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Future Internet
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Big Data Processing and Analytics in the Era of Extreme...
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Big Data Processing and Analytics in the Era of Extreme Connectivity and Automation

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (30 May 2019) | Viewed by 14033

Special Issue Editors

Dr. Simon Fong

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Guest Editor
Department of Computer and Information Science, University of Macau, Room 4023, E11, FST Building, Taipa, Macau 999078, China
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sabah Mohammed

E-Mail Website
Guest Editor
Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: artificial intelligence; ambient intelligence; decision support systems
Special Issues, Collections and Topics in MDPI journals
Prof. Luiz Moutinho

E-Mail Website
Guest Editor
Visiting Professor of Marketing, Suffolk Business School, University of Suffolk, England, UK
Interests: marketing; neuroscience; futurecast
Prof. Dr. Jinan Fiaidhi

E-Mail Website
Guest Editor
Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada
Interests: thick data analytics; web mining; learning analytics; social networking; web services; interoperability; software agility development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big data is a term that has risen to prominence describing data that exceeds the processing capacity of conventional database systems. McKinsey and company announced the big data revolution in 2011 and suggested that the age of relational database management systems and SQL-based data manipulation and access methods was drawing to a close because those technologies could not keep pace with what McKinsey projected was a coming deluge of new and complex data sets, for most organizations in most industries. This projection is getting more challenging with the lack of mechanisms for managing all of the metadata associated with the big data “data pool” — where data sets reside, how they entered the pool, what data engineering flows they are implicated in, what kinds of algorithms and decision-making processes the data sets are suitable (and unsuitable) for, what kinds of governance, regulatory and compliance restrictions are associated with the data sets, and so forth. This technology is tagged today as big data connectivity, which is a growing area of importance as the world is entering the new Industry 4.0 with the increasing reliance on extreme connectivity and automation. The heterogeneity of the big data landscape is characterized not only by the distributed nature and connectivity of its most prominent technologies, but also by their underlying architectures. There is great need for mechanisms for expressing, managing, versioning, executing and monitoring data engineering work streams and models and algorithms consuming data from the data pool and from data warehouses as well as mechanisms for orchestrating data flows that cross the logical boundary between the data pool. This Special Issue is an attempt to study big data and analytics in the era of extreme connectivity and automation that relies on technologies like IoT, big data connectivity, blockchain, named data networking and artificial intelligence.

Prof. Simon James Fong
Prof. Sabah Mohammed
Prof. Luiz Moutinho
Prof. Jinan Fiaidhi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data Automation
  • Extreme Automation and Connectivity
  • Blockchain
  • Internet of Things
  • Connected Data Analytics
  • Named Data Networking

Published Papers (3 papers)

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Research

17 pages, 2321 KiB  
Article
THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
by Jiwei Qin, Liangli Ma and Jinghua Niu
Future Internet 2019, 11(1), 10; https://doi.org/10.3390/fi11010010 - 03 Jan 2019
Cited by 13 | Viewed by 3895
Abstract
The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most [...] Read more.
The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the colocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes. Full article
(This article belongs to the Special Issue Big Data Processing and Analytics in the Era of Extreme Connectivity and Automation)
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12 pages, 2096 KiB  
Article
A Method for Filtering Pages by Similarity Degree based on Dynamic Programming
by Ziyun Deng and Tingqin He
Future Internet 2018, 10(12), 124; https://doi.org/10.3390/fi10120124 - 13 Dec 2018
Viewed by 2925
Abstract
To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of [...] Read more.
To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd.com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS). Full article
(This article belongs to the Special Issue Big Data Processing and Analytics in the Era of Extreme Connectivity and Automation)
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11 pages, 2307 KiB  
Article
A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification
by Yonghua Zhu, Xun Gao, Weilin Zhang, Shenkai Liu and Yuanyuan Zhang
Future Internet 2018, 10(12), 116; https://doi.org/10.3390/fi10120116 - 24 Nov 2018
Cited by 33 | Viewed by 6464
Abstract
The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and [...] Read more.
The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods. Full article
(This article belongs to the Special Issue Big Data Processing and Analytics in the Era of Extreme Connectivity and Automation)
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