RE: DQV - metrics related to the completeness dimension

The number of measurements does not assure that the data is trustworthy.

Best Regards,


   Makx Dekkers --- RE: DQV - metrics related to the completeness dimension --- 
    From:"Makx Dekkers" <>To:"'Debattista, Jeremy'" <>, "Steven Adler" <>Cc:"'Nandana Mihindukulasooriya'" <>, "'Data on the Web Best Practices Working Group'" <>Date:Wed, Sep 30, 2015 5:00 AMSubject:RE: DQV - metrics related to the completeness dimension
   Aren’t we making this too complex? 
   It seems to me that in certain cases there can be ‘absolute’ measures of quality. For example, if I publish a dataset with air quality observations from 50 measuring stations, I can state that the dataset is complete because it contains all observations from all measuring stations, or that it is not complete because observations from stations X and Y are missing. This is not subjective at all.
   Defining quality as “fitness for use” allows for a discussion about the completeness of the approach behind the dataset, e.g. someone can argue that my measurements of air quality do not include parameters that are crucial for their research and therefore the measurements are “incomplete”. I would argue that the dataset is still “complete”. 
   In my mind, the three completeness metrics (Schema completeness, population completeness, column completeness) as formulated by Nandana point mainly to the quality of the approach as it talks about “required attributes”, not to the quality of the dataset itself. If you change the phrases “required attributes” and “required population” by “observed attributes” and “observed population”, you can have an objective measure of the completeness of the dataset.
   Of course, if a user has particular requirements in terms of the set of attributes, and a dataset contains a different set of attributes, that dataset may not be “fit for (this user’s) use”, but it could still be 100% complete with respect to its own set of attributes and population.
                  From: Debattista, Jeremy [] Sent: 30 September 2015 09:26To: Steven Adler <>Cc: Nandana Mihindukulasooriya <>; Data on the Web Best Practices Working Group <>Subject: Re: DQV - metrics related to the completeness dimension
         What you said is true Steven, and (in principle) I would agree on avoiding universal completeness in favour of a more sustainable measure. On the other hand your solution is highly subjective and thus very hard to calculate. It would be nice to have such an index score, but I’m not quite sure that this will work in practice as there are many factors that have to be considered.
               On 30 Sep 2015, at 03:42, Steven Adler <> wrote:
           You can avoid "universal" completeness by allowing publishers and consumers to publish their confidence level in the data. The combination of confidence attributes would be calculated as an index of confidence and doubt, like a set of product reviews. This method is more organic to how the data has been and is used. Just a thought.Best Regards,SteveMotto: "Do First, Think, Do it Again"<graycol.gif>Nandana Mihindukulasooriya ---09/27/2015 08:07:02 PM---Hi all, In the F2F (re: action-153), we talked about the difficulties of definingFrom: Nandana Mihindukulasooriya <>To: Data on the Web Best Practices Working Group <>Date: 09/27/2015 08:07 PMSubject: DQV - metrics related to the completeness dimension
            Hi all,In the F2F (re: action-153), we talked about the difficulties of defining metrics for measuring completeness and the need for examples. Here's some input from a project we are working on at the moment. TD;LR versionIt's hard to define universal completeness metrics that suit everyone. However, completeness metrics can be defined for concrete use cases or specific contexts of use. In the case of RDF data, a closed world assumption has to be applied to calculate completeness. Longer versionQuality is generally defined as "fitness for *use*". Further, completeness is defined as "The degree to which subject data associated with an entity has values for all expected attributes and related entity instances *in a specific context of use*" [ISO 25012]. It's important to note that both definitions emphasize that the perceived quality depends on the intended use. Thus, a dataset fully complete for a one task might be quite incomplete for another task. For example, it's not easy to define a metric that universally measures the completeness of a dataset. However, for a concrete use case such as calculating some economic indicators of Spanish provinces, we can define a set of completeness metrics. In this case, we can define three metrics(i) Schema completeness i.e. the degree to which required attributes are not missing in the schema. In our use case, the attributes we are interested are the total population, unemployment level, and average personal income of a province and the schema completeness is calculated using those attributes.  (ii) Population completeness i.e. the degree to which elements of the required population are not missing in the data. In our use case, the population we are interested in is all the provinces of Spain and the population completeness is calculated against them. (iii) Column completeness i.e. the degree to which which the values of the required attributes are not missing in the data. Column completeness is calculated using the schema and the population defined before and the facts in the dataset.With these metrics, now we can measure the completeness of the dataset for our use case. As we can see, those metrics are quite specific to our use case. Later if we have another use case about Spanish movies, we can define a set of different schema, population, and column completeness metrics and the same dataset will have different values for those different metrics. If the data providers foresee some specific use cases, they might be able to define some concrete completeness metrics and made them available as quality measures. If not, the data consumers can define more specific completeness metrics for their use cases and measure values for those metrics. These completeness metrics can be used to evaluate the "fitness for use" of different datasets for a given use case. To generate population completeness, the required population should be known. The required attributes and other constraints of schema might be expressed using SHACL shapes [1].In the case of RDF data, we will assume a closed world assumption and only consider the axioms and facts included in the dataset. Also, if the use case involves linksets, other metrics such as interlinking completeness can be used. Hope this helps to discuss more concretely about the completeness metrics. It will be interesting to hear other experiences in defining completeness metrics and counter examples where it is easy to define universal completeness metrics.  Best Regards,Nandana[1]

Received on Wednesday, 30 September 2015 09:36:02 UTC