Parallel Shortest Path Big Data Graph Computations of US Road Network Using Apache Spark: Survey, Architecture, and Evaluation

Arfat, Yasir; Suma, Sugimiyanto; Mehmood, Rashid; Albeshri, Aiiad

doi:10.1007/978-3-030-13705-2_8

Cited by 13 publications

(8 citation statements)

References 62 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

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“…Moreover, we will use deep learning to improve classification performance. A major theme of our research is smart cities and societies [23]- [25], big data [26]- [28], high performance computing [29], [30], healthcare [31]- [33], information systems [34], [35], system integration [36], [37], and artificial intelligence [38], [39]. Future work on DNA profiling will also look into developing new smart applications for DNA profiling and its integration with other smart city systems.…”

Section: Discussionmentioning

confidence: 99%

DNA Profiling: An Investigation of Six Machine Learning Algorithms for Estimating the Number of Contributors in DNA Mixtures

Alotaibi¹,

Alsolami²,

Mehmood³

2021

IJACSA

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DNA (Deoxyribonucleic acid) profiling involves analysis of sequences of individual or mixed DNA profiles to identify persons these profiles belong to. DNA profiling is used in important applications such as for paternity tests, in forensic science for person identification on a crime scheme, etc. Finding the number of contributors in a DNA mixture is a major task in DNA profiling with challenges caused due to allele dropout, stutter, blobs, and noise. The existing methods for finding the number of unknowns in a DNA mixture suffer from issues including computational complexity and accuracy of estimating the number of unknowns. Machine learning has received attention recently in this area but with limited success. Many more efforts are needed for improving the robustness and accuracy of these methods. Our research aims to advance the state-of-the-art in this area. Specifically, in this paper, we investigate the performance of six machine learning algorithms --Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Stochastic Gradient Descent (SGD), and Gaussian Naïve-Bayes (GNB) --applied to a publicly available dataset called PROVEDIt, containing mixtures with up to five contributors. We evaluate the algorithmic performance using confusion matrices and four performance metrics namely accuracy, F1-Score, Recall, and Precision. The results show that LR provides the highest Accuracy of 95% for mixtures with five contributors.

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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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Parallel Shortest Path Big Data Graph Computations of US Road Network Using Apache Spark: Survey, Architecture, and Evaluation

Cited by 13 publications

References 62 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

DNA Profiling: An Investigation of Six Machine Learning Algorithms for Estimating the Number of Contributors in DNA Mixtures

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

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