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Electronics
Special Issues
Real-Time Data Management and Analytics
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Real-Time Data Management and Analytics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 16352

Special Issue Editors

Dr. M. Asif Naeem

E-Mail Website
Guest Editor
Department of Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
Interests: data science; stream analytics; big data management; real-time data warehousing; continuous queries
Dr. Farhaan Mirza

E-Mail Website
Guest Editor
Department of Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
Interests: information management; social data management; health information management; Internet of Things

Special Issue Information

In today's competitive and highly dynamic environment, managing and analyzing data to understand business performance and to predict outcomes and trends has become critical. The traditional approach to reporting is no longer adequate; users now demand easy-to-use platforms and applications capable of analyzing real-time data to provide insight and actionable information at the right time. The end goal is to support better and timelier decision making, enabled by the availability of up-to-date, high quality information. The aims of this Special Issue are to provide a venue to publish the latest research results, new technology developments, and new applications in the areas of real-time data processing, data warehousing, data mining, and business intelligence.

Dr. M. Asif Naeem
Dr. Farhaan Mirza
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • Data Management in Clouds
  • Data Management in Enterprise Applications
  • Data Quality and Cleansing
  • Business Intelligence over Streaming Data
  • Data stream Processing
  • Performance and Scalability
  • Real-time Decision Support
  • Tuning of the Real-time Data Warehouse
  • Data Warehouse Evolution
  • ETL for the Real-time Data Warehouse
  • Data Mining and Data Analysis in Real-time
  • Real-time Business Activity Monitoring
  • Real-time OLAP
  • NLP for Data Management
  • Novel Data Models
  • Data Visualization
  • IoT Data Management
  • Mining Text and Semi-structured Data
  • Mining Retail Data
  • Mining Social Data
  • Mining Stream Data

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Published Papers (5 papers)

