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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
  1. Books
  • Andrew McGregor is writing a book on sketching and data streaming algorithms, parts of the draft is available here
@andypetrella

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andypetrella Dec 30, 2013

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

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

@debasishg

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debasishg Dec 31, 2013

done ..

Owner

debasishg commented Dec 31, 2013

done ..

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slnovak Jun 12, 2014

Thank you! Found via DataTau.

slnovak commented Jun 12, 2014

Thank you! Found via DataTau.

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blinsay 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

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

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vhazrati Jan 12, 2015

Thanks Debashish this is valuable!

Thanks Debashish this is valuable!

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tdunning Mar 26, 2015

Great summary.

Great summary.

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finlay-liu Apr 4, 2015

Good

Good

@racranjan

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racranjan May 5, 2015

Thanks !

Thanks !

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ronaldsuwandi Jun 15, 2015

Thanks! This is great 👍

Thanks! This is great 👍

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jrjtLite Jun 25, 2015

great stuff!

great stuff!

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bistaumanga Oct 22, 2015

Thanks (y) for this awesome stuff

Thanks (y) for this awesome stuff

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SemanticBeeng Dec 9, 2015

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

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

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keshavbashyal Dec 19, 2015

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

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

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ajgappmark Feb 1, 2016

Thanks a great resource indeed

Thanks a great resource indeed

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pbarker Feb 20, 2016

fantastic

pbarker commented Feb 20, 2016

fantastic

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Adewole Apr 17, 2016

Interesting post.

Adewole commented Apr 17, 2016

Interesting post.

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leerho May 25, 2016

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

leerho commented May 25, 2016

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

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fioreggianni Jul 11, 2016

Exactly what I was looking for! Thanks!

Exactly what I was looking for! Thanks!

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OElesin Dec 9, 2016

Great Stuff, just found this!!

OElesin commented Dec 9, 2016

Great Stuff, just found this!!

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visenger May 3, 2017

Great! found via @ds_ldn

visenger commented May 3, 2017

Great! found via @ds_ldn

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ChamodDamitha Jul 5, 2017

Great

Great

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tamyiuchau Jul 12, 2017

Link to Blog Post on q-digest seems broken. A quick google point me to https://papercruncher.wordpress.com/2011/07/31/q-digest/
Please update it. Thanks.

Link to Blog Post on q-digest seems broken. A quick google point me to https://papercruncher.wordpress.com/2011/07/31/q-digest/
Please update it. Thanks.

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guozheng Sep 27, 2017

really nice list, can you consider adding it to the Awesome project so that more people can benefit ;-)

https://github.com/sindresorhus/awesome

really nice list, can you consider adding it to the Awesome project so that more people can benefit ;-)

https://github.com/sindresorhus/awesome

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