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Big data machine learning and graph analytics: Current state and future challenges

H. Howie Huang, Hang Liu
2014 2014 IEEE International Conference on Big Data (Big Data)  
Big data machine learning and graph analytics have been widely used in industry, academia and government.  ...  In this paper, we present some current projects and propose that next-generation computing systems for big data machine learning and graph analytics need innovative designs in both hardware and software  ...  Two of most important big data applications are machine learning and graph analytics.  ... 
doi:10.1109/bigdata.2014.7004471 dblp:conf/bigdataconf/HuangL14 fatcat:w3or7u2hy5d3znq3friiquudkm

Graph BI & Analytics: Current State and Future Challenges [chapter]

Amine Ghrab, Oscar Romero, Salim Jouili, Sabri Skhiri
2018 Lecture Notes in Computer Science  
This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs.  ...  Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.  ...  The remainder of this paper presents the current state and the open challenges of graph BI & analytics, with a focus on graph warehousing.  ... 
doi:10.1007/978-3-319-98539-8_1 fatcat:56fgfclobveifcbkwvbyejt5pi

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

Shaker El-Sappagh, Farman Ali, Samir El-Masri, Kyehyun Kim, Amjad Ali, Kyung-Sup Kwak
2019 IEEE Access  
We consider dimensions such as clinical decision support systems, EHRs, cloud computing, semantic interoperability, wireless body area networks, and big data analytics.  ...  In this survey, we discuss current challenges in MH, along with research gaps, opportunities, and trends.  ...  The ability to predict the long-term future of the patient based on some machine-learning and data-mining algorithms is a challenge.  ... 
doi:10.1109/access.2018.2881001 fatcat:ha4lscdyxfbi7jed5w2blan5yu

An Updated Survey on the Convergence of Distributed Ledger Technology and Artificial Intelligence: Current State, Major Challenges and Future Direction

Jagger S. Bellagarda, Adnan M. Abu-Mahfouz
2022 IEEE Access  
Furthermore, we identify research gaps and discuss open research challenges in developing future directions.  ...  INDEX TERMS Artificial intelligence, distributed ledger technology, blockchain technology, machine learning.  ...  The ultimate goal will be to identify the current state of research, major challenges and future research directions regarding the convergence of DLT and AI.  ... 
doi:10.1109/access.2022.3173297 fatcat:eblpxwzvzng5ddcgn4cilkrur4

Real time analytics

Arun Kejariwal, Sanjeev Kulkarni, Karthik Ramasamy
2015 Proceedings of the VLDB Endowment  
We shall walk through how the field has evolved over the last decade and then discuss the current challenges -the impact of the other three Vs, viz., Volume, Variety and Veracity, on Big Data streaming  ...  Velocity is one of the 4 Vs commonly used to characterize Big Data [5] .  ...  In light of the dynamic nature of streaming data, a field of incremental machine learning has emerged to cater to Big Data streaming analytics.  ... 
doi:10.14778/2824032.2824132 fatcat:srkqipurr5hfvka5jv2mrutnuu

Big Data Analytics: A Perspective View

Suman Pandey
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
Big data analytics challenges the situation of the present infrastructure of data storage management and also statistical data estimation.  ...  This paper studies the content, scope, methods, advantages and challenges of big data and also discusses privacy issue concern on it.  ...  It uses techniques from statistics and machine learning. The Big Data mining is a Challenging issue.  ... 
doi:10.23956/ijarcsse/sv7i5/0237 fatcat:mq75vo3n4rbihnc43mtpktzrru

Industrial Big Data Analytics: Challenges, Methodologies, and Applications [article]

JunPing Wang, WenSheng Zhang, YouKang Shi, ShiHui Duan, Jin Liu
2018 arXiv   pre-print
These challenges for industrial big data analytics is real-time analysis and decision-making from massive heterogeneous data sources in manufacturing space.  ...  For each phase, we introduce to current research in industries and academia, and discusses challenges and potential solutions.  ...  ACKNOWLEDGMENT The authors also would like to thank anonymous editor and reviewers who gave valuable suggestion that has helped to improve the quality of the manuscript.  ... 
arXiv:1807.01016v2 fatcat:wyvz2pxasjh3pm6t7ozow3hlki

Distributed data analytics [article]

