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Probabilistic counting algorithms for data base applications

Philippe Flajolet, G. Nigel Martin
1985 Journal of computer and system sciences (Print)  
Probabilistic counting algorithms for data base applications Philippe Flajolet, G. Nigel Martin. Probabilistic counting algorithms for data base applications. [Research Report] RR-0313, INRIA. 1984.  ...  Submitted on 24 May 2006 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not.  ... 
doi:10.1016/0022-0000(85)90041-8 fatcat:gtkavzvqgbcnvcyxqf3u3v3te4

Incremental Temporal Mining using Incremental TPMiner and Incremental P-TPMiner Algorithms

R. V., Sonali Vijaykumar
2017 International Journal of Computer Applications  
There are two types of interval-based patterns: Temporal pattern and Probabilistic temporal pattern are proposed.  ...  There are some applications using temporal event data have used to discovering patterns from events.  ...  Mrs.S.S.Apte for providing necessary guidance concerning projects implementation.  ... 
doi:10.5120/ijca2017915365 fatcat:2nujtrboxzhi5jojl4ppuom4gu

Estimating Aggregate Properties on Probabilistic Streams [article]

Andrew McGregor, S. Muthukrishnan
2006 arXiv   pre-print
The probabilistic model is applicable for not only analyzing streams where the input has uncertainties (such as sensor data streams that measure physical processes) but also where the streams are derived  ...  We present streaming algorithms for computing commonly used aggregates on a probabilistic stream.  ...  Previous work on probabilistic streams has included data stream algorithms for certain aggregate queries [6, 7, 16] .  ... 
arXiv:cs/0612031v1 fatcat:chfqglwknvhttnjx3fwqxc57dy

Novel Algorithms CIPFP for Mining Frequent Patterns using Counting Inference from Probabilistic Databases and Future Possibilities

Niket Bhargava, Manoj Shukla
2016 International Journal of Computer Applications  
This method was implemented in the CIPFP, counting inference based probabilistic frequent pattern mining algorithm that is an optimization of the simple and efficient Apriori algorithm.  ...  in probabilistic databases, this algorithm allows to perform as few support counts as possible.  ...  These solutions were not developed on probabilistic data models. For probabilistic databases, [32, 25] derived patterns based on their expected support counts.  ... 
doi:10.5120/ijca2016909478 fatcat:gvjpxigirrb5pniplyoosy6fna

Efficient and secure threshold-based event validation for VANETs

Hsu-Chun Hsiao, Ahren Studer, Rituik Dubey, Elaine Shi, Adrian Perrig
2011 Proceedings of the fourth ACM conference on Wireless network security - WiSec '11  
Quite counter-intuitively, we found that the z-smallest approach offers the best tradeoff between security and efficiency since other approaches perform better for probabilistic counting.  ...  We present the first efficient and secure threshold-based event validation protocol for VANETs.  ...  Acknowledgements We gratefully thank Fan Bai, Bhargav Bellur, and Aravind Iyer for their insightful suggestions, as well as the anonymous reviewers for their valuable comments.  ... 
doi:10.1145/1998412.1998440 dblp:conf/wisec/HsiaoSDSP11 fatcat:flwbthoce5c7hg5mvdznxjsike

Bitmap Algorithms for Counting Active Flows on High-Speed Links

C. Estan, G. Varghese, M. Fisk
2006 IEEE/ACM Transactions on Networking  
By contrast, our new probabilistic algorithms use little memory and are fast. The reduction in memory is particularly important for applications that run multiple concurrent counting instances.  ...  The best known prior algorithm (probabilistic counting) takes four times more memory on port scan detection and eight times more on a measurement application.  ...  Baboescu for extremely valuable conversations.  ... 
doi:10.1109/tnet.2006.882836 fatcat:oagjxyqgdzcsfa7rojop22zllq

An Inference Language for Imaging [chapter]

Stefano Pedemonte, Ciprian Catana, Koen Van Leemput
2014 Lecture Notes in Computer Science  
The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications.  ...  We introduce iLang, a language and software framework for probabilistic inference.  ...  The authors would like to thank Paulina Golland and the MIT EECS/CSAIL journal club for the useful introduction to the ADMM algorithm.  ... 
doi:10.1007/978-3-319-12289-2_6 fatcat:og6xiwvb6rhc5obpgt3yb546ie

Privacy Preserving Count Statistics [article]

