Data Sketching - ACM Queue
May 31, 2017 Volume 15, issue 2 PDF Data Sketching The approximate approach is often faster and more efficient. Graham Cormode Do you ever feel overwhelmed by an unending stream of information? It can seem like a barrage of new email and text messages demands constant attention, and there are also phone calls to pick up, articles to read, and knocks on the door to answer. Putting these pieces toge
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Sisense blog: AI, analytics, and the future of insights
Home > Sisense blog: AI, analytics, and the future of insights By Maor Aharoni and Maria Ciampa March 2, 2026 Sisense product roundup: AI connectivity, security, and smarter dashboards in 2026.1 With the 2026.1.0 release, Sisense continues to focus on making analytics more accessible, flexible, and secure for teams building data-driven products. This release introduces major advancements like the
HyperLogLogã§éŠã¶ - Negative/Positive Thinking
ã¯ã˜ã‚㫠「ã•ãã€ãŠå‰ã®ç½ªã®ç•°ãªã‚Šæ•°ã‚’æ•°ãˆã‚ï¼ã€ã¨è¨€ã‚ã‚ŒãŸã¨ãã«ä½¿ãˆãã†ãªã€ŒHyperLogLogã€ã¨ã„ã†ç•°ãªã‚Šæ•°ã‚’カウントã™ã‚‹æ–¹æ³•ã‚’æ•™ãˆã¦ã‚‚らã£ãŸã®ã§ã€éŠã‚“ã§ã¿ãŸã€‚ ã„ã¤ã‚‚ãªãŒã‚‰è«–æ–‡ã¡ã‚ƒã‚“ã¨èªã‚“ã§ãªã„ã®ã§ã€æ¡ä»¶ã‚„コード間é•ã£ã¦ã‚‹ã‹ã‚‚。。。 HyperLogLogã¨ã¯ cardinalityã¨å‘¼ã°ã‚Œã‚‹ã€è¦ç´ ã®ç•°ãªã‚Šæ•°ã‚’決定ã™ã‚‹å•é¡Œ ã‹ãªã‚Šçœãƒ¡ãƒ¢ãƒªã§ç²¾åº¦ã®ã‚ˆã„ç•°ãªã‚Šæ•°ã‚’推定ã§ãる方法 è¦ç´ ã‚’ãã®ã¾ã¾ä¿å˜ã›ãšã€ãƒãƒƒã‚·ãƒ¥å€¤ã«å¤‰æ›ã—ãŸã‚‚ã®ã‚’ã†ã¾ãレジスタã«ä¿å˜ã—ã¦ãŠã ã®ã§ã€ãƒ¬ã‚¸ã‚¹ã‚¿ã‚µã‚¤ã‚ºç¨‹åº¦ã—ã‹ãƒ¡ãƒ¢ãƒªã‚’使ã‚ãªã„ 並列化もã§ãã¦ã€æœ€è¿‘ã®bigdataã¨ã‹ã§æ³¨ç›®ã•ã‚Œã¦ã„ã‚‹ ã¾ãŸã€googleãŒä¸¦åˆ—計算用ã«æ”¹å–„ã—ãŸHyperLogLogã‚’æ案ã—ã¦ã‚‹ã¿ãŸã„ http://blog.aggregateknowledge.com/2013/01/24/hyperloglog-googles-take-on-
Screen6 - HyperLogLog with Cascalog
We’ll look briefly in how you would utilize awesomeness of both Cascalog and HyperLogLog in order to execute Hadoop M/R tasks with amounts of data too big to have them in their original form. Intro HyperLogLog Cardinality estimator allowing you to count amount of distinct values. Cascalog The main use cases for Cascalog are processing "Big Data" on top of Hadoop or doing analysis on your local com
Probabilistic Data Structures for Web Analytics and Data Mining
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor appl
HyperLogLog++: Google’s Take On Engineering HLL –
Matt Abrams recently pointed me to Google’s excellent paper “HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithmâ€Â [UPDATE: changed the link to the paper version without typos] and I thought I’d share my take on it and explain a few points that I had trouble getting through the first time. The paper offers a few interesting improvements that are w
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Count-distinct problem - Wikipedia
GitHub - google/zetasketch: A collection of libraries for single-pass, distributed, sublinear-space approximate aggregation and sketching algorithms. Currently: HyperLogLog++; more to come.
Efficient Aggregation, Roll-ups with BigQuery HyperLogLog++ functions
KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale
Counting uniques faster in BigQuery with HyperLogLog++ | Google Cloud Blog
DataSketches |
Approximate Algorithms in Apache Spark: HyperLogLog and Quantiles
https://dl.acm.org/citation.cfm?doid=2452376.2452456
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https://tech.nextroll.com/media/hllminhash.pdf
GitHub - MLnick/hive-udf: Approximate cardinality estimation with HyperLogLog, as a Hive function
http://research.google.com/pubs/archive/40671.pdf
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