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Research

12 pages, 5789 KiB  
Article
Making Sense of Complex Sensor Data Streams
by Rongrong Liu and Birgitta Dresp-Langley
Electronics 2021, 10(12), 1391; https://doi.org/10.3390/electronics10121391 - 10 Jun 2021
Cited by 4 | Viewed by 2375
Abstract
This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specificity of [...] Read more.
This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image-guided task performed in a real-world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task-specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is necessary to decipher the meaning of intra- and inter-individual variance in the sensor data on the basis of appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio-temporal grip force profiles allows detecting expertise-specific differences between individual users. It is concluded that both analytic strategies are complementary and enable drawing meaning from thousands of biosensor data reflecting human performance measures while fully taking into account their considerable inter- and intra-individual variability. Full article
(This article belongs to the Special Issue Real-Time Data Management and Analytics)
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21 pages, 2913 KiB  
Article
Optimal Planning of Real-Time Bus Information System for User-Switching Behavior
by Zoonky Lee, Sewoong Hwang and Jonghyuk Kim
Electronics 2020, 9(11), 1903; https://doi.org/10.3390/electronics9111903 - 13 Nov 2020
Cited by 1 | Viewed by 2207
Abstract
Seoul Metropolitan City’s buses cater to more than 50% of the average daily public transportation use, and they are the most important transportation mode in Korea, together with the subway. Since 2004, all public transportation records of passengers have been stored in Seoul, [...] Read more.
Seoul Metropolitan City’s buses cater to more than 50% of the average daily public transportation use, and they are the most important transportation mode in Korea, together with the subway. Since 2004, all public transportation records of passengers have been stored in Seoul, using smart transportation cards. This study explores the environmental and psychological factors in implementing a smart transportation system. We analyze the switching behavior of traffic users according to traffic congestion time and number of transfers based on public transportation data and show that bus-use behavior differs according to the traffic information of users and the degree of traffic congestion. Information-based switching behavior of people living near bus stops induces people to change routes during traffic congestion. However, in non-congested situations, the original routes are used. These results can guide the formulation of policy measures on bus routes. We made it possible to continuously change the routes for certain buses, which were temporarily implemented due to traffic congestion. Moreover, we added a service that posts the estimated arrival time to major stops while reflecting real-time traffic conditions in addition to the bus location and arrival time information through the global positioning system. Full article
(This article belongs to the Special Issue Real-Time Data Management and Analytics)
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20 pages, 3757 KiB  
Article
Reply Using Past Replies—A Deep Learning-Based E-Mail Client
by Yiwei Feng, M. Asif Naeem, Farhaan Mirza and Ali Tahir
Electronics 2020, 9(9), 1353; https://doi.org/10.3390/electronics9091353 - 20 Aug 2020
Cited by 1 | Viewed by 4545
Abstract
Email is the most common and effective source of communication for most enterprises and individuals. In the corporate sector the volume of email received daily is significant while timely reply of each email is important. This generates a huge amount of work for [...] Read more.
Email is the most common and effective source of communication for most enterprises and individuals. In the corporate sector the volume of email received daily is significant while timely reply of each email is important. This generates a huge amount of work for the organisation, in particular for the staff located in the help-desk role. In this paper we present a novel Smart E-mail Management System (SEMS) for handling the issue of E-mail overload. The Term Frequency-Inverse Document Frequency (TF-IDF) model was used for designing a Smart Email Client in previous research. Since TF-IDF does not consider semantics between words, the replies suggested by the model are not very accurate. In this paper we apply Document to Vector (Doc2Vec) and introduce a novel Gated Recurrent Unit Sentence to Vector (GRU-Sent2Vec), which is a hybrid model by combining GRU and Sent2Vec. Both models are more intelligent as compared to TF-IDF. We compare our results from both models with TF-IDF. The Doc2Vec model performs the best on predicting a response for a similar new incoming Email. In our case, since the dataset is too small to require a deep learning algorithm model, the GRU-Sent2Vec hybrid model cannot produce ideal results, whereas in our understanding it is a robust method for long-text prediction. Full article
(This article belongs to the Special Issue Real-Time Data Management and Analytics)
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12 pages, 825 KiB  
Article
Parallelisation of a Cache-Based Stream-Relation Join for a Near-Real-Time Data Warehouse
by M. Asif Naeem, Habib Ullah Khan, Saad Aslam and Noreen Jamil
Electronics 2020, 9(8), 1299; https://doi.org/10.3390/electronics9081299 - 12 Aug 2020
Cited by 3 | Viewed by 2519
Abstract
Near real-time data warehousing is an important area of research, as business organisations want to analyse their businesses sales with minimal latency. Therefore, sales data generated by data sources need to reflect immediately in the data warehouse. This requires near-real-time transformation of the [...] Read more.
Near real-time data warehousing is an important area of research, as business organisations want to analyse their businesses sales with minimal latency. Therefore, sales data generated by data sources need to reflect immediately in the data warehouse. This requires near-real-time transformation of the stream of sales data with a disk-based relation called master data in the staging area. For this purpose, a stream-relation join is required. The main problem in stream-relation joins is the different nature of inputs; stream data is fast and bursty, whereas the disk-based relation is slow due to high disk I/O cost. To resolve this problem, a famous algorithm CACHEJOIN (cache join) was published in the literature. The algorithm has two phases, the disk-probing phase and the stream-probing phase. These two phases execute sequentially; that means stream tuples wait unnecessarily due to the sequential execution of both phases. This limits the algorithm to exploiting CPU resources optimally. In this paper, we address this issue by presenting a robust algorithm called PCSRJ (parallelised cache-based stream relation join). The new algorithm enables the execution of both disk-probing and stream-probing phases of CACHEJOIN in parallel. The algorithm distributes the disk-based relation on two separate nodes and enables parallel execution of CACHEJOIN on each node. The algorithm also implements a strategy of splitting the stream data on each node depending on the relevant part of the relation. We developed a cost model for PCSRJ and validated it empirically. We compared the service rates of both algorithms using a synthetic dataset. Our experiments showed that PCSRJ significantly outperforms CACHEJOIN. Full article
(This article belongs to the Special Issue Real-Time Data Management and Analytics)
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18 pages, 1555 KiB  
Article
Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities
by Iván García-Magariño, Moustafa M. Nasralla and Shah Nazir
Electronics 2020, 9(7), 1101; https://doi.org/10.3390/electronics9071101 - 6 Jul 2020
Cited by 11 | Viewed by 3272
Abstract
Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all [...] Read more.
Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%). Full article
(This article belongs to the Special Issue Real-Time Data Management and Analytics)
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