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

Factors for the implementation of the circular economy in Big Data environments in service companies in post pandemic times of COVID-19: The case of Colombia

In emerging economies, Big Data (BD) analytics has become increasingly popular, particularly regarding the opportunities and expected benefits. Such analyzes have identified that the production and consumption of goods and services, while unavoidable, have proven to be unsustainable and inefficient. For this reason, the concept of the circular economy (CE) has emerged strongly as a sustainable approach that contributes to the eco-efficient use of resources. However, to develop a circular economy in DB environments, it is necessary to understand what factors influence the intention to accept its implementation. The main objective of this research was to assess the influence of attitudes, subjective norms, and perceived behavioral norms on the intention to adopt CE in BD-mediated environments. The methodology is quantitative, cross-sectional with a descriptive correlational approach, based on the theory of planned behavior and a Partial Least Squares Structural Equation Model (PLS-SEM). A total of 413 Colombian service SMEs participated in the study. The results show that managers' attitudes, subjective norms, and perceived norms of behavior positively influence the intentions of organizations to implement CB best practices. Furthermore, most organizations have positive intentions toward CE and that these intentions positively influence the adoption of DB; however, the lack of government support and cultural barriers are perceived as the main limitation for its adoption. The research leads to the conclusion that BD helps business and government develop strategies to move toward CE, and that there is a clear positive will and intent toward a more restorative and sustainable corporate strategy.

Using Real-Time Dashboards to Monitor the Impact of Disruptive Events on Real Estate Market. Case of COVID-19 Pandemic in Australia

Real estate markets are complex both in terms of structure and dynamics: they are both influenced by and influence almost all aspects of the economy and are equally vulnerable to the shocks experienced by the broader economy. Therefore, understanding the extent and nature of the impact of large-scale disruptive events such as natural disasters and economic financial downturns on the real estate market is crucial to policy makers and market stakeholders. In addition to anticipating and preparing for long-term effects, it has become imperative for stakeholders to monitor and manage the short-term effects as well due to the emergence of ‘PropTech’ and ‘platform real estate’. In this work, we explore the use of online, real-time dashboards which have been used extensively in the context of urban management, policymaking, citizen engagement and disaster response as an appropriate tool for the purpose of monitoring real estate markets. We describe the process of designing, building, and maintaining an operational dashboard for monitoring the residential real estate market in Australia during the COVID-19 pandemic in 2020. We detail the techniques and methods used in creating the dashboard and critically evaluate their feasibility and usefulness. Finally, we identify the major challenges in the process, such as the spatial and temporal availability and veracity of the real estate market data, and we identify possible avenues for consistent, high-quality data; methodology; and outputs for further research.

Disaster-Resilient Communities on Flood Plains and Their Agricultural Regeneration: A Case Study in Meinong Plain, Taiwan

Taiwan’s near-mountain alluvial plain is a high-risk area with frequent disasters, and residents have become more tolerant of the compound disasters that occur with overall environmental changes associated with the development of urbanization in recent years. This paper presents a case study of a near-mountainous alluvial plain in Southern Taiwan. The Hakka ethnic group is the main community in the study area and also the main research object. This case study illustrates the disaster resilience of the community to natural and artificial disasters. This study adopted two research approaches, namely historical geography and political economic geography, as well as community resilience theory. Research methods including case study, secondary literature analysis, fieldwork, and interviews were used. Through text analysis, it was found that (1) the community’s awareness of disaster avoidance was rooted in the experience of reclamation in the early 17th century; (2) communities have experienced artificial disasters caused by political and economic intervention, which have been transformed into disaster awareness and community resilience; (3) cumulative artificial disasters have a greater impact on communities than unpredictable natural disasters; and (4) the energy of community resilience and agricultural regeneration is based on the duality of disasters.

Internet of Things Data Visualization for Business Intelligence

This study contributes to the research on Internet of Things data visualization for business intelligence processes, an area of growing interest to scholars, by conducting a systematic review of the literature. A total of 237 articles published over the past 11 years were obtained and compared. This made it possible to identify the top contributing and most influential authors, countries, publishers, institutions, papers, and research findings, together with the challenges facing current research. Based on these results, this work provides a thorough insight into the field by proposing four research categories (Technology infrastructure, Case examples, Final-user experience, and Big Data tools), together with the development of these research streams over time and their future research directions.

