science

Information integration

Information integration 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 Information integration rather than just read about it. In short: Information integration (II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. It is used in data mining and consolidation of data from unstructured or semi-structured resources.

Key takeaways

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

Reference excerpt

Information integration (II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. It is used in data mining and consolidation of data from unstructured or semi-structured resources. Typically, information integration refers to textual representations of knowledge but is sometimes applied to rich-media content. Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty. Examples of technologies available to integrate information include deduplication, and string metrics which allow the detection of similar text in different data sources by fuzzy matching. A host of methods for these research areas are available such as those presented in the International Society of Information Fusion. Other methods rely on causal estimates of the outcomes based on a model of the sources.

See also Data fusion (is a subset of Information integration) Sensor fusion Data integration Image fusion Synesthesia

Books M. E. Liggins; D. L. Hall; J. Llinas (2008). Theory and Practice (Multisensor Data Fusion) (Second ed.). CRC. ISBN 978-1-4200-5308-1. D. L. Hall; S. A. H. McMullen (2004). Mathematical Techniques in Multisensor Data Fusion. ISBN 978-1-58053-335-5. Springer, Information Fusion in Data Mining (2003), ISBN 3-540-00676-1 H. B. Mitchell, Multi-sensor Data Fusion – An Introduction (2007) Springer-Verlag, Berlin, ISBN 978-3-540-71463-7 S. Das, High-Level Data Fusion (2008), Artech House Publishers, Norwood, MA, ISBN 978-1-59693-281-4 and 1596932813 E. P. Blasch, E. Bosse, and D. A. Lambert, High-Level Information Fusion Management and System Design (2012), Artech House Publishers, Norwood, MA. ISBN 1608071510 | ISBN 978-1608071517 L. Snidaro; J. Garcia-Herrero; J. Llinas; et al. (2016). Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge. Springer-Verlag. ISBN 978-3319289694.

References

External links Discriminant Correlation Analysis (DCA) Information Integration Using Logical View LNCS 1997. International Society of Information Fusion

Worked examples

Example 1 — a first encounter with Information integration

Start with the simplest possible case. Write down what Information integration 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 Information integration 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 Information integration 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 Information integration

In research: Information integration 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 Information integration 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: Information integration is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data management, so understanding it makes those chapters shorter.
In everyday life: Look for Information integration 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 Information integration in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Information integration 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 Information integration out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Information integration in simple terms?

Why does Information integration 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 Information integration?

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 Information integration.