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Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics

Milad Makkie, Xiang Li, Tianming Liu, Shannon Quinn, Binbin Lin, Jieping Ye
2016 2016 IEEE International Conference on Big Data (Big Data)  
Performance and accuracy of D-r1DL on both individual and group-wise fMRI Human Connectome Project (HCP) dataset shows that the proposed framework is highly scalable.  ...  We propose to address these shortcomings in this work, based on previous success in using dictionary learning method for functional network decomposition studies on fMRI data.  ...  Secondly, fast and scalable tools for analyzing the large-scale data at group or population level are much in needed.  ... 
doi:10.1109/bigdata.2016.7841000 dblp:conf/bigdataconf/MakkieLLQLY16 fatcat:zk7qohvpi5ftjakakmfxtj7u2q

Data centric computing for internet scale enterprises

Yuqing Gao
2013 Proceedings of the ACM/SPEC international conference on International conference on performance engineering - ICPE '13  
■ Deep analytics on data at rest -Finding of non-trivial relations -Competitive advantage ■ Low-latency analytics on massive and rapidly generating data, i.e., data in motion -Timeliness in decision making  ...  -Interactive: client facing ■ Use of operational and transactional data for analytics: -Concurrency of high velocity data acquisition and analytics on same data source • Need for low-latency analytics  ...  specified by the pointer • No server-side code or CPU involvement -zero server CPU utilization  Very fast, near wire-speed remote access • Develop an elastic, scalable, language-independent runtime platform  ... 
doi:10.1145/2479871.2479924 dblp:conf/wosp/Gao13 fatcat:74ooahyt5rannjuywj3nv3zwoq

OpenMRS Analytics Engine: A FHIR Based Approach [chapter]

Allan Kimaina, Jonathan Dick, Bashir Sadjad
2022 Studies in Health Technology and Informatics  
To address these two critical needs, we present a scalable analytics pipeline architecture, designed from the bottom-up to harness the power of FHIR (Fast Healthcare Interoperability Resources) for improving  ...  collaborative efforts in health data analytics and indicator reporting.  ...  on operational EHR performance while performing analytics and reporting and query data quickly.  ... 
doi:10.3233/shti220086 pmid:35673025 fatcat:jvcgniuqsjb5biqcn55pggeyxq

Analytics of Application Resource Utilization within the Virtual Machine

2016 International Journal of Science and Research (IJSR)  
Cloud environments are becoming increasingly prominent and are hosted in large data enters.  ...  These cloud environments support large number of applications which consume resources in a varied sizes and are supported on these VM environment.  ...  The experiment has been conducted on a 72 nodes Beowulf Cluster and the results show that SCMS/RMS is very fast and highly scalable.  ... 
doi:10.21275/v5i4.nov162978 fatcat:lndt22hpujbodmgfkrvds3izxy

Performance Assay of Big IoT Data Analytics Framework

2019 International journal of recent technology and engineering  
Experiments focus on evaluating the performance of three Distributed Stream Analytics Here Analytics frameworks, namely Apache Spark, Splunk and Apache Storm are being evaluated over large steam IoT data  ...  management & analytics of this BIG IOT STREAM data.  ...  One of the main challenges in big data stream analysis is the scalability issue. Big data streams are experiencing exponential growth in a much faster way than computing resources.  ... 
doi:10.35940/ijrte.d7383.118419 fatcat:r65sqxqy6bedpmsvt4sfdqg2ii

Taming Hybrid-Cloud Fast and Scalable Graph Analytics at Twitter [article]

Chunxu Tang, Yao Li, Zhenxiao Luo, Mainak Ghosh, Huijun Wu, Lu Zhang, Anneliese Lu, Ruchin Kabra, Nikhil Kantibhai Navadiya, Prachi Mishra, Prateek Mukhedkar, Vrushali Channapattan
2022 arXiv   pre-print
To bring fast and scalable graph analytics capability into production, we investigate the challenges we are facing in large-scale graph analytics at Twitter and propose a unified graph analytics platform  ...  We have witnessed a boosted demand for graph analytics at Twitter in recent years, and graph analytics has become one of the key parts of Twitter's large-scale data analytics and machine learning for driving  ...  ACKNOWLEDGMENT We would like to express our gratitude to everyone who has served in the Twitter Graph Analytics Working Group for their profound insight and dedicated effort.  ... 
arXiv:2204.11338v1 fatcat:hf3kd4dqbfbopaha2fvo4opd3y

A Comprehensive Review of Tools & Techniques for Big Data Analytics

Amita Dhankhar, Maharshi Dayanand University, Rohtak-124001, India
2019 International Journal of Emerging Trends in Engineering Research  
As such data is growing rapidly, there is a need for advanced analytic techniques that operates on such data and extracts effective information, unknown patterns, and relationships that help in making  ...  Big Data Analytics provides such valuable insight. In this paper, we start with a definition of Big Data.  ...  Flexible . 64KB limit on row size . Distributed . 1MB limit on querying . Scalable BigT able Google Column . No limit for row length . The secondary index is not .  ... 
doi:10.30534/ijeter/2019/257112019 fatcat:ntnjikgimfbe7b6u6vln3cxjvq

Introduction to Big Data: Scalable Representation and Analytics for Data Science Minitrack

Stephen Kaisler, Frank Armour, Alberto Espinosa
2013 2013 46th Hawaii International Conference on System Sciences  
Velocity is concerned with not only how fast we accumulate data, but also how fast some of the data that we already have is changing.  ...  Big data is an emerging phenomenon characterized by the three Vs: volume, velocity, and variety. The volume of data has increased from terabytes to petabytes and is encroaching on exabytes.  ... 
doi:10.1109/hicss.2013.292 dblp:conf/hicss/KaislerAE13 fatcat:xdak3suq7faf5pnqqxyv2tawoe

