science

Quality of Data

Quality of Data 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 Quality of Data rather than just read about it. In short: Quality of Data (QoD) is a designation coined by L. Veiga, that specifies and describes the required Quality of Service of a distributed storage system from the Consistency point of view of its data.

Key takeaways

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

Reference excerpt

Quality of Data (QoD) is a designation coined by L. Veiga, that specifies and describes the required Quality of Service of a distributed storage system from the Consistency point of view of its data. It can be used to support big data management frameworks, Workflow management, and HPC systems (mainly for data replication and consistency). It takes into account data semantics, namely the Time interval of data freshness, the Sequence of tolerable number of outstanding versions of the data read before ore refresh, and the Value divergence allowed before displaying it. Initially it was based on a model from an existing research work regarding vector-field Consistency, awarded the best-paper prize in the ACM/IFIP/Usenix Middleware Conference 2007 and later enhanced for increased scalability and fault-tolerance. This consistency model has been successfully applied and proven in big data key/value store Apache HBase, initially designed as a middleware module seating between clusters from separate data centres. The HBase-QoD coupling minimises bandwidth usage and optimises resources allocation during replication achieving the desired consistency level at a more fine-grained level. QoD is defined by the three-dimensions of vector k=(θ,σ,ν), but with a broader view of the issue, applicable also to large-scale data management techniques in regards to their timely delivery.

Other descriptions Quality of Data should not be confused with other definitions for data quality such as completeness, validity, and accuracy.

References

Worked examples

Example 1 — a first encounter with Quality of Data

Start with the simplest possible case. Write down what Quality of Data 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 Quality of Data 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 Quality of Data 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 Quality of Data

In research: Quality of Data 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 Quality of Data 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: Quality of Data 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 Quality of Data 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 Quality of Data in 20 minutes

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

Frequently asked questions

What is Quality of Data in simple terms?

Quality of Data (QoD) is a designation coined by L. Veiga, that specifies and describes the required Quality of Service of a distributed storage system from the Consistency point of view of its data.

Why does Quality of Data 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 Quality of Data?

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 Quality of Data.