Machine learning on big data: Opportunities and challenges

Zhou, Lina; Pan, Shimei; Wang, Jianwu; Vasilakos, Athanasios V.

doi:10.1016/j.neucom.2017.01.026

Cited by 736 publications

(362 citation statements)

References 78 publications

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“…Neural classifiers such as fuzzy adaptive resonance associative maps are scalable for large volumes of data (Benites & Sapozhnikova, 2017). Unsupervised learning provides so many research opportunities in workflow management and task scheduling, particularly in the field of big data (Zhoua, Pana, Wanga, Athanasios, &Vasilakos, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

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Section: Literature Reviewmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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“…Machine learning is the effective tool for the description and prediction to nonliear behaviors, not only in macroacale phenomena, but in the submillimeter as well [11,12]. In this study, the Neural Network Modeling (NNM) for hydrogel particle size variation was performed.…”

Section: Machine Learning and Modeling Verificationmentioning

confidence: 99%

Submillimeter-Scale Analysis and Machine Learning For the Generation of Alginate Hydrogel Beads

he¹,

Zhang²

2018

Proceedings of the 2018 International Workshop on Bioinformatics, Biochemistry, Biomedical Sciences (BBBS 2018)

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Abstract. The fabrication of microbeads at submillimter-scale has been focused on as the scientific research topic since they have been widely applied in various kinds of industries, such as pharmacology, cosmetics as well as food industry. The development of applied mathematics, material technology and information technology accelerated the submillimeter-scale analysis on the generation behaviors for the hydrogel microbeads and afterwards the manipulated its fabrication. In this work, the experiments were designed to manipulate hydrogel microbeads. The statistical analysis was carried out, the nonlinear modeling on the variation of microbeads was undertaken and its improved version was created. Submillimeter analysis-based machine learning technology was finally introduced to test and verified the neural network modelling stisfactorily.

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“…Finally, ML is widely used for supporting 'big data' tools. The interested reader could refer in [49,50], for a survey on the ML techniques adopted in large scale analytics.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Predictive Analytics in Smart Cities

Kolomvatsos

Anagnostopoulos

2017

Informatics

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Abstract:The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens' lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller (QC) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework.

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Machine learning on big data: Opportunities and challenges

Cited by 736 publications

References 78 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Submillimeter-Scale Analysis and Machine Learning For the Generation of Alginate Hydrogel Beads

Reinforcement Learning for Predictive Analytics in Smart Cities

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