Power of Big Data System for Storing and Processing Huge Data

S. Natarajan, S. Rajarajesware, Suresh Ram R
2019 International Journal of Scientific Research in Science and Technology  
BDAF includes components such as Big Data Infrastructure, Big Data Analytics, Data structures & models, Big Data Lifecycle Management and Big Data Security.  ...  In this paper we try to give an overview of Big Data Analytics system for storing and processing huge volume of various types of data.  ...  It is fast as it provides direct access to the DataBase memory and one can do manipulations quickly.  ... 
doi:10.32628/ijsrst196422 fatcat:golivpxwpfb5licqfnpstrz67q

FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing

Ramneek, Seung-Jun Cha, Sangheon Pack, Seung Hyub Jeon, Yeon Jeong Jeong, Jin Mee Kim, Sungin Jung
2020 IEEE Access  
As a result, a lot of research has been focusing on efficient analytics models such as data stream mining and concentric computing models [14] .  ...  (eBPF) [38] , and traffic classifier can be considered [39] . 6) Scalable File Systems Fast and scalable storage at the edge is one of the key enablers for satisfying the latency requirement of I/  ... 
doi:10.1109/access.2020.3007747 fatcat:2udiua57pfgklivxt26rn6jhlm

Detailed author index

2015 2015 31st IEEE International Conference on Data Engineering Workshops  
[Search] ABCDEFGHI JKLMNOPQRSTUVWXYZ m Dynamic Resource Management in a MapReduce-Style Platform for Fast Data Processing Ziegler, Tobias 14 m Spotgres -Parallel Data Analytics on Spot Instances  ...  Madsen, Kasper Grud Skat 10 m Dynamic Resource Management in a MapReduce-Style Platform for Fast Data Processing Manolescu, Ioana 71 m Efficient OLAP Operations for RDF Analytics Meng, Rui 216 m On Bottleneck-Aware  ... 
doi:10.1109/icdew.2015.7129529 fatcat:4vkbzbkin5fvhmiibrbqxegjaq

Implementing a Volunteer Notification System into a Scalable, Analytical Realtime Data Processing Environment [chapter]

Jesko Elsner, Tomas Sivicki, Philipp Meisen, Tobias Meisen, Sabina Jeschke
2016 Automation, Communication and Cybernetics in Science and Engineering 2015/2016  
This paper will focus on a basic concept for implementing a VNS approach into a scalable, fault-tolerant environment that uses state-of-the-art analytical tools to process information streams in real-time  ...  This work concentrates on leveraging open source Big Data technologies with the aim to deliver a robust, secure and highly available enterprise-class Big Data platform.  ...  ACKNOWLEDGMENT This paper is based on work done in the INTERREG IVa project EMuRgency (  ... 
doi:10.1007/978-3-319-42620-4_64 fatcat:pd2nu6qe5ffb3m76hoctaimfse

BigDataGrapes D4.1 - Methods and Tools for Scalable Distributed Processing

Nicola Tonellotto, Franco Maria Nardini, Raffaele Perego, Vinicius Monteiro de Lira, Ida Mele, Matteo Catena, Cristina Muntean
2018 Zenodo  
Specifically, the two demonstrators perform scalable operations on geospatial raster data using the Spark-based GeoTrellis geographic data processing engine provided by the BDG platform.  ...  Besides the customization of some existing components, the BigDataGrapes software stack extends the BDE to better support efficient processing and distributed predictive analytics of geospatial raster  ...  Specifically, the two demonstrators perform scalable operations on geospatial raster data using the Spark-based GeoTrellis geographic data processing engine provided by the BDG platform.  ... 
doi:10.5281/zenodo.1575551 fatcat:ed7smls3wfhvbowdi3vkp67fhm

Secondary use of routine data in hospitals: description of a scalable analytical platform based on a business intelligence system

Jan A Roth, Nicole Goebel, Thomas Sakoparnig, Simon Neubauer, Eleonore Kuenzel-Pawlik, Martin Gerber, Andreas F Widmer, Christian Abshagen, Rakesh Padiyath, Balthasar L Hug, Christian Abshagen, Geoffrey Fucile (+12 others)
2018 JAMIA Open  
This platform involves an in-memory database management system for data modeling and analytics and a high-performance cluster for more computing-intensive analytical tasks.  ...  We describe a scalable platform for research-oriented analyses of routine data in hospitals, which evolved from a state-of-the-art business intelligence architecture for enterprise resource planning.  ...  These procedures do not require any data storage and are therefore fast and scalable. At this level, the models are still 1:1 representations and contain basic key figures.  ... 
doi:10.1093/jamiaopen/ooy039 pmid:31984330 pmcid:PMC6952002 fatcat:xqranrcx5nh3jgtkob2hflox4i

Big Challenges? Big Data …

Sahil R., Aarati Mahajan
2015 International Journal of Computer Applications  
This document provides insights on the challenges of managing such a huge Datapopularly known as Big Data, the solutions offered by Big Data management tools/ techniques and the opportunities it has created  ...  In today"s world, every tiny gadget is a potential data source, adding to the huge data bank.  ...  TRADITIONAL DATA ANALTICS V/S BIG DATA ANALYTICS In Traditional Analytics, the analysis used to be done on the known data topography which was well understood.  ... 
doi:10.5120/ijca2015907452 fatcat:dgmzyowjyncvjjkepcdc4i4yey
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