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Knowledge discovery from data streams

João Gama, Jesus Aguilar-Ruiz, João Gama, Jesus Aguilar-Ruiz
2007 Intelligent Data Analysis  
Examples include customer click streams, telephone records, large sets of web pages, multimedia data, and sets of retail chain transactions. These sources of continuous data are called data streams.  ...  Standard machine learning algorithms work on static data. Most of the times, all the data is loaded into memory and the learning task is solved by performing multiple scans over the training data.  ...  The common concept in all the papers is that learning occurs while data continuously flow.  ... 
doi:10.3233/ida-2007-11101 fatcat:kkmc3coipfhx5etpz4vozavwby

Context-Aware Collaborative Data Stream Mining in Ubiquitous Devices [chapter]

João Bártolo Gomes, Mohamed Medhat Gaber, Pedro A. C. Sousa, Ernestina Menasalvas
2011 Lecture Notes in Computer Science  
This paper motivates and describes a novel Context-aware Collaborative data stream mining system CC-Stream that allows intelligent mining and classification of time-changing data streams on-board ubiquitous  ...  Such knowledge is associated with context information that captures the system state for a particular underlying concept.  ...  This work is focused on collaborative data stream mining on-board ubiquitous devices in complex ubiquitous environments.  ... 
doi:10.1007/978-3-642-24800-9_5 fatcat:2syrymi7nzhjddnj6trmnbmjmy


2013 International Journal of Information Technology and Decision Making  
As an illustrative example, collaborative spam filtering 4 is one of the possible applications for the proposed collaborative learning approach.  ...  analysis in such ubiquitous computing environments 14, 18 This work is focused on collaborative data stream mining on-board these ubiquitous devices.  ... 
doi:10.1142/s0219622013500375 fatcat:x3e7r2jwrzeorbqm3k4zpdbwby

Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
2018 Future generations computer systems  
Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in  ...  When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.  ...  Data volatility From the early works on recommender systems that capture the user interest drifts [69, 70] , to more recent works that model the dynamics of user interest in activity streams [71] , the  ... 
doi:10.1016/j.future.2017.09.015 fatcat:jdllbp6snfckfj7xfpu4xycfii

Learning Relational User Profiles and Recommending Items as Their Preferences Change

Zaigham Faraz Siddiqui, Eleftherios Tiakas, Panagiotis Symeonidis, Myra Spiliopoulou, Yannis Manolopoulos
2015 International journal on artificial intelligence tools  
It then employs collaborative filtering (CF) to recommend new items to the customers based on their group similarity.  ...  To evaluate the working of xStreams, we use a multi-relational data generator for streams. We evaluate xStreams on real and synthetic datasets.  ...  Content-based, collaborative filtering and hybrid recommenders In the context of recommender systems, there exist three parallel approaches: collaborative filtering (CF), content-based filtering (CB) and  ... 
doi:10.1142/s0218213015400096 fatcat:l5jzt3yt4nehjnzza6gekq3oni

Coupled Variational Recurrent Collaborative Filtering [article]

Qingquan Song, Shiyu Chang, Xia Hu
2019 arXiv   pre-print
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner.  ...  To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation  ...  [46] propose a streaming ranking-based framework based on Bayesian Personalized Ranking [29] to address the user interest drifting as well as system overload problem.  ... 
arXiv:1906.04386v1 fatcat:ldsxz2xb2ffu7ju4mid56suxye

An Efficient Method for Dynamic Recommendation

Ben-jie FAN, Ling CHEN, Chen XU
2017 DEStech Transactions on Computer Science and Engineering  
The experimental results show that the accuracy of the recommendation results by our method (UBCFT) is improved compared with the existing collaborative filtering algorithms.  ...  using the static data.  ...  [13] presented a collaborative filtering method based on label information of the items.  ... 
doi:10.12783/dtcse/wcne2016/5104 fatcat:kpzzyezcabbilhgd7b4pmfhhfa

Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics

Alexandros Bousdekis, Nikos Papageorgiou, Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas
2020 IEEE Access  
, and non-stationarity of real-world data streams.  ...  INDEX TERMS Big data, machine learning, data analytics, Internet of Things, non-stationary time-series.  ...  His research interests include proactive computing, event processing, machine learning, cloud computing, and e-collaboration.  ... 
doi:10.1109/access.2020.2994933 fatcat:qxggkp4fpnbcdc4w4whcjrvai4

