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A collection of links for streaming algorithms and data structures
  1. General Background and Overview
  1. Hyperloglog and MinHash : Implementation of a form of hyperloglog and adding capabilities of MinHash algorithm on to it which would enable to perform set intersections."While it does require extra processing power to deal with collecting all the minima, it’s possible to get satisfactory performance out of the structure for a relatively low storage or memory footprint"

  2. Streaming/Sketching Conference from AK Tech : Contains links to videos and slides from the speakers like Muthukrishnan who spoke about Count Min Sketch

  3. Q-digest

  1. t-digest : A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means. Ted Dunning's variant of Q-digest that does some improvements

  2. Implementations

  1. Count-Min Sketch
  1. Surveys
  • References for Data Stream Algorithms by Graham Cormode : an exhaustive set of references with explanations
  • Data Streams - Algorithms and Applications by S. Muthukrishnan : This is an excellent monograph with surveys of all algorithms related to data streams. Also a free copy of the book is available from Muthu's web site at http://www.cs.rutgers.edu/~muthu/
  • Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches by Graham Cormode1, Minos Garofalakis, Peter J. Haas and Chris Jermaine . Describes basic principles and recent developments in approximate query processing. It focuses on four key synopses: random samples, histograms, wavelets, and sketches. It considers issues such as accuracy, space and time efficiency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. It also discusses the trade-offs between the different synopsis types.
  1. Distributed Streams Algorithms for Sliding Windows by Phillip B. Gibbons and Srikanta Tirthapura

  2. Frugal Streaming

  3. A Framework for Clustering Massive-Domain Data Streams by Charu C. Aggarwal

  4. A framework for clustering evolving data streams by Charu C. Aggarwal et. al.

  5. Unsupervised Feature Selection on Data Streams by Hao Huang

  6. Presentations

  1. Courses
  1. Incremental Learning with Decision Trees for Streamed Data
  1. Clustering Data Streams
@andypetrella

What about having a render-friendlier format like md?
This gist is a pearl, but horizontal scrolling is a bit sad ;-)

@debasishg
Owner

done ..

@slnovak
slnovak commented Jun 12, 2014

Thank you! Found via DataTau.

@blinsay
blinsay commented Jun 13, 2014

AggregateKnowledge has a good set of serialization-compatible Postgres/JS/Java HLL implementations:

https://github.com/aggregateknowledge/postgresql-hll
https://github.com/aggregateknowledge/java-hll
https://github.com/aggregateknowledge/js-hll

@vhazrati

Thanks Debashish this is valuable!

@tdunning

Great summary.

@finlay-liu

Good

@racranjan

Thanks !

@ronaldsuwandi

Thanks! This is great 👍

@terryyannan

great stuff!

@bistaumanga

Thanks (y) for this awesome stuff

@SemanticBeeng

If Apache Flink's "delta iterations", "off heap memory management" and "cost based optimization" qualify as "algorithms" then consider "Overview of Apache Flink: Next-Gen Big Data Analytics Framework": http://www.slideshare.net/sbaltagi/overview-of-apacheflinkbyslimbaltagi

@keshavbashyal

Thank you.. It is really valuable for the community..

@ajgappmark

Thanks a great resource indeed

@Grillz
Grillz commented Feb 20, 2016

fantastic

@Adewole
Adewole commented Apr 17, 2016

Interesting post.

@leerho
leerho commented May 25, 2016

You might be interested in DataSketches. A new production quality library of unique counting, quantiles, and frequent items sketches.

@fioreggianni

Exactly what I was looking for! Thanks!

@OElesin
OElesin commented Dec 9, 2016

Great Stuff, just found this!!

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