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

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

Next generation analytics for open pervasive display networks

Public displays and digital signs are becoming increasingly widely deployed as many spaces move towards becoming highly interactive and augmented environments. Market trends suggest further significant increases in the number of digital signs and both researchers and commercial entities are working on designing and developing novel uses for this technology. Given the level of investment, it is increasingly important to be able to understand the effectiveness of public displays. Current state-of-the-art analytics technology is limited in the extent to which it addresses the challenges that arise from display deployments becoming open (increasing numbers of stakeholders), networked (viewer engagement across devices and locations) and pervasive (high density of displays and sensing technology leading to potential privacy threats for viewers). In this thesis, we provide the first exploration into achieving next generation display analytics in the context of open pervasive display networks. In particular, we investigated three areas of challenge: analytics data capture, reporting and automated use of analytics data. Drawing on the increasing number of stakeholders, we conducted an extensive review of related work to identify data that can be captured by individual stakeholders of a display network, and highlighted the opportunities for gaining insights by combining datasets owned by different stakeholders. Additionally, we identified the importance of viewer-centric analytics that use traditional display-oriented analytics data combined with viewer mobility patterns to produce entirely new sets of analytics reports. We explored a range of approaches to generating viewer-centric analytics including the use of mobility models as a way to create 'synthetic analytics' - an approach that provides highly detailed analytics whilst preserving viewer privacy. We created a collection of novel viewer-centric analytics reports providing insights into how viewers experience a large network of pervasive displays including reports regarding the effectiveness of displays, the visibility of content across the display network, and the visibility of content to viewers. We further identified additional reports specific to those display networks that support the delivery of personalised content to viewers. Additionally, we highlighted the similarities between digital signage and Web analytics and introduced novel forms of digital signage analytics reports created by leveraging existing Web analytics engines. Whilst the majority of analytics systems focus solely on the capture and reporting of analytics insights, we additionally explored the automated use of analytics data. One of the challenges in open pervasive display networks is accommodating potentially competing content scheduling constraints and requirements that originate from the large number of stakeholders - in addition to contextual changes that may originate from analytics insights. To address these challenges, we designed and developed the first lottery scheduling approach for digital signage providing a means to accommodate potentially conflicting scheduling constraints, and supporting context- and event-based scheduling based on analytics data fed back into the digital sign. In order to evaluate the set of systems and approaches presented in this thesis, we conducted large-scale, long-term trials allowing us to show both the technical feasibility of the systems developed and provide insights into the accuracy and performance of different analytics capture technologies. Our work provides a set of tools and techniques for next generation digital signage analytics and lays the foundation for more general people-centric analytics that go beyond the domain of digital signs and enable unique analytical insights and understanding into how users interact across the physical and digital world