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Big data machine learning and graph analytics: Current state and future challenges
2014
2014 IEEE International Conference on Big Data (Big Data)
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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]
2018
Lecture Notes in Computer Science
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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
2019
IEEE Access
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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
2022
IEEE Access
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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
2015
Proceedings of the VLDB Endowment
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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
2017
International Journal of Advanced Research in Computer Science and Software Engineering
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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]
2018
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2015
arXiv
pre-print
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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
2017
Proceedings of the VLDB Endowment
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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
2020
International Journal of Engineering Research and
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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]
2017
Lecture Notes in Computer Science
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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
2018
Iran Journal of Computer Science
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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
2016
International Journal of Advanced Computer Science and Applications
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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
2019
Information
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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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