Datawunder is first a data filtering tool, and then a data visualization tool. It is intended to be a data querying application above all else.
The objective is not just to find patterns in the data that one is actively looking to derive. Datawunder also aims to provide you with patterns previously unlooked for, or those not considered.
The original creator can show the world his derived patterns. Another individual can come along and derive his own conclusion(s) from the very same data, so on and so forth… In this way, each use can present a different point-of-view to the world, all from a single datasource.
Once the view is made public, it is accessible to all and sundry (with an internet connection, of course). Anyone can see the data, anyone can use the data, and anyone can modify the data. This last part is the core of ‘social data-mining’!
There are several features tailored toward ‘Social Data-mining’:
The comments system in Datawunder is an important part of the whole. Users can leave (traditional) plain-text comments about the default view or a particular snapshot of a view. Additionally, they can also create their own ‘snapshot’ from the original view to present their own point-of-view as a comment.
This would be a great way of pointing out a particular fact or observation or counterpoint. The user making the comment can also give the ‘snapshot’ a title and description of its own, thus differentiating it from the original (parent) view.
Similarly, each new comment can also incorporate its own snapshot, either of the original view, or a snapshot itself. These will all be presented in a conversation-style thread.
Each comment will also have the full range of sharing options i.e. Facebook, Twitter etc. so that users can share their comments with others.
One of the most important features coming to Datawunder is ‘Snapshots’. “What is a snapshot?” you may ask. A snapshot is basically a ‘saved’ selection of data, or a pre-configured/pre-filtered selection of data that can be saved.
The objective of a snapshot is to present a certain point-of-view or a particular observation or an alternate conclusion based on the data in the view. Use Datawunder to filter your data, and then save that selection as a ‘snapshot’. Write a small description about it and share it with the world.
Now, in most cases, a snapshot implies finality. This is not so in Datawunder. Snapshots can be edited and modified based on the user’s requirements.
A user can take an existing snapshot, add and remove filters and save it as a new snapshot as a comment, which can be shared via social networking services.
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Social Data-mining, Part III: Social Data-mining and Datawunder
Datawunder is first a data filtering tool, and then a data visualization tool. It is intended to be a data querying application above all else.
The objective is not just to find patterns in the data that one is actively looking to derive. Datawunder also aims to provide you with patterns previously unlooked for, or those not considered.
The original creator can show the world his derived patterns. Another individual can come along and derive his own conclusion(s) from the very same data, so on and so forth… In this way, each use can present a different point-of-view to the world, all from a single datasource.
Once the view is made public, it is accessible to all and sundry (with an internet connection, of course). Anyone can see the data, anyone can use the data, and anyone can modify the data. This last part is the core of ‘social data-mining’!
There are several features tailored toward ‘Social Data-mining’:
The comments system in Datawunder is an important part of the whole. Users can leave (traditional) plain-text comments about the default view or a particular snapshot of a view. Additionally, they can also create their own ‘snapshot’ from the original view to present their own point-of-view as a comment.
This would be a great way of pointing out a particular fact or observation or counterpoint. The user making the comment can also give the ‘snapshot’ a title and description of its own, thus differentiating it from the original (parent) view.
Similarly, each new comment can also incorporate its own snapshot, either of the original view, or a snapshot itself. These will all be presented in a conversation-style thread.
Each comment will also have the full range of sharing options i.e. Facebook, Twitter etc. so that users can share their comments with others.
One of the most important features coming to Datawunder is ‘Snapshots’. “What is a snapshot?” you may ask. A snapshot is basically a ‘saved’ selection of data, or a pre-configured/pre-filtered selection of data that can be saved.
The objective of a snapshot is to present a certain point-of-view or a particular observation or an alternate conclusion based on the data in the view. Use Datawunder to filter your data, and then save that selection as a ‘snapshot’. Write a small description about it and share it with the world.
Now, in most cases, a snapshot implies finality. This is not so in Datawunder. Snapshots can be edited and modified based on the user’s requirements.
A user can take an existing snapshot, add and remove filters and save it as a new snapshot as a comment, which can be shared via social networking services.
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