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Introduction to spatio-temporal data driven urban computing

Shuo Shang, Kai Zheng, Panos Kalnis
2020 Distributed and parallel databases  
., "A parameter-level parallel optimization algorithm for large-scale spatio-temporal data mining," propose an efficient parameter-level parallel optimization algorithm for large-scale spatio-temporal  ...  data mining.  ... 
doi:10.1007/s10619-020-07300-3 fatcat:e7js4tbxuneg7nzqakksvdtlum

Review of Power Spatio-Temporal Big Data Technologies for Mobile Computing in Smart Grid

Ying Ma, Chao Huang, Yu Sun, Guang Zhao, Yunjie Lei.
2019 IEEE Access  
INDEX TERMS Mobile computing, data processing, smart grids, spatio-temporal big data.  ...  Recently, power spatio-temporal big data (PSTBD) technology of smart grid based on mobile computing has experienced explosive growth.  ...  Spatio-temporal big data can optimize the parameter estimation, take corrective measures for emergencies and analyze late emergencies.  ... 
doi:10.1109/access.2019.2957181 fatcat:txr7efdc3vd5lk2c3v2khm3ooa

An integrated intelligent system for estimating and updating a large-size matrix

Ting Yu
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
This unique data analysis algorithm runs over the parallel computer to enable the system to estimate a matrix of the size up to 3700by-3700.  ...  The data mining component is based on a unique modelling algorithm which constructs the matrix from the historical data and the spatial data simultaneously.  ...  ACKNOWLEDGMENT Author would like to give thanks to A/Prof. Regina Burachik and Dr.  ... 
doi:10.1109/icsmc.2009.5345936 dblp:conf/smc/Yu09 fatcat:uoo4mcv5xnby7lpacv36sh363a

Storing and Querying Large-Scale Spatio-Temporal Graphs with High-Throughput Edge Insertions [article]

Mengsu Ding, Muqiao Yang, Shimin Chen
2020 arXiv   pre-print
We propose and evaluate PAST, a framework for efficient PArtitioning and query processing of Spatio-Temporal graphs. Experimental results show that PAST successfully achieves the above goals.  ...  Compared with static graphs, spatio-temporal graphs have very different characteristics, presenting more significant challenges in data volume, data velocity, and query processing.  ...  Several recent studies introduce light-weight algorithms for partitioning large-scale dynamic graphs [20, 45, 40, 29] .  ... 
arXiv:1904.09610v2 fatcat:6ysnlkkc5vam7nwscdqdk52mdy

Intelligent simulation tools for mining large scientific data sets

Feng Zhao, Chris Bailey-Kellogg, Xingang Huang, Iván Ordóñez
1999 New generation computing  
These applications, which include weather data interpretation, distributed control optimization, and spatio-temporal di usion-reaction pattern analysis, demonstrate that intelligent simulation tools are  ...  Prototype intelligent s i m ulation tools have been constructed for interpreting massive data sets from physical elds and for designing engineering systems.  ...  Acknowledgment This paper describes research conducted at Ohio State University a n d Xerox P alo Alto Research Center, supported in part by FZ's ONR YI grant N00014-97-1-0599, NSF NYI grant CCR-9457802  ... 
doi:10.1007/bf03037240 fatcat:cut4xpervbegllpmmndj3ullsy

On Using Clustering for the Optimization of Hydrological Simulations

Elnaz Azmi
2018 2018 IEEE International Conference on Data Mining Workshops (ICDMW)  
However, the simulation of such a large-scale dynamical system is compute-intensive, and hence time consuming.  ...  Accurate water-related predictions and decisionmaking require a simulation of hydrological systems in high spatio-temporal resolution.  ...  The execution of a high spatio-temporal resolution simulation is very time consuming.  ... 
doi:10.1109/icdmw.2018.00215 dblp:conf/icdm/Azmi18 fatcat:g5rhkoeio5cudlvjoz6ltsqpbm

A Survey on Trajectory Big Data Processing

Amina Belhassena
2018 International Journal of Performability Engineering  
As the massive trajectory data processing exceeds the power of centralized approaches used previously, in this paper, we survey various existing tools used to process large-scale trajectory data in a distributed  ...  Therefore, large-scale trajectory data has received increasing attention in research fields as well as in industry.  ...  Acknowledgements This paper was partially supported by NSFC grant U1509216,61472099, National Sci-Tech Support Plan 2015BAH10F01, the Scientific Research Foundation for the Returned Overseas Chinese Scholars  ... 
doi:10.23940/ijpe.18.02.p13.320333 fatcat:m74w3cfajrbzpamzpghfyrm6am

The Simpler The Better

Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction  ...  We further design a series of optimization techniques for efficient model training and updating.  ...  ACKNOWLEDGMENT We are grateful to anonymous reviewers for their constructive comments on this work. Yongxin Tong is supported in part by  ... 
doi:10.1145/3097983.3098018 dblp:conf/kdd/TongCZCWYYL17 fatcat:7vmt7jczqfdnndraqmarw4w7uq

