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- ArticleDecember 2014
Scalable Multi-instance Learning
ICDM '14: Proceedings of the 2014 IEEE International Conference on Data MiningPages 1037–1042https://doi.org/10.1109/ICDM.2014.16Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the ...
- short-paperApril 2014
YouTube monetization: creating user-centric experiences using large scale data
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide WebPages 613–614https://doi.org/10.1145/2567948.2579230Over the last 4 years, YouTube has grown from a viral video sharing site to a platform that fuels a win-win ecosystem for video content creators, advertisers and users. A key driving force behind this successful transformation is building out products/...
- ArticleAugust 2013
Big Data Challenges in Industrial Automation
HoloMAS 2013: Proceedings of the 6th International Conference on Industrial Applications of Holonic and Multi-Agent Systems - Volume 8062Pages 305–316https://doi.org/10.1007/978-3-642-40090-2_27Within the industrial domain including manufacturing a lot of various data is produced. For exploiting the data for lower level control as well as for the upper levels such as MES systems or virtual enterprises, the traditional business intelligence ...
- ArticleMarch 2013
Data Mining and Analysis of Large Scale Time Series Network Data
WAINA '13: Proceedings of the 2013 27th International Conference on Advanced Information Networking and Applications WorkshopsPages 39–43https://doi.org/10.1109/WAINA.2013.92Large amounts of data are readily available and collected daily by global networks worldwide. However, much of the real-time utility of this data is not realized, as data analysis tools for very large datasets, particularly time series data are ...
- ArticleOctober 2012
VISCERAL: towards large data in medical imaging -- challenges and directions
MCBR-CDS'12: Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision SupportPages 92–98https://doi.org/10.1007/978-3-642-36678-9_9The increasing amount of medical imaging data acquired in clinical practice holds a tremendous body of diagnostically relevant information. Only a small portion of these data are accessible during clinical routine or research due to the complexity, ...
- ArticleAugust 2012
An Efficient Join Query Processing Based on MJR Framework
SNPD '12: Proceedings of the 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed ComputingPages 698–703https://doi.org/10.1109/SNPD.2012.85Large data analysis is an important topic in cloud computing. Large-scale data analysis requires complex data analysis, such as Theta-Join, which includes equi-join and nonequi-join. On the other hand, MapReduce is a programming framework in cloud ...
- articleJuly 2010
A Fast Algorithm for Updating and Downsizing the Dominant Kernel Principal Components
SIAM Journal on Matrix Analysis and Applications (SIMAX), Volume 31, Issue 5Pages 2376–2399https://doi.org/10.1137/090774422Many important kernel methods in the machine learning area, such as kernel principal component analysis, feature approximation, denoising, compression, and prediction require the computation of the dominant set of eigenvectors of the symmetric kernel ...
- articleJune 2010
Supervised learning vector quantization for projecting missing weights of hierarchical neural networks
WSEAS Transactions on Information Science and Applications (WSTOISAA), Volume 7, Issue 6Pages 799–808A supervised learning vector quantization (LVQ) method is proposed in this paper to project stratified random samples to infer hierarchical neural networks. Comparing with two traditional methods, i.e., list-wise deletion (LWD), and non-amplified (NA), ...
- research-articleNovember 2009
Interactive remote large-scale data visualization via prioritized multi-resolution streaming
UltraVis '09: Proceedings of the 2009 Workshop on Ultrascale VisualizationPages 1–10https://doi.org/10.1145/1838544.1838545The simulations that run on petascale and future exascale supercomputers pose a difficult challenge for scientists to visualize and analyze their results remotely. They are limited in their ability to interactively visualize their data mainly due to ...
- posterNovember 2008
Pedestrian flow prediction in extensive road networks using biased observational data
GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systemsArticle No.: 67, Pages 1–4https://doi.org/10.1145/1463434.1463512In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, ...
- ArticleOctober 1999
Real-Time Visualization of Scalably Large Collections of Heterogeneous Objects
This paper presents results for real-time visualization ofout-of-core collections of 3D objects. This is a significantextension of previous methods and shows the generality ofhierarchical paging procedures applied both to globalterrain and any objects ...
- ArticleOctober 1999
Real-time visualization of scalably large collections of heterogeneous objects (case study)
This paper presents results for real-time visualization of out-of-core collections of 3D objects. This is a significant extension of previous methods and shows the generality of hierarchical paging procedures applied both to global terrain and any ...