What is Semi-Structured data?

We are familiar with structured data, which is the data that has been clearly formed, formatted, modeled, and organized into customs that are easy for us to work and manage. We are also familiar with unstructured data.

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Unstructured data combines the bulk of information that does not fit into a set of databases. The most easily recognized form of unstructured data is the text in a document, like this article.

What you may not have known is that there is a middle ground for data; this is the data we refer to as semi-structured. This would be data sets that some implied structure is usually followed, but still not a standard enough structured to meet the criteria needed for the types of management and mechanization that is normally applied to structured data.

We deal with semi-structured data every day; this applies in both technical and non-technical environments. Web pages track definite distinctive forms, and the content entrenched within HTML usually have a certain extent of metadata within the tags.

Details about the data are implied instantly when using this information. This is why semi-structured data is so intriguing, though there is no set formatting rule, and there is still adequate reliability in which some interesting information can be taken from.

What does the Semi-Structured Data Model do?

Some advantages to the semi-structured data model include:

• Representation of the information about data sources that normally can not be constrained by schema.
• The model provides a flexible format used for the data switch over amongst dissimilar kinds of databases.
• Semi-structured data models are supportive in screening structured data as semi-structured data.
• The schema is effortlessly altered with the model
• The data transportation configuration can be convenient.

The most important exchange being made in using a semi-structured database model is quite possibly that the queries will not be made as resourcefully as in the more inhibited structures, like the relational model.

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Normally the records in a semi-structured database are stored with only one of a kind IDs that are referenced with indicators to their specific locality on a disk. Due to this the course-plotting or path based queries are very well-organized, yet for the purpose of doing searches over scores of records it is not as practical for the reason that it is forced to seek in the various regions of the disk by following the indicators.

We can clearly see that there are some disadvantages with semi-structured data model, as there are with all other models, lets take a moment to outline a few of these disadvantages.

Next Page: Semi-Structured Data Issues