Big Data for Smart Infrastructure Design: Opportunities and Challenges

Arfat, Yasir; Usman, Sardar; Mehmood, Rashid; Katib, Iyad

doi:10.1007/978-3-030-13705-2_20

Cited by 18 publications

(16 citation statements)

References 68 publications

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“…The core ingredients of these digital societies, or digital platforms that enable these societies include a range of emerging technologies and their convergence. The technologies include big data [ 27 , 28 , 29 , 30 ], high-performance computing (HPC) [ 31 , 32 , 33 , 34 ], artificial intelligence [ 35 , 36 , 37 , 38 ], cloud, fog, and edge computing [ 39 , 40 , 41 ], social sensing [ 42 , 43 , 44 , 45 ], and Internet of Things (IoT) [ 36 , 40 , 46 , 47 , 48 ]. The applications include transportation [ 4 , 49 , 50 , 51 , 52 , 53 ], healthcare [ 7 , 54 , 55 , 56 ], and others [ 57 , 58 , 59 , 60 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Alomari

Katib

Albeshri

et al. 2021

Sensors

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Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

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Section: Literature Reviewmentioning

confidence: 99%

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Alomari

Katib

Albeshri

et al. 2021

Sensors

Self Cite

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“…This makes the brain of the iResponse system that needs to provide support for many tasks including autonomous decision-making and coordination. A part of this is to manage big data that is particularly challenging for iResponse and smart city systems [215][216][217][218][219]. Pandemics management, including resource planning, requires a sophisticated method that integrates knowledge and operations leveraging cutting-edge technologies [61,89,[220][221][222].…”

Section: Discussionmentioning

confidence: 99%

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

et al. 2021

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SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

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“…A range of technologies is contributing to the development of these smart systems. These include the Internet of Things (IoT) [32][33][34][35][36], social media [21][22][23]37,38], big data [39][40][41][42][43][44], high performance computing (HPC) [45][46][47][48], cloud, fog, and edge computing [34,[49][50][51][52], and machine learning [36,53]. The applications include healthcare [34,39,[54][55][56], transportation [57,58], and others [59,60].…”

Section: Literature Reviewmentioning

confidence: 99%

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

et al. 2020

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Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

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Big Data for Smart Infrastructure Design: Opportunities and Challenges

Cited by 18 publications

References 68 publications

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

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