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Mining and Forecasting of Big Time-series Data

Yasushi Sakurai, Yasuko Matsubara, Christos Faloutsos
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
of time-series mining and tensor analysis.  ...  We review the state of the art in four related fields: (1) similarity search and pattern discovery, (2) linear modeling and summarization, (3) non-linear modeling and forecasting, and (4) the extension  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, ARL, or other  ... 
doi:10.1145/2723372.2731081 dblp:conf/sigmod/SakuraiMF15 fatcat:dsmv2sqs35bm5ifeqr4qnuz6ty

Real-time Forecasting of Non-linear Competing Online Activities

Thinh Minh Do, Yasuko Matsubara, Yasushi Sakurai
2020 Journal of Information Processing  
In this paper, we present RFCast, a unifying adaptive non-linear method for forecasting future patterns of co-evolving data streams.  ...  In this paper, we present RFCast, an efficient and effective non-linear method for forecasting future dynamics of co-evolving data streams.  ...  FUNNEL [18] is a novel non-linear method of mining spatially coevolving epidemic sequences using tensor analysis technique, while EcoWeb [14] has been one of the first methods that effectively discover  ... 
doi:10.2197/ipsjjip.28.333 fatcat:isu42tkulngpnjwgwuvxx7xyva

Non-linear Time-series Analysis of Social Influence

Thinh Minh Do
2016 Proceedings of the 2016 on SIGMOD'16 PhD Symposium - SIGMOD'16 PhD  
In this paper, we present ∆-SPOT, a non-linear model for analysing large scale web search data, and its fitting algorithm. ∆-SPOT can forecast long-range future dynamics of the keywords/queries.  ...  We use the Google Search, Twitter and MemeTracker data set for extensive experiments, which show that our method outperforms other non-linear mining methods.  ...  Social activity analysis. The work described in [7] studied the rise and fall patterns in the information diffusion process through online social media.  ... 
doi:10.1145/2926693.2929902 dblp:conf/sigmod/Do16 fatcat:mo6ckmlnzrcdfgklewtedxayvi

Mining Concurrent Topical Activity in Microblog Streams [article]

A. Panisson, L. Gauvin, M. Quaggiotto, C. Cattuto
2014 arXiv   pre-print
We mine the temporal structure of topical activity by using two methods based on non-negative matrix factorization.  ...  Streams of user-generated content in social media exhibit patterns of collective attention across diverse topics, with temporal structures determined both by exogenous factors and endogenous factors.  ...  The Authors aknowledge partial support from the Lagrange Project of the ISI Foundation funded by the CRT Foundation, from the Q-ARACNE project funded by the Fondazione Compagnia di San Paolo, and from  ... 
arXiv:1403.1403v1 fatcat:l5qudfuxobdhhaz3aeja3jcnsu

ICDM 2009 Program

2009 2009 Ninth IEEE International Conference on Data Mining  
on Distributed Stream Processing Systems Deepak Turaga, Hyunggon Park, Rong Yan, and Olivier Verscheure Demo Wednesday 10:30AM-12:30PM MT Matrix and Tensor Feature Selection in the Tensor Product  ...  using Heuristic-Based Polygonal Clustering Deepti Joshi, Leen-Kiat Soh, and Ashok Samal Short Wednesday 10:30AM-12:30PM STD Spatial and Temporal Data Mining Active Selection of Sensor Sites in  ... 
doi:10.1109/icdm.2009.151 fatcat:xvzjtpkkvbh25k5lmjslaf2jdi

Evolution in Social Networks: A Survey [chapter]

Myra Spiliopoulou
2011 Social Network Data Analytics  
There is much research on social network analysis but only recently did scholars turn their attention to the volatility of social networks. An abundance of questions emerged.  ...  This survey organizes advances on evolution in social networks into a common framework and gives an overview of these different perspectives.  ...  In the eld of Knowledge Discovery from Data, there is a distinction between mining static data and mining a data stream.  ... 
doi:10.1007/978-1-4419-8462-3_6 fatcat:eorguarigndvnjdy3uosmxnige

Incremental pattern discovery on streams, graphs and tensors

Jimeng Sun
2008 SIGKDD Explorations  
fraud detection tries to find fraudulent activities from a large number of transactions in realtime.  ...  In this thesis proposal, we first investigate a powerful data model tensor stream (TS) where there is one tensor per timestamp.  ...  In theory, the generalization to tensors is not hard as described by Drineas et al. [15] . However, there are still a lot of the practical concerns on prototyping the idea.  ... 
doi:10.1145/1540276.1540284 fatcat:l7tkqca3gzdejhdktai2u6ouji

