Ask HN: What are some cool but obscure data structures you know about? | Hacker News
I'm very interested in what types of interesting data structures are out there HN. Totally your preference.I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algor
Core algorithms deployed
Algorithms that are the main driver behind a system are, in my opinion, easier to find in non-algorithms courses for the same reason theorems with immediate applications are easier to find in applied mathematics rather than pure mathematics courses. It is rare for a practical problem to have the exact structure of the abstract problem in a lecture. To be argumentative, I see no reason why fashiona
Top 10 Algorithms and Data Structures for Competitive Programming - GeeksforGeeks
TutorialsPython TutorialTaking Input in PythonPython OperatorsPython Data TypesPython NumbersPython StringPython ListsPython TuplesSets in PythonPython DictionaryPython Loops and Control FlowPython Conditional StatementsPython LoopsPython FunctionsPython OOPS ConceptPython Data StructuresPython DSALinked ListStackQueueTreeHeapHashingGraphSetsMapAdvance Data StructureSorting AlgorithmsSearching Alg
Stream Mining essentials - O'Reilly Radar
Stream Mining essentials At the most basic level, stream mining is about generating summaries that can be used to answer fundamental questions A series of open source, distributed stream processing frameworks have become essential components in many big data technology stacks. Apache Storm remains the most popular, but promising new tools like Spark Streaming and Apache Samza are going to have the
最適化ソフトとテスト問題集 (Optimization Softwares and Test Problems)
最適化ソフトとテスト問題集 (Optimization Softwares and Test Problems) 最適化ソフトウェアとテスト問題集 Optimization Softwares and Test Problems English version is here. このページに加えた方が良い場所をご存知の方は、御一報下さい。 特に日本国内の場所（商用もOK）についての情報をお待ちしております。 初めていらっしゃった方へ ここには、最適化関連のソフト（コード）に関するリンクを集めました。 初めての方は以下の検索用のサイトを用いるのも一つの手です。 Guide to Available Mathematical Software (GAMS) NETLIB Index search OPT-NET Index search あとは、各サイトのインデックスを御利用下
Online Electronic Catalog of Jeff Vitter
This index provides access to an online publication library of several of my more recent papers -- as well as a few oldies but goodies. In a few cases, I also include the overhead transparencies for talks. Most of the papers and talks deal with the design and analysis of algorithms and data structures. They are grouped, roughly in chronological order, according to the specific topic areas listed b
Google Sites: Sign-in
Not your computer? Use a private browsing window to sign in. Learn more about using Guest mode
Streaming Data Mining Tutorial slides (and more)
Jelani Nelson.and Edo Liberty just released an important tutorial they gave at KDD 12 on the state of the art and practical algorithms used in mining streaming data, it is entitled: Streaming Data Mining I personally marvel at the development of these deep algorithms which, because of the large data streams constraints, get to redefine what it means to do seemingly simple functions such as count
The MMDS 2012 Slides are out! Workshop on Algorithms for Modern Massive Data Sets
In case you have to take your mind off tomorrow's suspense-filled and technologically challenging landing of Curiosity on Mars (see 7 minutes of Terror, a blockbuster taking place on Mars this Summer ) Michael Mahoney, Alex Shkolnik, Gunnar Carlsson, Petros Drineas, the organizers of Workshop on Algorithms for Modern Massive Data Sets (MMDS 2012), just made available the slides of the meeting. Oth
IBM Research | Almaden
The team's work builds on a rich history of breakthroughs at Almaden’s unique campus in San Jose, California that include innovations in data storage technology, novel material discovery for sustainability, the distributed relational database, the ability to position individual atoms, and the first data mining algorithms.
What are the lesser known but useful data structures?
There are some data structures around that are really useful but are unknown to most programmers. Which ones are they? Everybody knows about linked lists, binary trees, and hashes, but what about Skip lists and Bloom filters for example. I would like to know more data structures that are not so common, but are worth knowing because they rely on great ideas and enrich a programmer's tool box. PS: I
Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) | atbrox
Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) The prior update of this posting was in May, and a lot has happened related to Mapreduce and Hadoop since then, e.g. 1) big software companies have started offering hadoop-based software (Microsoft and Oracle), 2) Hadoop-startups have raised record amounts, and 3) nosql-landscape becoming increasingly datawarehouse’ish and sq
List of Algorithms
A complete list of all major algorithms (300), in any domain. The goal is to provide a ready to run program for each one, or a description of the algorithm. Programming languages include Java, JavaScript and PHP, C, C++ either in direct form or generated from a Scriptol source. Automata Artificial intelligence Computer vision Genetic algorithms Neural networks Machine learning Bioinformatics and c
私のブックマーク ： 簡潔データ構造
田部井靖生（科学技術振興機構 ERATO湊離散構造処理系プロジェクト研究員) はじめに 近年、Web技術や計測技術の発展により言語やゲノムデータは大規模化しています。従来のデータ構造は大規模データを扱うにはサイズが大きくメモリに載らない、 しかし、圧縮するとランダムアクセスをすることができないという欠点があります。 簡潔データ構造とはデータを小さく保存かつ高速な操作が可能なデータ構造です。 近年、集合、文字列、木、グラフデータを扱うための簡潔データ構造が提案され注目を集めています。 私たちの身近なアプリケーションとして、Google日本語入力では簡潔木LOUDSの実装が使われ、実際に使われはじめています。 また、有志によるそれらを解説したサイトやライブラリなども利用可能になりつつあります。 そこで、このページでは簡潔データ構造を用いた研究開発のためのいろいろなリソースを紹介します。 解説記
15-499: Parallel Algorithms
15-499: Parallel Algorithms (Spring 2009) Instructor: Guy Blelloch, Office hours: Tue 1:30-2:30PM, WeH 7125 Place/Time: Doherty 1212, TR 12:00PM - 1:20PM. TA: Harsha Simhadri, email: harshas]AT[cs-cmu-edu, Office hours: Wednesday 4:00-5:00PM, WeH 8303 Course Description: In this course students will learn about parallel algorithms. The emphasis will be on algorithms that can be used on shared-memo
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
The Algorithms
Open Problems in Sublinear Algorithms
Data Compression Techniques | Department of Computer Science [pre 2018 site]
Mathematical Constants and computation
What are some good resources for learning about stream mining? Why?