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Probabilistic counting algorithms for data base applications
1985
Journal of computer and system sciences (Print)
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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
2017
International Journal of Computer Applications
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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]
2006
arXiv
pre-print
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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
2016
International Journal of Computer Applications
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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
2011
Proceedings of the fourth ACM conference on Wireless network security - WiSec '11
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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
2006
IEEE/ACM Transactions on Networking
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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]
2014
Lecture Notes in Computer Science
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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]
2019
arXiv
pre-print
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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
2007
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '07
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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
2014
International Journal of Computer Applications
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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
2008
ACM Transactions on Database Systems
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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
2008
Computer communication review
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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"
2015
Applicative 2015 on - Applicative 2015
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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]
2018
arXiv
pre-print
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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
2014
Bioinformatics
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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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