Individual and Group Dynamics in the Reality Mining Corpus

Charlie K. Dagli, William M. Campbell
2012 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing  
Though significant progress has been made in recent years, traditional work in social networks has focused on static network analysis or dynamics in a large-scale sense.  ...  Experimental results and discussion suggest temporal information has great potential for improving both individual and group level understanding of real-world, dense social network data.  ...  ACKNOWLEDGMENT The authors would like to thank Nathan Eagle and Professor Sandy Pentland of the Human Dynamics Laboratory at MIT Media Lab for access to, and helpful discussions regarding the Reality Mining  ... 
doi:10.1109/socialcom-passat.2012.75 dblp:conf/socialcom/DagliC12 fatcat:qkrj7wv2rbctrdltludeletghm

SCENT

Yu-Ru Lin, K. Selçcuk Candan, Hari Sundaram, Lexing Xie
2011 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
We propose SCENT, an innovative, scalable spectral analysis framework for internet scale monitoring of multirelational social media data, encoded in the form of tensor streams.  ...  In SCENT, we focus on the computational cost of structural change detection in tensor streams. We extend compressed sensing (CS) to tensor data.  ...  Figure 2 illustrates a social data stream in tensor form. In this example, the data stream is represented as a sequence of tensors, each representing a snapshot of the social network.  ... 
doi:10.1145/2037676.2037686 fatcat:4b25ljdh6rbztjmorizls3r4ju

Data Mining and Knowledge Discovery [chapter]

Chao Zhang, Jiawei Han
2021 The Urban Book Series  
In this chapter, we present recent developments in data-mining techniques for urban activity modeling, a fundamental task for extracting useful urban knowledge from social-sensing data.  ...  Such digital data are a result of social sensing: namely people act as human sensors that probe different places in the physical world and share their activities online.  ...  As massive social-sensing data stream in, it is an important yet challenging problem to design on-line learning algorithms that can handle large-scale streaming data efficiently.  ... 
doi:10.1007/978-981-15-8983-6_42 fatcat:gxnx3jgu4fcqvbieg3ltmdovk4

Beyond streams and graphs

Jimeng Sun, Dacheng Tao, Christos Faloutsos
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
streaming data, text, graphs, social networks and many more.  ...  Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA.  ...  This work is also supported in part by the Pennsylvania Infrastructure Technology Alliance (PITA), a partnership of Carnegie Mellon, Lehigh University and the Commonwealth of Pennsylvania's Department  ... 
doi:10.1145/1150402.1150445 dblp:conf/kdd/SunTF06 fatcat:6eyy2onjare7jaq4j36ofwdhaq

SimTensor: A synthetic tensor data generator [article]

Hadi Fanaee-T, Joao Gama
2016 arXiv   pre-print
The most innovative part of SimTensor is this that can generate temporal tensors with periodic waves, seasonal effects and streaming structure. it can apply constraints such as non-negativity and different  ...  The source code and binary versions of SimTensor is available for download in http://www.simtensor.org.  ...  multi-linear data Orthogonal HOSVD (De Lathauwer et al., 2000b) , HOOI (De Lathauwer et al., 2000a) or similar High quality multi-linear data Stochastic Non-negative algorithms like (Carroll et al  ... 
arXiv:1612.03772v1 fatcat:i3j2hkd33vgz5bz4wwljeszfie

Chatbots Employing Deep Learning for Big Data

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
But these DNN are unable to handle big data involving large amounts of heterogeneous data. While Tensor Auto Encoder which overcomes this drawback is time-consuming.  ...  This paper has proposed the Chatbot to handle the big data in a manageable time.  ...  It is denoted as a systematical model for illustration of big data, then storing, mining and analyzing accompanied by a theory of tensor.  ... 
doi:10.35940/ijitee.i8017.0981119 fatcat:eszidvr2gndafbomcxvkhnhyiu

A survey of multilinear subspace learning for tensor data

Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos
2011 Pattern Recognition  
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications.  ...  It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining  ...  Acknowledgment The authors would like to thank the anonymous reviewers for their insightful comments, which have helped to improve the quality of this paper.  ... 
doi:10.1016/j.patcog.2011.01.004 fatcat:6puqzxrohfawraeiyid6633wdm

Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams

Tasuku Kimura, Yasuko Matsubara, Koki Kawabata, Yasushi Sakurai
2022 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining  
CCS CONCEPTS • Information systems → Data stream mining; • Mathematics of computing → Nonlinear equations.  ...  Our proposed method is designed as a dynamic and flexible system, and is based on a unified non-linear differential equation.  ...  be infected in the future, we can manage pandemic risks in advance by, for example, avoiding frequent social activities and controlling hospital occupancy rates [6] .  ... 
doi:10.1145/3534678.3539078 fatcat:g65ndlkvxnhnjpuqxrfldn2zne
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