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A Scalable Distributed Stream Mining System for Highway Traffic Data
[chapter]
2006
Lecture Notes in Computer Science
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The central server performs various data mining tasks only in the training and updating stage and sends the interesting patterns to the sensors. ...
Existing distributed stream mining algorithms do not consider the resource limitation on the lightweight devices such as sensors. In this paper, we propose a distributed traffic stream mining system. ...
In contrast, our system doesn't need to transmit any data streams unless updating the patterns. ...
doi:10.1007/11871637_31
fatcat:5y3nsxsbj5dbhbnbscnccpmr6a
TIFIM: Tree based Incremental Frequent Itemset Mining over Streaming Data
2011
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
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) based Incremental Frequent Itemset Mining (TIFIM) over data streams. ...
Since the caching and mining the streaming-data is sensitive, here in this paper a scalable, memory efficient caching and frequent itemset mining model is devised. ...
The resultant patterns of frequent pattern mining over multiple streaming data are collaborative and comparative patterns. ...
doi:10.24297/ijct.v10i5.4149
fatcat:tp3b5nnfwndddivbeuqajk2s2y
Sliding window based weighted maximal frequent pattern mining over data streams
2014
Expert systems with applications
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reflecting recent information over data streams. ...
However, it is hard to mine all of the frequent patterns in the data stream environment since generated patterns are remarkably increased as data streams are continuously extended. ...
Maximal frequent pattern mining over data streams Mining all frequent patterns over data streams as well as static databases can cause numerous computational overheads in general if data sizes are large ...
doi:10.1016/j.eswa.2013.07.094
fatcat:bzv36qsxwfaanlc4rvfqajrqse
Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size
2014
International Journal of Computer Applications
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The results obtained from experiments are significant to prove that the proposed PCFIM is scalable and robust on high speed data streams and miles ahead over existing bench mark models. ...
The said model is referred as Parallel Closed Frequent Itemsets Mining (PCFIM) over High Speed Distributed Data streams by Manifold Varying Size Windows (MVSW). ...
Section 2 presented the associated research in mining frequent itemsets over data streams. ...
doi:10.5120/17662-8479
fatcat:ykuilwrrpfbqbipdnivignntx4
Percolator
2018
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18
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We demonstrate a) the feasibility of incremental pattern mining by walking through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic ...
In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining ...
This motivates us to develop Percolator, a prototype system that combines both incremental mining and parallel mining for feasible pattern detection over graph streams. ...
doi:10.1145/3159652.3160589
dblp:conf/wsdm/ChoudhuryPLWHA18
fatcat:stvjt4szangorntq2pgexcgz64
Mobi-X Architecture Modelling for Mobile Agent using Association Pattern Mining
2019
International journal of recent technology and engineering
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To overcome this crisis, the investigation anticipates a model based data mining approach based on tree structure to achieve co-ordination amongst the mobile agent, effectual modelling and less memory ...
In addition, this model is appropriate for knowledge mining. ...
pattern in mobile data stream. ...
doi:10.35940/ijrte.c4282.098319
fatcat:r2iigfwevfhd5jmksrxxrllbzi
Mining Evolving Web Clickstreams with Explicit Retrieval Similarity Measures
2004
The Web Conference
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Data on the Web is noisy, huge, and dynamic. This poses enormous challenges to most data mining techniques that try to extract patterns from this data. ...
This dynamic and single pass setting can be cast within the framework of mining evolving data streams. ...
MINING EVOLVING USER PROFILES FROM NOISY WEB CLICKSTREAM DATA Recently, data mining techniques have been applied to extract usage patterns from Web log data [24, 26, 20, 7, 21, 19, 22, 3, 18, 17, 25] ...
dblp:conf/www/NasraouiCR04
fatcat:efley3hh3bcbhjsqj3a33vmata
Database research at the University of Illinois at Urbana-Champaign
2002
SIGMOD record
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We h a ve been working on multi-dimensional on-line mining of unusual patterns in stream data, including stream data cubing, clustering, classi cation, and comparison of multiple data streams for mining ...
Our data mining projects focus on scalable and effective data mining methods, their system and architectural support, and their applications in stream data analysis 1], integration of data mining with ...
doi:10.1145/601858.601881
fatcat:ff6mvu2aorherel6rvb46zj6hm
GraphZip: Dictionary-based Compression for Mining Graph Streams
[article]
2017
arXiv
pre-print
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In this paper we present GraphZip, a scalable method for mining interesting patterns in graph streams. ...
users (nodes) over time. ...
Our results show that G Z is clearly more scalable than G M when mining large graphs in the streaming se ing. ...
arXiv:1703.08614v1
fatcat:qgy7votdxzgwlfckmoe5zqrzgq
Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media
2018
EAI Endorsed Transactions on Scalable Information Systems
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But, it is difficult to mine the actionable knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so ...
The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and ...
HUPC and SFPM applied pattern mining process to detect hot topics from Twitter data streams. ...
doi:10.4108/eai.29-5-2018.154807
fatcat:kvgaqlzvxnc7xou4upc5tmrehq
Using retrieval measures to assess similarity in mining dynamic web clickstreams
2005
Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05
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This dynamic and single pass setting can be cast within the framework of mining evolving data streams. ...
While scalable data mining methods are expected to cope with massive Web data, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stoppages and reconfigurations ...
morphing as the stream data mining unfolds. ...
doi:10.1145/1081870.1081921
dblp:conf/kdd/NasraouiCR05
fatcat:pjd7jlanazaddayjx4ydqv7ghm
Novel holistic architecture for analytical operation on sensory data relayed as cloud services
2020
International Journal of Electrical and Computer Engineering (IJECE)
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Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. ...
However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. ...
The study outcome as shown in Figures 4-6 clearly shows that proposed system is better in contrast to existing frequent pattern approach. ...
doi:10.11591/ijece.v10i4.pp4322-4330
fatcat:ukszsbhgrrgjzlafsoevqdikd4
Frequent Pattern Retrieval on Data Streams by using Sliding Window
2021
EAI Endorsed Transactions on Energy Web
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Mining regular patterns from sliding windows over streaming information has become a complex task. ...
In different applications like recommender frameworks and market examination, regular patterns play a significant role in useful mining data. ...
This technique mines the latest and potential patterns in the stream of data. ...
doi:10.4108/eai.13-1-2021.168091
fatcat:ud3pbbgmq5gl3b525spburyl7i
Perspectives on Data Mining
2010
International Journal of Market Research
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Data mining is a changing discipline. ...
Pattern discovery and detection In DM a pattern is an unusual structure or relationship in the data set. ...
About time Already mentioned streaming data, analysis of which has to be incremental and adaptive. Databases collected over time. ...
doi:10.2501/s147078531020103x
fatcat:fplt7imtrrc4xgyqus3ohtlb3a
Data stream mining techniques: a review
2019
TELKOMNIKA (Telecommunication Computing Electronics and Control)
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We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining. ...
However, data stream mining has different challenges making it different from traditional data mining. ...
Data Stream Mining Characteristics Data stream mining has many unique characteristics that make a contrast when compared to traditional data mining as shown in Table 1 . ...
doi:10.12928/telkomnika.v17i2.11752
fatcat:rls2qzcl3vhobmkpycsdwhzplu
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