Modeling and Management Big Data in Databases—A Systematic Literature Review

Martinez-Mosquera, Diana; Navarrete, Rosa; Luján‐Mora, Sergio

doi:10.3390/su12020634

Cited by 23 publications

(15 citation statements)

References 48 publications

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“…Expectedly, the shortlisted articles mainly focused on big data technologies (59), followed by others (29), review (23), conference (18), and case study (10). Similar analyses were conducted by Martinez-Mosquera et al [37]; however, none of the previously published articles explored big data applications in the context of smart real estate or disaster and risk management, which is the novelty of the current study. The current study further provides an integrated framework for the two fields.…”

Section: Review Resultsmentioning

confidence: 79%

“…Features and applications of some NOSQL databases, as well as their categories, features, and applications, are discussed in Table 8. A further four categories as defined by Martinez-Mosquera et al [37] are used to classify the databases which are column-oriented, document-oriented, graph, and key value. Apache Cassandra is a NoSQL database management system, which can handle big data over several parallel servers.…”

Section: Data Storagementioning

confidence: 99%

See 1 more Smart Citation

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

107

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Review Resultsmentioning

confidence: 79%

Section: Data Storagementioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

107

View full text Add to dashboard Cite

show abstract

“…Mongo is an open-source schemaless, Not Only SQL (NoSQL), document-based database that stores and retrieves documents as extensive Markup language (XML), JavaScript Object Notification (JSON), binary JSON (BSON) [36]. New techniques in the database context have evolved towards NoSQL [37]. In this research, Mongo is selected as it can support the variety, volume, and velocity features of Big Data.…”

Section: Data Storagementioning

confidence: 99%

Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

et al. 2021

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Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

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“…Based on the suggestions of some papers [20]- [23] we searched for SLRs and similar publications related to web accessibility of educational websites to verify if the SLR proposed in this paper can fill any gaps. For the search we use one search strings for Scopus and one search string for the Web of Science.…”

Section: ) Identification Of Need For a Systematic Literature Reviewmentioning

confidence: 99%

Empirical Studies on Web Accessibility of Educational Websites: A Systematic Literature Review

2020

Self Cite

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Web accessibility means that people with some type of disability can make use of the Web in the same conditions as the rest of the people. When we talk about web accessibility, we refer to a web design and development that allows these people to perceive, understand, navigate and interact with the Web. Web accessibility also benefits other people, including elderly people whose abilities have declined as a result of age. The Web is an essential resource in human activity: education, employment, government, commerce, health, entertainment and many others benefit of the power of the Web. The aim of this systematic literature review is to analyze the empirical methods of evaluating accessibility to educational websites, disabilities and their errors described in a total of 25 selected studies. The results show that in 20 of the 25 papers, web accessibility was evaluated with automatic tools, in 2 papers it was evaluated with real users and in the other 3 papers with automatic tools, real users and experts. There is also evidence that all the educational websites analyzed in the papers need to correct errors. In conclusion, educational websites do not meet any version of the Web Content Accessibility Guidelines (WCAG) and their conformance levels. According to the results, the empirical evaluation methods used for web accessibility could be improved by adopting automatic evaluation tools for website construction and manual mechanisms with web accessibility experts. The challenge for educational institutions is to carry out web accessibility projects to comply with WCAG and other web accessibility standards and current laws of educational inclusion. INDEX TERMS Assessment, education, systematic literature review, websites, web page design, Web content accessibility guidelines (WCAG).

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Modeling and Management Big Data in Databases—A Systematic Literature Review

Cited by 23 publications

References 48 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

Empirical Studies on Web Accessibility of Educational Websites: A Systematic Literature Review

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