Big Data in Construction: Current Applications and Future Opportunities

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

Automatic Target Detection from Satellite Imagery Using Machine Learning

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

Disruptive technologies as a solution for disaster risk management: A review

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

The smart grid (SG) is a contemporary electrical network that enhances the network's performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions

This study examines the effects of quality of service, product awareness, and perceptions among customers of Islamic financial institutions (IFIs) on customer loyalty through technology integration using customer satisfaction as a mediator. A well-structured, comprehensive questionnaire was developed and data were collected from 203 respondents who were customers of six IFIs in Pakistan and had at least 2 years of experience in dealing confiorm this is correct with these IFIs. A total of 171 accurate responses were received from the respondents. Ten hypotheses were developed and statistically verified using regression and correlation analytical techniques. The results reveal that the quality of customer services and awareness of IFIs had a direct and positive relationship with customer loyalty, which in turn was mediated by customer satisfaction. Perceptions about IFIs had a direct positive relation with customer satisfaction. However, the relation of perceptions and quality of service with customer loyalty and satisfaction in financial institutions through technology integration was found to be insignificant, even in the presence of customer satisfaction as a mediator.

Effects of COVID-19 on the Australian Economy: Insights into the Mobility and Unemployment Rates in Education and Tourism Sectors

Coronavirus Disease 2019 (COVID-19) has affected global economies due to lockdowns, business closures, and travel and other restrictions. To control the spread of the virus, several countries, including Australia, imposed strict border restrictions and lockdown measures. Accordingly, international borders have been closed, and all incoming international passengers are mandated to a 14-day hotel quarantine. Residents’ movements and businesses have been limited to essential services only. Employees have been directed to work from home while businesses moved to a remote working model. Due to such stringent measures, small and medium businesses such as cafes, restaurants, hotels, childcare centers, and tourism-based institutions incurred heavy losses, pushing a considerable portion of such small businesses to close. The airlines, education, tourism, and hospitality sector were the worst impacted among all. Due to such closures and associated effects of COVID-19, the unemployment rates are assumed to be significantly increased in countries like Australia. However, a study investigating this unemployment and reporting its status does not exist for Australia. Therefore, in this study, we investigated the effects of COVID-19 control measures such as travel restriction and lockdown on Australia’s employment status and labor markets. The data for the local transport network, unemployment rates and impacts on the tourism industry in Australia were extracted from the public data sources to assess the unemployment rates at both national and state-wide levels. Further, we also looked into the rehabilitation measures by the Australian government, such as the Job Keeper and Job Seeker programs in March 2020, that aim to provide support to people who are unable to run their businesses or have lost their jobs due to the pandemic. Overall, we observed that despite the global crisis, the Australian unemployment rate has reduced in the last year.

Big-Data Management: A Driver for Digital Transformation?

The rapid evolution of technology has led to a global increase in data. Due to the large volume of data, a new characterization occurred in order to better describe the new situation, namel. big data. Living in the Era of Information, businesses are flooded with information through data processing. The digital age has pushed businesses towards finding a strategy to transform themselves in order to overtake market changes, successfully compete, and gain a competitive advantage. The aim of current paper is to extensively analyze the existing online literature to find the main (most valuable) components of big-data management according to researchers and the business community. Moreover, analysis was conducted to help readers in understanding how these components can be used from existing businesses during the process of digital transformation.

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.

UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.

Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation

Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships.

A Gabor Filter-Based Protocol for Automated Image-Based Building Detection

Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites needs to be automated to detect the buildings. Additionally, buildings exhibit several different characteristics, and their appearance in these images is unplanned. Moreover, buildings in the metropolitan environment are typically crowded and complicated. Therefore, it is challenging to identify the building and hard to locate them. To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches. A local feature extraction technique was implemented, which was used to calculate the probability density function. The locations in the image were represented as joint probability distributions and were used to estimate their probability distribution function (pdf). The density of building locations in the image was extracted. Kernel density distribution was also used to find the density flow for different metropolitan cities such as Sydney (Australia), Tokyo (Japan), and Mumbai (India), which is useful for distribution intensity and pattern of facility point f interest (POI). The purpose system can detect buildings/rooftops and to test our system, we choose some crops with panchromatic high-resolution satellite images from Australia and our results looks promising with high efficiency and minimal computational time for feature extraction. We were able to detect buildings with shadows and building without shadows in 0.4468 (seconds) and 0.5126 (seconds) respectively.

