science

Wrapper (data mining)

Wrapper (data mining) is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Wrapper (data mining) rather than just read about it. In short: Wrapper in data mining is a procedure that extracts regular subcontent of an unstructured or loosely-structured information source and translates it into a relational form, so it can be processed as structured data. Wrapper induction is the problem of devising extraction procedures on an automatic basis, with minimal reliance on hand-crafted rules.

Key takeaways

Wrapper (data mining) belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Wrapper (data mining) to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Wrapper (data mining) from memory before moving on to harder problems.

Reference excerpt

Wrapper in data mining is a procedure that extracts regular subcontent of an unstructured or loosely-structured information source and translates it into a relational form, so it can be processed as structured data. Wrapper induction is the problem of devising extraction procedures on an automatic basis, with minimal reliance on hand-crafted rules. Many web pages are automatically generated from structured data – telephone directories, product catalogs, etc. – wrapped in a loosely structured presentation language (usually some variant of HTML), formatted for human browsing and navigation. Structured data are typically descriptions of objects retrieved from underlying databases and displayed in web pages following fixed templates at a low level, injected into pages where the high-level structure can vary from week to week, per the rapidly evolving fashion of the site's presentation skin. The precise dividing line between the fluid high-level skin and the less fluid structured data templates is rarely documented for public consumption, outside of the content management team at the web property. Software systems using such resources must translate HTML content into a relational form. Wrappers are commonly used as such translators. Formally, a wrapper is a function from a page to the set of tuples it contains.

Wrapper generation There are two main approaches to wrapper generation: wrapper induction and automated data extraction. Wrapper induction uses supervised learning to learn data extraction rules from manually labeled training examples. The disadvantages of wrapper induction are

the time-consuming manual labeling process and the difficulty of wrapper maintenance. Due to the manual labeling effort, it is hard to extract data from a large number of sites as each site has its own templates and requires separate manual labeling for wrapper learning. Wrapper maintenance is also a major issue because whenever a site changes the wrappers built for the site become obsolete. Due to these shortcomings, researchers have studied automated wrapper generation using unsupervised pattern mining. Automated extraction is possible because most Web data objects follow fixed templates. Discovering such templates or patterns enables the system to perform extraction automatically. Wrapper generation on the Web is an important problem with a wide range of applications. Extraction of such data enables one to integrate data/information from multiple Web sites to provide value-added services, e.g., comparative shopping, object search, and information integration.

See also Business intelligence (section semi-structured or unstructured data) Web scraping

Sources

Worked examples

Example 1 — a first encounter with Wrapper (data mining)

Start with the simplest possible case. Write down what Wrapper (data mining) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Wrapper (data mining) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Wrapper (data mining) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Wrapper (data mining)

In research: Wrapper (data mining) appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Wrapper (data mining) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Wrapper (data mining) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data mining, so understanding it makes those chapters shorter.
In everyday life: Look for Wrapper (data mining) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Wrapper (data mining) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Wrapper (data mining) means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Wrapper (data mining) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Wrapper (data mining) in simple terms?

Why does Wrapper (data mining) matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Wrapper (data mining)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Wrapper (data mining).