- From: Laufer <laufer@globo.com>
- Date: Sat, 18 Apr 2015 12:30:08 -0300
- To: DWBP WG <public-dwbp-wg@w3.org>
- Message-ID: <CA+pXJii7rZwtH3T7OPRF0va8ky8XOg+hkHnOn-LwcTSy0JAxgw@mail.gmail.com>
Hi Eric, Hi all. First of all, I would like to apologize for this long text. But the discussion about the usage of the Dataset is pretty much interesting and I feel that is one of the central things that could contribute for this new world of data. As I said in a previous message, I like to think in Data as a kind of product and, as one, demands a lot of things to help the consumers to find and use it (reuse is also a use). One of the things that is very important to the producer of a good is to provide communication channels to foster the interaction with consumers. Producers need feedback to better understand what is wrong and what is right with their products, what are the expectations, new desired features, etc. It is a kind of outside view: the real use versus the expected use. If it was a person, maybe it would be a kind of therapy where someone could perceive how others see you, in a way that you could adjust certain features or explain better other things. Who is me? Besides the official communication channels, eventually provided by the producers, nowadays people have a lot of other informal channels where they talk about so many things, including the products. And these talks are each day more valuable. Tools like twitter, for example, has a huge value because there are a lot of opinions, visions, etc, about a myriad of subjects that can provide information about these subjects (and these people, of course). Back to Dataset, a publisher provide a bunch of data and metadata that she imagines could clarify what is the Dataset being published, and, sometimes, what are the possible usages of this Dataset. Collecting feedback could provide information to the publishers in a way that the quality of the Dataset could (maybe) be enhanced, or new Datasets could be published, or the quality of metadata could be enhanced, or new metadata could be published. The initial DUV diagram has a Feedback class that has some specializations. One specialization that is being discussed is Citation. In some sense, all the specializations must have some kind of reference to the Dataset, in a way to connect the Opinion, Rating, etc., to the Dataset. How this link is done? If it is an official communication channel provided by the publisher, this is automatic. But if it is not? Citation is one of them. Using the Dataset identifier (?) is other. Or a not so directed link, but some link that could be extracted from a more informal reference. I am not saying that it is an easy thing to get the feedback provided by unofficial communication channels. But this is not easy for products in general, and tools for this task appear when the importance of feedback is perceived by the market. It is not in the scope of the group to provide these tools. What is a citation? It is a link that one work makes with a previous work. Why? “Why” is the feedback. Could be, as Annette pointed, a way to trust this new work. Even in this case, the new work has something that, in some sense, could extend the previous one. Citation, in general applies to frozen works. But our Datasets could evolve. Besides the “trust” use, the citation could be used in a work that makes a comparison between previous works and could have some criticisms about these works. It is common to have in works like thesis a section called “Related Works”, where the author analyses these works to show the contributions of his own work. Summarizing, I don’t think that citation is the (complete) feedback. It is a feedback in numeric terms, as it is an important fact that some work has a big number of citations. But citation is the link between works. The “why” is an important feedback. Again, I don’t think that it is an easy task to extract this information. I see DUV as a way to give semantics to describe all these data generated by the Dataset. To describe the (possible) universe that is created from an origin Dataset. In rough terms, a kind of reverse provenance. I have other issues in my mind but I think that they will be discussed along the development of DUV. Sorry again for this long (digressing) text. Cheers, Laufer -- . . . .. . . . . . .. . .. .
Received on Saturday, 18 April 2015 15:30:36 UTC