# Regaining Lost Knowledge

DevFeed: [Regaining Lost Knowledge](<https://devfeed.tech/articles/regaining-lost-knowledge-21695.md>)

Original publisher: [Read original article](<https://rhettinger.wordpress.com/2010/02/06/lost-knowledge/>)

Author: rhettinger

Published: 2010-02-06T23:28:30Z

Content type: article

Language: en

Sources: [Raymond Hettinger](<https://devfeed.tech/sources/raymond-hettinger.md>)

Topics: [Data structures](<https://devfeed.tech/topics/data-structures.md>), [Python](<https://devfeed.tech/topics/python.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [data-structures](<https://devfeed.tech/tags/data-structures.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [lost-knowledge](<https://devfeed.tech/tags/lost-knowledge.md>), [python](<https://devfeed.tech/tags/python.md>), [research](<https://devfeed.tech/tags/research.md>), [running-median](<https://devfeed.tech/tags/running-median.md>), [skiplists](<https://devfeed.tech/tags/skiplists.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>)

## AI overview

The article examines solutions for computing a running median over a large sliding window in a data stream. It describes an indexable skiplist with link widths that supports O(log n) updates and median lookup, offering a portable solution compared with earlier approaches.

## Source excerpt

Rediscovery of Indexable Skiplists in the solution of the Running Median problem.