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

Localization of Sound Sources: A Systematic Review

Sound localization is a vast field of research and advancement which is used in many useful applications to facilitate communication, radars, medical aid, and speech enhancement to but name a few. Many different methods are presented in recent times in this field to gain benefits. Various types of microphone arrays serve the purpose of sensing the incoming sound. This paper presents an overview of the importance of using sound localization in different applications along with the use and limitations of ad-hoc microphones over other microphones. In order to overcome these limitations certain approaches are also presented. Detailed explanation of some of the existing methods that are used for sound localization using microphone arrays in the recent literature is given. Existing methods are studied in a comparative fashion along with the factors that influence the choice of one method over the others. This review is done in order to form a basis for choosing the best fit method for our use.

Sound Localization for Ad-Hoc Microphone Arrays

Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.

Post-Flood Risk Management and Resilience Building Practices: A Case Study

The study was conducted to assess the post 2010 flood risk management and resilience-building practices in District Layyah, Pakistan. Exploratory research was applied to gain knowledge of flood risk management to embed the disaster risk reduction, mitigation, and adaptation strategies at the local government and community level. Around 200 questionnaires were collected from the four devastated areas/union councils. Primary data from the field uncovered flood risk management practices by organizations, local government, and the community. It highlights resilience-building practices undertaken by the community through rehabilitation, community participation, and local indigenous practices. The role of the District Layyah’s local government and organizations to mitigate the 2010 flood and their contribution towards flood resilience in affected communities was investigated, as no comparable studies were carried out in the riverine belt of District Layyah previously. Moreover, the tangible and non-tangible measures to lessen the vulnerability to floods and improve flood risk governance at a local level were identified. This study makes a valuable contribution in strengthening the resilience building of vulnerable communities by recommending few changes in existing practices concerning flood risk at a local level.

Analysis of the State of Learning in University Students with the Use of a Hadoop Framework

Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making to improve learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent.

Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective

The Coronavirus Disease 2019 (COVID-19) is a major virus outbreak of the 21st century. The Australian government and local authorities introduced some drastic strategies and policies to control the outspread of this virus. The policies related to lockdown, quarantine, social distancing, shut down of educational institute, work from home, and international and interstate travel bans significantly affect the lifestyle of citizens and, thus, influence their activity patterns. The transport system is, thus, severely affected due to the COVID-19 related restrictions. This paper analyses how the transport system is impacted because of the policies adopted by the Australian government for the containment of the COVID-19. Three main components of the transport sector are studied. These are air travel, public transport, and freight transport. Various official sources of data such as the official website of the Australian government, Google mobility trends, Apple Mobility trends, and Moovit were consulted along with recently published research articles on COVID-19 and its impacts. The secondary sources of data include databases, web articles, and interviews that were conducted with the stakeholders of transport sectors in Australia to analyse the relationship between COVID-19 prevention measures and the transport system. The results of this study showed reduced demand for transport with the adoption of COVID-19 prevention measures. Declines in revenues in the air, freight, and public transport sectors of the transport industry are also reported. The survey shows that transport sector in Australia is facing a serious financial downfall as the use of public transport has dropped by 80%, a 31.5% drop in revenues earned by International airlines in Australia has been predicted, and a 9.5% reduction in the freight transport by water is expected. The recovery of the transport sector to the pre-pandemic state is only possible with the relaxation of COVID-19 containment policies and financial support by the government.