Lu Yu and Oluwakemi Hambolu and Yu Fu and Jon Oakley and Richard R. Brooks
2019 arXiv   pre-print
We extend previous work in probabilistic counting by considering its use for preserving user anonymity, developing application guidelines and including hash collisions in the estimate.  ...  Probabilistic counting has been used to find the cardinality of a multiset when precise counting is too resource intensive.  ...  Based on the results of our experiments, it is safe to say that CIPC outperforms probabilistic counting in general.  ... 
arXiv:1910.07020v1 fatcat:jwxqpbygt5havh5uwptq22q6my

Estimating statistical aggregates on probabilistic data streams

T. S. Jayram, Andrew McGregor, S. Muthukrishnan, Erik Vee
2007 Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '07  
Designing efficient aggregation algorithms for probabilistic data is crucial for handling uncertainty in data-centric applications such as OLAP.  ...  Next, we consider the problem of estimating frequency moments for probabilistic data.  ...  Data stream algorithms for frequency moments are well-studied and use sophisticated techniques based on hashing to produce high quality estimates.  ... 
doi:10.1145/1265530.1265565 dblp:conf/pods/JayramMMV07 fatcat:dx4nutnwdzejdl26cmy2eqmnbm

Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset

Meenu Dave, Hitesh Maharwal
2014 International Journal of Computer Applications  
Setting a single value of minsup for a transaction set doesn't seem feasible for some real life applications. Similarly the probabilistic value of items in the transaction set may be acceptable.  ...  It uses minimum support (minsup) and support confidence (supconf) as a base to generate the frequent patterns and strong association rules.  ...  Setting of single minsup value for itemset does not work in real-life application. In many applications, some items appear very frequently in the data, while others rarely appear.  ... 
doi:10.5120/17377-7913 fatcat:443dxiy6kzekbfmkvsxahussam

Estimating statistical aggregates on probabilistic data streams

T. S. Jayram, Andrew McGregor, S. Muthukrishnan, Erik Vee
2008 ACM Transactions on Database Systems  
Designing efficient aggregation algorithms for probabilistic data is crucial for handling uncertainty in data-centric applications such as OLAP.  ...  Next, we consider the problem of estimating frequency moments for probabilistic data.  ...  Data stream algorithms for frequency moments are well-studied and use sophisticated techniques based on hashing to produce high quality estimates.  ... 
doi:10.1145/1412331.1412338 fatcat:bm3dzkxb6ffghmkjjgsmale4ne

Probabilistic lossy counting

Xenofontas Dimitropoulos, Paul Hurley, Andreas Kind
2008 Computer communication review  
The probabilistic-based error bound substantially improves the memory consumption of the algorithm.  ...  One of the most efficient and well-known algorithms for finding heavy hitters is lossy counting [29] .  ...  In addition, PLC is based on and improves upon the lossy counting algorithm. Compared with lossy counting, PLC uses a probabilistic error bound instead of a deterministic one.  ... 
doi:10.1145/1341431.1341433 fatcat:i5o2rxzgrzcollnaxxvu72sfca

"It Probably Works"

Tyler McMullen
2015 Applicative 2015 on - Applicative 2015  
This algorithm takes a probabilistic approach to the count-distinct problem.  ...  A programmer's day-to-day work is filled with probabilistic algorithms and data structures. They tend not to be noticed because they work. Indeed, probabilistic algorithms are all around us.  ... 
doi:10.1145/2742580.2742809 fatcat:x54zieqklrftjmtylm2tueybtm

The Story of HyperLogLog: How Flajolet Processed Streams with Coin Flips [article]

Jérémie O. Lumbroso
2018 arXiv   pre-print
This article is a historical introduction to data streaming algorithms that was written as a companion piece to the talk "How Philippe Flipped Coins to Count Data", given on December 16th, 2011, in the  ...  In particular, I am deeply indebted to Nigel Martin for his archival records.  ...  Recent extensions and applications In addition to the statistical application introduced as motivation, Approximate Counting has been used recurrently in a number of different data compression schemes,  ... 
arXiv:1805.00612v2 fatcat:hz4cjp4ejrh6tjyeqsdx2tpfqy

Probabilistic partitioning methods to find significant patterns in ChIP-Seq data

Nishanth Ulhas Nair, Sunil Kumar, Bernard M.E. Moret, Philipp Bucher
2014 Bioinformatics  
Motivation: We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years.  ...  Discovering significant patterns in these data is an important problem for understanding biological mechanisms.  ...  Modified 'Shape-Only' EM algorithm We also propose a shape-only version of the EM algorithm for normalization purposes. For all K classes, the average count frequency is set to 1.  ... 
doi:10.1093/bioinformatics/btu318 pmid:24812341 fatcat:yqjl7qrgb5h63ih747zezdfwrm
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