computer science

Information Processing Letters

Information Processing Letters 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 Information Processing Letters rather than just read about it. In short: Information Processing Letters is a peer-reviewed scientific journal in the field of computer science, published by Elsevier. The aim of the journal is to enable fast dissemination of results in the field of information processing in the form of short papers.

Key takeaways

Information Processing Letters 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 Information Processing Letters to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Information Processing Letters from memory before moving on to harder problems.

Reference excerpt

Information Processing Letters is a peer-reviewed scientific journal in the field of computer science, published by Elsevier. The aim of the journal is to enable fast dissemination of results in the field of information processing in the form of short papers. Submissions are limited to nine double-spaced pages. The scope of IPL covers fundamental aspects of information processing and computing. This naturally covers topics in the broadly understood field of theoretical computer science, including algorithms, formal languages and automata, computational complexity, computational logic, distributed and parallel algorithms, computational geometry, learning theory, computational number theory, computational biology, coding theory, theoretical cryptography, and applied discrete mathematics. Generally, submissions in all areas of scientific inquiry are considered, provided that they describe research contributions credibly motivated by applications to computing and involve rigorous methodology. High quality experimental papers that address topics of sufficiently broad interest are also considered. IPL implements a 3-tier review process. Each submissions is assigned to an associate editor, who determines whether it falls within IPL's scope and meets basic quality criteria. On average, about 60% of submissions are desk-rejected by associate editors. Submissions determined to be suitable for further review are distributed between the members of the editorial board, who handle the review process, which typically involves soliciting external reviews from 2-3 experts in the area. Between 2017 and 2020, the overall acceptance rate in IPL averaged 20–25%. Established in 1971, IPL is one of the oldest journals in computer science. In its now over 50-year old history, IPL has published research contributions from leading figures in computer science research, including multiple Turing Award winners: Alan Perlis, Edsger Dijkstra, Donald Knuth, Robert Floyd, Stephen Cook, Niklaus Wirth, Richard Karp, John Hopcroft, Robert Tarjan, Ronald Rivest, Edmund Clarke, Judea Perl, Silvio Micali, and Leslie Lamport. Among its earlier, pre-1990 articles, its list of influential papers includes the following:

Graham, R.L., An efficient algorithm for determining the convex hull of a finite planar set, 1972 Hyafil, L., Rivest, R.L., Constructing optimal binary decision trees is NP-complete, 1976 Garey, M.R., Johnson, D.S., Preparata, F.P., Tarjan, R.E., Triangulating a simple polygon, 1978 Aspvall, B., Plass, M.F., Tarjan, R.E., A linear-time algorithm for testing the truth of certain quantified boolean formulas, 1979 Dijkstra, E.W., Scholten, C.S., Termination detection for diffusing computations, 1980 Peterson, G.L., Myths about the mutual exclusion problem, 1981 Crochemore, M., An optimal algorithm for computing the repetitions in a word, 1981 Fischer, M.J., Lynch, N.A., A lower bound for the time to assure interactive consistency, 1982 Alpern, B., Schneider, F.B., Defining liveness, 1985 Reif, J.H., Depth-first search is inherently sequential, 1985 Apt, K.R., Kozen, D.C., Limits for automatic verification of finite-state concurrent systems, 1986 Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K., Occam's Razor, 1987 Boppana, R.B., Hastad, J., Zachos, S., Does co-NP have short interactive proofs?, 1987 Korel, B., Laski, J., Dynamic program slicing, 1988 Kamada, T., Kawai, S., An algorithm for drawing general undirected graphs, 1989

External links Official website

Worked examples

Example 1 — a first encounter with Information Processing Letters

Start with the simplest possible case. Write down what Information Processing Letters 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 Information Processing Letters 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 Processing Letters 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 Processing Letters

In research: Information Processing Letters 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 Information Processing Letters 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 Processing Letters is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic journals established in 1971, Computer science journals, Elsevier academic journals, so understanding it makes those chapters shorter.
In everyday life: Look for Information Processing Letters 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 Processing Letters in 20 minutes

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

Frequently asked questions

What is Information Processing Letters in simple terms?

Why does Information Processing Letters 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 Information Processing Letters?

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 Processing Letters.