High-performance face tracking

Nenad Markuš, Miroslav Frljak, Igor S. Pandžić, Jörgen Ahlberg, Robert Forchheimer
2012 Proceedings of the 3rd Symposium on Facial Analysis and Animation - FAA '12  
Acknowledgements The core tracking algorithm has been developed by Visage Technologies AB, Linköping, Sweden and work continues in collaboration with the company.  ...  The obtained data is passed to the information form of extended Kalman filter (EIF) [Bar-Shalom et al. 2001 ]. The filter recursively estimates the rotation, translation and action parameters.  ...  It is robust, resistant to rapid changes in pose and facial expressions, does not suffer from drifting and is modestly computationally expensive. The tracker runs in real-time on mobile devices.  ... 
doi:10.1145/2491599.2491600 dblp:conf/faa/MarkusFPAF12 fatcat:nu7ztyme7nalnjnzsuoy2ajmwy

WebKDD 2005

Olfa Nasraoui, Osmar R. Zaïane, Myra Spiliopoulou, Bamshad Mobasher, Brij Masand, Philip S. YU
2005 SIGKDD Explorations  
on Knowledge Discovery and Data Mining (KDD 2005), August 21-24, 2005, in Chicago, Illinois.  ...  In this report, we summarize the contents and outcomes of the recent WebKDD 2005 workshop on Web Mining and Web Usage Analysis that was held in conjunction with the 11th ACM SIGKDD International Conference  ...  Vector Machines (SVM) in the collaborative filtering framework using data sets with different properties.  ... 
doi:10.1145/1117454.1117475 fatcat:kd2fftl6n5dc7cen5ssvqeq6zm

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes

San Gultekin, John Paisley
2014 2014 IEEE International Conference on Data Mining  
We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models.  ...  This is naturally interpreted as a Kalman filter with multiple interacting state space vectors.  ...  INTRODUCTION Collaborative filtering is a general and effective method for making pairwise predictions based on historical data.  ... 
doi:10.1109/icdm.2014.61 dblp:conf/icdm/GultekinP14 fatcat:rqxqacvuebewrjzrfv4qxi4voy

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes [article]

San Gultekin, John Paisley
2015 arXiv   pre-print
We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models.  ...  This is naturally interpreted as a Kalman filter with multiple interacting state space vectors.  ...  INTRODUCTION Collaborative filtering is a general and effective method for making pairwise predictions based on historical data.  ... 
arXiv:1501.05624v1 fatcat:q3v6i5ocbne4tga7dyd3h5a5ly


Nayer Wanas, Ahmed Farouk, Dina Said, Nabila Khodeir, Magda Fayek
2019 International Journal of Computer Science & Information Technology (IJCSIT)  
Data modelling in IKCD uses k-means clustering to determine the occurrence of a drift while avoiding the dependency on the availability of data labels.  ...  However, data streams are highly prone to the phenomena of concept drift, in which the data distribution changes over time.  ...  One of the major issues facing RSs is that the user's interest, and/or the item features themselves may change over time especially for data streams that online users interact with [4] .  ... 
doi:10.5121/ijcsit.2019.11107 fatcat:bynnlce7ujhshkqdhooswuuyru

The Palomar-Quest digital synoptic sky survey

S.G. Djorgovski, C. Baltay, A.A. Mahabal, A.J. Drake, R. Williams, D. Rabinowitz, M.J. Graham, C. Donalek, E. Glikman, A. Bauer, R. Scalzo, N. Ellman (+1 others)
2008 Astronomical Notes - Astronomische Nachrichten  
Finally, we discuss some issues and challenges posed by the real-time analysis and scientific exploitation of massive data streams from modern synoptic sky surveys.  ...  We describe briefly the Palomar-Quest (PQ) digital synoptic sky survey, including its parameters, data processing, status, and plans.  ...  We thank many collaborators who have made essential contributions to the survey, and the staff of Palomar Observatory for their tireless efforts during the survey operations.  ... 
doi:10.1002/asna.200710948 fatcat:5qlagms6zvfh3ezvqccfngyzde

An adaptive personalized news dissemination system

Ioannis Katakis, Grigorios Tsoumakas, Evangelos Banos, Nick Bassiliades, Ioannis Vlahavas
2008 Journal of Intelligent Information Systems  
of the content not only per user but also per each feed a user is subscribed to, and c) the ability for every user to watch a more abstracted topic of interest by filtering through a taxonomy of topics  ...  PersoNews is freely available for public use on the WWW (  ...  We have designed a specialized framework for the problem of text stream classification with concept drift.  ... 
doi:10.1007/s10844-008-0053-8 fatcat:zwyvnbvj75fojn3fyrondpgmga
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