computer science

Weissman score

Weissman score is a computer 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 Weissman score rather than just read about it. In short: The Weissman score is a performance metric for lossless compression applications. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Misra, a graduate student, at the request of producers for HBO's television series Silicon Valley, a television show about a fictional tech start-up working on a data compression algorithm.

Key takeaways

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

Reference excerpt

The Weissman score is a performance metric for lossless compression applications. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Misra, a graduate student, at the request of producers for HBO's television series Silicon Valley, a television show about a fictional tech start-up working on a data compression algorithm. It compares both required time and compression ratio of measured applications, with those of a de facto standard according to the data type. The formula is the following; where r is the compression ratio, T is the time required to compress, the overlined ones are the same metrics for a standard compressor, and alpha is a scaling constant.

W = α r r ¯ log ⁡ T ¯ log ⁡ T {\displaystyle W=\alpha {r \over {\overline {r}}}{\log {\overline {T}} \over \log {T}}}

The Weissman score has been used by Daniel Reiter Horn and Mehant Baid of Dropbox to explain real-world work on lossless compression. According to the authors it "favors compression speed over ratio in most cases."

Example This example shows the score for the data of the Hutter Prize, using the paq8f as a standard and 1 as the scaling constant.

Limitations Although the value is relative to the standards against which it is compared, the unit used to measure the times changes the score (see examples 1 and 2). This is a consequence of the requirement that the argument of the logarithmic function must be dimensionless. The multiplier also can't have a numeric value of 1 or less, because the logarithm of 1 is 0 (examples 3 and 4), and the logarithm of any value less than 1 is negative (examples 5 and 6); that would result in scores of value 0 (even with changes), undefined, or negative (even if better than positive).

See also Benchmark Coding theory Information theory Phred quality score

References

Worked examples

Example 1 — a first encounter with Weissman score

Start with the simplest possible case. Write down what Weissman score claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer 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 Weissman score 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 Weissman score 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 Weissman score

In research: Weissman score appears in computer 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 Weissman score 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: Weissman score is common in secondary-school and first-year university syllabi. It links to neighbouring topics Benchmarks (computing), Data compression, Silicon Valley (TV series), so understanding it makes those chapters shorter.
In everyday life: Look for Weissman score 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 Weissman score in 20 minutes

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

Frequently asked questions

What is Weissman score in simple terms?

Why does Weissman score matter?

Because it connects several computer 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 Weissman score?

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 Weissman score.