Richard Mortier, Hamed Haddadi, Sandra Servia, Liang Wang
2022 arXiv   pre-print
Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are a key component of online service providers.  ...  Traditionally, behavioural analytics relies on collecting vast amounts of data in centralised cloud infrastructure before using it to train machine learning models that allow user behaviour and preferences  ...  Distributed analytics Distributed machine learning (DML) arose as a solution to effectively use large computer clusters and highly parallel computational architectures to speed up the training of big models  ... 
arXiv:2203.14088v1 fatcat:injpdnbssnberlumn6kuudlgfm

Big Data Analytics in Bioinformatics: A Machine Learning Perspective [article]

Hirak Kashyap, Hasin Afzal Ahmed, Nazrul Hoque, Swarup Roy, Dhruba Kumar Bhattacharyya
2015 arXiv   pre-print
This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.  ...  Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel.  ...  ACKNOWLEDGMENTS The authors would like to thank the Ministry of HRD, Govt. of India for funding as a Centre of Excellence with thrust area in Machine Learning Research and Big Data Analytics for the period  ... 
arXiv:1506.05101v1 fatcat:oix7d5hecbfgthzhepznwyi6fm

New trends on exploratory methods for data analytics

Davide Mottin, Matteo Lissandrini, Yannis Velegrakis, Themis Palpanas
2017 Proceedings of the VLDB Endowment  
We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.  ...  Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes cumbersome.  ...  TARGET AUDIENCE This tutorial is intended for researchers and practitioners interested in big data analytics, graph analytics, and data exploration methods.  ... 
doi:10.14778/3137765.3137824 fatcat:unfsqox5jjb2lhd7eur764uy3m

Predictive Analytics for Disaster Management

Anuja Patil, Kaustubh Magdum, Atharva Phadke, Fr. C. Rodrigues Institute of Technology, Vashi, Maharashtra, India
2020 International Journal of Engineering Research and  
Large number of supervised and unsupervised approaches can be used to identify at risk areas and improve predictions of future events.  ...  Predictive analytics helps analyze past events to identify and extract patterns and populations vulnerable to natural calamities.  ...  Predictive analytics uses many techniques from datamining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future.  ... 
doi:10.17577/ijertv9is020415 fatcat:q53x6n7pazdmvpiyrvj6yurkti

Data Multiverse: The Uncertainty Challenge of Future Big Data Analytics [chapter]

Radu Tudoran, Bogdan Nicolae, Götz Brasche
2017 Lecture Notes in Computer Science  
With the explosion of data sizes, extracting valuable insight out of big data becomes increasingly difficult.  ...  This vision paper focuses on one such challenge, which we refer to as the analytics uncertainty: with so much  ...  Machine Learning: A key class of applications that are based on big data analytics is machine learning.  ... 
doi:10.1007/978-3-319-53640-8_2 fatcat:qgn4pzqul5d4znbd2aq5hgpkpi

Construing the big data based on taxonomy, analytics and approaches

Ajeet Ram Pathak, Manjusha Pandey, Siddharth Rautaray
2018 Iran Journal of Computer Science  
Big data have become an important asset due to its immense power hidden in analytics.  ...  Every organization is inundated with colossal amount of data generated with high speed, requiring high-performance resources for storage and processing, special skills and technologies to get value out  ...  Current Trends and Future Directions Ongoing trends in big data analytics, open challenges as future direction 5.  ... 
doi:10.1007/s42044-018-0024-3 fatcat:teiovluolngepjyebzz2wnwjxu

A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

D. P., Kauser Ahmed
2016 International Journal of Advanced Computer Science and Applications  
The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it.  ...  Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development.  ...  In addition to map reduce operations, it supports SQL queries, streaming data, machine learning, and graph data processing.  ... 
doi:10.14569/ijacsa.2016.070267 fatcat:6g2xv2q4ijcvpgikxjzomgjc5a

Big Data Analytics Correlation Taxonomy

Husamaldin, Saeed
2019 Information  
evidence of a real-world link of big data analytics methods and its associated techniques.  ...  Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques.  ...  Stage Three: Predictive analytics stage is an important stage because it helps to designate the future outcomes, by using statistical and machine-learning techniques.  ... 
doi:10.3390/info11010017 fatcat:g6g7ji4arzba7ijfvcbh4jokm4
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