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A Design Methodology for Distributed Adaptive Stream Mining Systems
2013
Procedia Computer Science
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In this paper, we develop a design methodology for integrated design, simulation, and implementation of dynamic data-driven adaptive stream mining systems. ...
, prototyping, and implementation of alternative distributed design methods for data-driven, adaptive stream mining systems. ...
Conclusion In this paper, we have introduced a design framework for distributed, adaptive stream mining. ...
doi:10.1016/j.procs.2013.05.425
fatcat:evsnr3qu5zg55m2vyfae5nfcsm
Survey on Swarm Search Feature Selection for Big Data Stream Mining
2017
International Journal of Computer Applications
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Some of the research work illustrates the different kinds of optimization methods for data stream mining would lead to tremendous changes in big data. ...
Big data defines a knowledge used to record and execute the data set and it has the structured, semi structured and unstructured data that has to be mined for valuable data. ...
Fig 4 . 4 Performance
A distributed Adaptive Model Procedures for Mining Big Data Streams are proposed by Vu et. al (2014). ...
doi:10.5120/ijca2017912720
fatcat:tz7acbzbube5hnqq7q374sii3e
Data-Driven Stream Mining Systems for Computer Vision
[chapter]
2014
Advances in Computer Vision and Pattern Recognition
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In this chapter, we discuss the state of the art and future challenges in adaptive stream mining systems for computer vision. ...
Adaptive stream mining in this context involves the extraction of knowledge from image and video streams in real-time, and from sources that are possibly distributed and heterogeneous. ...
for Computer Vision
Data-Driven Stream Mining Systems for Computer Vision
Data-Driven Stream Mining Systems for Computer Vision ...
doi:10.1007/978-3-319-09387-1_12
fatcat:am4bf2g7tjbd3h3vefpeaxvvum
Knowledge discovery from sensor data (SensorKDD)
2010
SIGKDD Explorations
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On one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating ...
Extracting knowledge and emerging patterns from sensor data is a nontrivial task. The challenges for the knowledge discovery community are expected to be immense. ...
The United States Government retains, and the publisher by accepting the article for publication, acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, world-wide ...
doi:10.1145/1809400.1809417
fatcat:jrtrixjfzzgo3bdlelcyldihom
Knowledge discovery from sensor data (SensorKDD)
2008
SIGKDD Explorations
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On one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating ...
Extracting knowledge and emerging patterns from sensor data is a nontrivial task. The challenges for the knowledge discovery community are expected to be immense. ...
The United States Government retains, and the publisher by accepting the article for publication, acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, world-wide ...
doi:10.1145/1540276.1540297
fatcat:72jirtrxibbrpmpfcwrivmedjm
Enhancing Veracity of IoT Generated Big Data in Decision Making
2018
2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
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We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. ...
, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management. ...
A parallel data cleaning algorithm is designed by Chen et al. [17] for system data with missing information. Zhang et al. ...
doi:10.1109/percomw.2018.8480371
dblp:conf/percom/LiuTSSRRKS18
fatcat:qnx5ebuqubezvd4zzervjufwjq
IoT Big Data Stream Mining
2016
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
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This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. ...
The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza. ...
IoT Distributed Big Data Stream Mining In this part we focus on open source software tools for distributed processing used nowadays as Spark, Flink, Storm, Samza, and how to do data stream mining with ...
doi:10.1145/2939672.2945385
dblp:conf/kdd/MoralesBKGF16
fatcat:gothvopc25b5hmoagbeix2rkba
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Based Healthcare Applications
2012
Advanced Computing An International Journal
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These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health ...
This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a significant summary of existing approaches ...
Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. ...
doi:10.5121/acij.2012.3408
fatcat:4qsuijdiebfblpt22b3r6bmnii
Connected-Vehicles Applications Are Emerging [Connected Vehicles]
2016
IEEE Vehicular Technology Magazine
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In Figure 5 is adapted the general model for data stream mining proposed by Nguyen et al. (2015) :
Figure 5. A general model for data stream mining in CVs. ...
identify desirable properties of learning systems for efficient mining continuous, high-volume, open-ended data streams: (a) Require small constant time per data example. ...
doi:10.1109/mvt.2015.2508322
fatcat:gtlzk33iovcqrfcebfsrjjdxoe
Mathematical model of jet control of adaptability of mine turbomachines
2020
E3S Web of Conferences
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Improving the methodology for the aerodynamic calculation of circular lattice of aerodynamic profiles and the development of radial aerodynamic schemes with increased adaptability made it possible to derive ...
The possibility of a significant increase in aerodynamic loading, adaptability and efficiency of mine turbomachines, made according to radial aerodynamic schemes with vortex chambers built into the blades ...
The lack of adaptability of turbomachines actualizes the task of developing a methodology for designing and creation of aerodynamically adaptive turbomachines that adequately and at the same time economically ...
doi:10.1051/e3sconf/202017705008
fatcat:rtl3w2qdfrettjd6egosj6zkuu
Next challenges for adaptive learning systems
2012
SIGKDD Explorations
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We identify six forthcoming challenges in designing and building adaptive learning (prediction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrating ...
Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. ...
A way to create faster methods is using software data stream mining methodologies, where new arriving elements have to be processed essentially in real time. ...
doi:10.1145/2408736.2408746
fatcat:axeyhnowwnb6jcnpdvrhtqbvma
Guest editor's introduction: special issue on quality issues, measures of interestingness and evaluation of data mining models
2015
Journal of Intelligent Information Systems
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There are many data mining algorithms and methodologies for various fields and various problematic. ...
Every one should answer these questions, and assessing the quality and the performance of a data mining process is a critical issue. ...
The authors also present a method for designing new stream subspace and projected clustering algorithms, and a novel method for using available offline subspace clustering measures for data streams within ...
doi:10.1007/s10844-015-0370-7
fatcat:jysj5bmw2be2lhunmfgnhmfzv4
Differential privacy based classification model for mining medical data stream using adaptive random forest
2021
Acta Universitatis Sapientiae: Informatica
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In this work, a classification model with differential privacy is proposed for mining the medical data stream using Adaptive Random Forest (ARF). ...
Most typical data mining techniques are developed based on training the batch data which makes the task of mining the data stream represent a significant challenge. ...
Methodology The main aim of this work is to design and implement a classification model for stream data based on adaptive random forest, including differential privacy. ...
doi:10.2478/ausi-2021-0001
fatcat:yv3m3sbrareq5jjjf7lky36lfe
Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands
2019
Sensors
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The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the ...
The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. ...
Adaptive Learning Strategy-Data-Stream-Mining-Based The first part of our proposed adaptive learning methodology consists of having a variable number of θ parameters or previous instances considered in ...
doi:10.3390/s19102388
fatcat:2dtvixkkanhwhemb32vv2atotq
Process Mining in Big Data Scenario
2015
International Symposium on Data-Driven Process Discovery and Analysis
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The aim of this work is to present and discuss a brief review of the literature reporting most of the Process Mining chances that meet Big Data and the challenges carried out, showing the critical aspects ...
In the last years the management and analysis of big data generated from information systems are becoming one of the most important topics in the Business Process Intelligence (BPI). ...
An interesting approach presents a methodology designed for consistent process mining algorithms in a Big Data context [5] . ...
dblp:conf/simpda/AzziniD15
fatcat:nliit3k5gjaatlsa3n6gseayym
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