports, and for on site day by day usage of the audience analysis program Telereport[13]. The reports are sufficient broad to embrace a series of heterogeneous users, ranging from television to advertising. They cover the most important aspects of general TV content performance analysis. However, they do not address the singularities of advertising spots. The audience analyst are interested, mainly, to determine the audiences by channel
and/or program, for a set of targets and time periods; the analysis are never carried through individual viewers. Viewers are arranged in targets, that closely relate to socio-demographic information. For example, a typical target is the AB, consisting of high income viewers. Time periods range from minute by minute analysis, to standard audience periods, with 2 or more hours. Periods with more than a day, must be treated carefully, as the corrective weight change daily. More details can be found in the work of [3]. 2.3 Performance Indicators Audience analysis define more than a dozen performance indicators [3]. However, as a regular basis, audience analysts tend to use only a few. For demonstration purposes, these paper use only one: TV rating (rat). TV rating give us the percentage of the population that, for a given period, have watched a program/channel. It tell us the probability of an individual from the population become a viewer. Let P be the daily viewers’ panel and W the set of corrective weights. The rating for a single minute can be calculated as |V | ratminute = ∑ i=0 Vi ∗W(i),V ⊂ P (1) where V is the subset of individuals that watch television, on the a specific channel. (1) can be extend to accommodate a set of minutes M ratperiod = ∑ ratminute(m) m∈M (2) |M| However, (2) does not fully address the representativeness for a target. By definition, most of the performance indicators relate to a reference value and can be displayed as percentages. When one say that program X got a 20% rating, those are the percentage of the viewers from the panel that share the desire sociodemographic value, and happened to be watching TV during the analysis period. Let Tsd be the subset of the panel that share the desire socio-demographic values sd. One can determine the sum of the corrective weigths TW for the target as follows TW = ∑ W(i) (3) Tsd∈P Normalising (2) using (3) give us the representative value for the desired target rat = ratperiod (4) tw The rating calculated from (4) is valid measure, not only for the panel’s viewers, but also for the entire population of TV viewers. 3 THE MODEL 3.1 Dimensions From the requirements, each performance indicator is given context by: (i) Time, detailed to the minute; (ii) Date, giving access to the daily corrective weight; (iii) Program, the object of the performance analysis; and (iv) Socio-demographic information, enabling target analysis. Date is a regular dimension, whose characteristics and typical attributes can be observed in [9] and [6]. Time dimension represents the daily regularity, but also, typical time periods and a domain depended referential. The considered periods are the TV standard 1 : ”02h30-08h00”, ”08h00- 12h00”, ”12h00-14h30”, ”14h30-18h30”, ”18h30- 20h00”, ”20h00-23h00”, ”23h00-24h30”, ”24h30- 26h30”. Socio-demographic dimension encloses some of the most used demographic information, such as Genre, Region, Social Class, Occupation, and Housewife status. The age information is generally divided from 5 to 7 intervals. The authors decided to use the seven interval division: ”[4-14]”,”[15- 24]”, ”[25-34]”, ”[35-44]”, ”[45-54]”, ”[55-64]”, ”>64”. The dimension is populated with all the possible attribute’s values combination. For the used data 2 , the value is 6720. Program dimension encloses all the information related to a TV show; their name, description, type, classification and duration. TV shows are classified with a three level taxonomy, at most. The first level indicates the topmost classification, e.g. Fiction. Second level classify the show inside the first level, e.g. Fiction-Film. Finally, the last level give the maximum detailed classification for the show, e.g. Fiction-Film- Comedy. Notice that not all of the shows have a 2nd or 3rd level of detail. Classification is an enclosed hierarchy inside the program dimension [9]. However, it’s not very deep nor its value is unknown; it can be modelled simply with 3 attributes, one for each level, with no lack of generality. With these dimensions, the granularity of the fact table was fixed at the minute. Note that such a model doesn’t not addresses the necessities of advertising spots, which is out of the scope of this work. 3.2 Facts From a relational point of view, an OLAP cube is the projection of a relation R, where X1, X2, ..., Xn are 1 For Portuguese TV audience analysis, by the year 2006 2 From 2001
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Page 5 and 6: a contact of a set of viewers, shar

NON-ADDICTIVE SAMPLE BASED FACTS - Deetc

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