Smart Digital Marketing Capabilities for Sustainable Property Development: A Case of Malaysia

Digital tools and marketing have been widely adopted in various industries throughout the world. These tools have enabled companies to obtain real-time customer insights and create and communicate value to customers more effectively. This study aims at understanding the principles and practices of sustainable digital marketing in the Malaysian property development industry by investigating the extent to which digital marketing has been adopted, the impediments to its adoption, and the strategies to improve digital capabilities for the local context. Digital marketing theories, practices, and models from other industries are adopted and applied to the local property development industry to lay the foundation for making it smart and sustainable. This paper proposes a marketing technology acceptance model (MTAM) for digital marketing strategy and capability development. The key factors used in the model are ease of use, perceived usefulness, perceived cost, higher return, efficiency, digital service quality, digital information quality, digital system quality, attitude towards use, and actual use. The model and hypothetical relationships of critical factors are tested using structural modeling, reliability, and validity techniques using a sample of 279 Malaysian property development sector representatives. A quantitative approach is adopted, using an online questionnaire tool to investigate the behavior of respondents on the current digital marketing practices and capabilities of Malaysian property development companies. The results show that the sample property development companies are driven by the benefit of easily obtaining real-time customer information for creating and communicating value to customers more effectively through the company brand. Further strategies, such as creating real-time interactions, creating key performance indicators to measure digital marketing, personalization, and encouraging innovation in digital marketing are most preferred by local professionals. An adoption framework is provided based on the reviewed models and results of the current study to help transform the Malaysian property development sector into a smart and sustainable property development sector by facilitating the adoption of digital technologies. The results, based on real-time data and pertinent strategies for improvement of the local property sector, are expected to pave the way for inducing sustainable digital marketing trends, enhancing capabilities, and uplifting the state of the property development sector in developing countries.

Key Factors Influencing Purchase or Rent Decisions in Smart Real Estate Investments: A System Dynamics Approach Using Online Forum Thread Data

The real estate sector is receiving mix responses throughout the world, with some countries like USA receiving lesser and European and Asia Pacific markets receiving more transactions in recent years. Among the concerning factors, post-purchase regrets by the real estate owners or renters are on the rise, which have never been assessed to date through scholarly research. These regrets can further increase in the time of lockdowns and bans on inspections due to Corona Virus Disease 2019 (COVID-19) and social distancing rules enforced by various countries such as Australia. The current study aims at investigating the key post-purchase regret factors of real estate and property owners and renters over the last decade using published literature and online threads. Based on pertinent literature, 118 systematically identified and text-mined articles, and four online threads with 135 responses, the current study develops system dynamics models to assess and predict the increase in consumers’ regrets over the last decade. Further, a user-generated thread with 23 responses involving seven real estate managers and five agents with more than 20 years of experience, 10 buyers with at least three successful rentals or purchases, and a photographer with more than 10 years of experience, is initiated on five online discussion platforms whereby the respondents are involved in a detailed discussion to highlight the regret reasons specific to real estate purchases based on online information. General architecture for text mining (GATE) software has been utilised to mine the text from both types of threads: Published and user generated. Overall, the articles and threads published over the last decade are studied under two periods: P1 (2010–2014) and P2 (2015–2019) to highlight the post-purchase or rent-related regret reasons. The results show that regret levels of the real estate consumers based on published post-purchase data are at an alarmingly high level of 88%, which compared to 2015, has increased by 18%. Among the major cited reasons, complicated buy–sell process, lack or accuracy of information, housing costs, house size, mortgages, agents, inspections, and emotional decision making are key reasons of regret. Overall, a total of 10% and 8% increases have occurred in the regrets related to the buy–sell process and lack of inspections, respectively. On the other hand, regrets related to agents and housing costs have decreased drastically by 40% mainly due to the good return on investments in the growing markets. However, based on the current trend of over reliance on online information and more powers to the agents controlling online information coupled with lack of physical inspections, the situation can change anytime. Similarly, lack of information, housing size, and mortgage-related regrets have also decreased by 7%, 5%, and 2%, respectively, since 2019. The results are expected to encourage policy level changes for addressing the regrets and uplifting the real estate industry and moving towards a smart and sustainable real estate sector. These results and pertinent discussions may help the real estate decision makers to uplift the current state, move towards a smart real estate, and avoid futuristic regrets, especially in the COVID-hit environment where most of the industries are struggling to survive. Careful attention is required to the top regret factors identified in the study by the real estate managers, investors, and agents to pave the way for a more managed real estate and property sector whereby the consumers are more satisfied with the value they receive for their money. This win–win situation will enhance the property business and remove the stigmas of intentional and deliberate withholding of information by managers and agents from the property and real estate sectors that can help boost the business through more purchases and satisfaction of its customers.