Managing massive trajectories on the cloud

Jie Bao, Ruiyuan Li, Xiuwen Yi, Yu Zheng
2016 Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '16  
To utilize the large scale trajectory data efficiently and effectively, cloud computing platforms, e.g., Microsoft Azure, are the most convenient and economic way.  ...  To this end, we design and implement a holistic cloud-based trajectory data management system on Microsoft Azure to bridge the gap between the massive trajectory data and the urban applications.  ...  scale trajectory data mining and querying, and hence to offer the help for the urban com-puting applications.  ... 
doi:10.1145/2996913.2996916 dblp:conf/gis/0003LYZ16 fatcat:fkwou4fsy5cb3l6vco5x6pn66a

Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning

P. Heas, M. Datcu
2005 IEEE Transactions on Geoscience and Remote Sensing  
They represent a large data volume with a rich information content and may open a broad range of new applications.  ...  This paper presents an information mining concept which enables a user to learn and retrieve spatio-temporal structures in SITS.  ...  ACKNOWLEDGMENT The authors would like to thank A. Giros for stimulating discussions and for carefully preprocessing the data.  ... 
doi:10.1109/tgrs.2005.847791 fatcat:uiarpp3dw5e4figd4fyktlzx7e

Big Data New Frontiers: Mining, Search and Management of Massive Repositories of Solar Image Data and Solar Events [chapter]

Juan M. Banda, Michael A. Schuh, Rafal A. Angryk, Karthik Ganesan Pillai, Patrick McInerney
2014 Advances in Intelligent Systems and Computing  
While building a content-based image retrieval system for NASA's Solar Dynamics Observatory mission, we have discovered research problems that can be addressed by the use of big data processing techniques  ...  This paper presents the current status of our work with solar image data and events, our shift towards using big data methodologies, and future directions for big data processing in solar physics.  ...  New algorithms will need to be developed to fit the context of spatio-temporal data analysis for big data sources.  ... 
doi:10.1007/978-3-319-01863-8_17 fatcat:wroagiuelbclta5pp63dnu2s5a

Spatio-temporal random fields: compressible representation and distributed estimation

Nico Piatkowski, Sangkyun Lee, Katharina Morik
2013 Machine Learning  
For finding the best parameters via maximum likelihood estimation, we provide a separable optimization algorithm that can be performed independently in parallel in each graph node.  ...  The only drawback will be the cost for inference, storing and optimizing a very large number of parameters-not uncommon when we apply them for real-world applications.  ...  Acknowledgements We thank the reviewers for their suggestions that have helped us improve our manuscript.  ... 
doi:10.1007/s10994-013-5399-7 fatcat:gsll5ytgknh3nd4ollu72voa7y

Mining geophysical data for knowledge

E. Mesrobian, R. Muntz, E. Shek, S. Nittel, M. La Rouche, M. Kriguer, C. Mechoso, J. Farrara, P. Stolorz, H. Nakamura
1996 IEEE Expert  
PLORATORY DATA MINING AND analysis for scientific hypothesis testing or phenomenon detection is an iterative, successive-refinement process.  ...  Scientists apply a preliminary model on the data and then use the outcome of a series of experiments to refine the model and methodology.  ...  We thank Bob Haskins, Tientien Li, and Charles Thompson of JPL for Glint and for validating Oasis. We thank Andy Berkin and Michael Orton of JPL for integrating Linkwinds into the Oasis framework.  ... 
doi:10.1109/64.539015 fatcat:kuvc2usq7fcufnzrikfy5smssu

Exploiting Spatio-temporal Tradeoffs for Energy-Aware MapReduce in the Cloud

Michael Cardosa, Aameek Singh, Himabindu Pucha, Abhishek Chandra
2011 2011 IEEE 4th International Conference on Cloud Computing  
MapReduce is a distributed computing paradigm widely used for building large-scale data processing applications.  ...  We describe a unique spatio-temporal tradeoff that includes efficient spatial fitting of VMs on servers to achieve high utilization of machine resources, as well as balanced temporal fitting of servers  ...  INTRODUCTION MapReduce [1] has emerged as a popular computing paradigm for large-scale data processing and analysis, given its ability to scale-out to large clusters of machines.  ... 
doi:10.1109/cloud.2011.68 dblp:conf/IEEEcloud/CardosaSPC11 fatcat:d4srqv3v3rd6pbccrubd3e75um

On the path to sustainable, scalable, and energy-efficient data analytics: Challenges, promises, and future directions

Sriram Lakshminarasimhan, Prabhat Kumar, Wei-keng Liao, Alok Choudhary, Vipin Kumar, Nagiza F. Samatova
2012 2012 International Green Computing Conference (IGCC)  
We propose a number of future directions that could be pursued on the path to sustainable data analytics at scale.  ...  As scientific data is reaching exascale, scalable and energy efficient data analytics is quickly becoming a top notch priority.  ...  Oak Ridge National Laboratory is managed by UT-Battelle for the LLC U.S. D.O.E. under contract no. DEAC05-00OR22725.  ... 
doi:10.1109/igcc.2012.6322265 dblp:conf/green/LakshminarasimhanKLCKS12 fatcat:dremvfojkre5vm2n7qhimzxjgm
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