Enabling Next Generation Logistics and Planning for Smarter Societies

Suma, Sugimiyanto; Mehmood, Rashid; Albugami, Nasser; Katib, Iyad; Albeshri, Aiiad

doi:10.1016/j.procs.2017.05.440

Cited by 72 publications

(33 citation statements)

References 15 publications

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“…The challenges in this respect include management, integration, and distributed computation of data including the difficulties related to managing the 4V characteristics of big data, i.e., volume, velocity, variety, and veracity. There have been some works on the use of big data platforms in Twitter data analytics in various languages but in different application domains [18][19][20][21][22][23]37,78,79]. To the best of our knowledge, no work has been reported that uses big data technologies for data analytics in healthcare using tweets in the Arabic language.…”

Section: Research Gapmentioning

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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Section: Research Gapmentioning

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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“…Twitter. The result is obtained third top tweets words about that is being talked about and the location of each tweet in an area [12]. Social media to be an essential data source in smart cities.…”

Section: Big Data and Intelligence Technologies Can Store Manage Prmentioning

confidence: 99%

“…To identify tools of big data analytics in smart city, some researchers have expressed their opinion. The tools of big data analytics in smart city presented in Table V. Among the leading platforms for big data storage, processing, and management include IBM infosphere, Hadoop map-reduce, spark, stratosphere and NoSQL database system management [11], [12], [13], [15], [17], and [18]. Based on Table V, Hadoop is the most widely used tools on big data analytics.…”

Section: Tools Of Big Data Analytics In Smart Citymentioning

confidence: 99%

“…Based on Table V, Hadoop is the most widely used tools on big data analytics. Hadoop ma-reduce approach for distributing modeling and processing algorithms to deploy traffic analytics[ll], Hadoop used as storage space to store twitter data streaming [12], [18]. Standalone using Hadoop to store data, Hadoop has significant overhead when the works are submitted to the cluster [15], Hadoop as reliable software with stable, high-performance, and reliable computing capabilities for the various type of data [13].…”

Section: Tools Of Big Data Analytics In Smart Citymentioning

confidence: 99%

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Big Data Analytics Algorithm, Data Type and Tools in Smart City: A Systematic Literature Review

Putra

Kurniawan

2018

2018 International Conference on Information Technology Systems and Innovation (ICITSI)

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The smart city generated rapidly huge of data. Data can analyze with big data analytics technology to give solution from past data in smart city problem and help better solution in decision making. This paper summarizes the existing condition of big data analytics in the smart city in term of the algorithm, data type and tools were built using systematic literature review (SLR) as the standard methodology used to solve any problem by tracing the result from the previous research. The problem in SLR called as research question (RQ). To achieve that goal, we define some RQs related to that scope and clarify each question by tracing previous research. The research paper from reputable journal databases such as EEEE Xplore, Scopus, ScienceDirect, and Springerlink. After synthesizing 15 articles, the results are: algorithm data mining like ANN, Markov, graph mining, etc. needs to improve. That algorithm not enough to handle high data volume, high variety and high velocity to store and processes data; main data type have big chance to give the solution in the smart city is social media. That data has the potential to help in decision making in the smart city problem; Hadoop is the top tool to store and analyze data with highperformance, stable, reliable computing for the different type of data. Combination Hadoop with spark give less overhead to handle the high velocity and volume of data.

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“…societies, and smart infrastructure developments [11,12]; smart transportation infrastructure is at the forefront of these developments [13][14][15]. The use of GPS devices and mobile signals to collect vehicle location and congestion data [16]; the use of big data [17][18][19][20] and high-performance computing (HPC) [17,19,21,22] technologies; mobile, cloud and fog computing [16,[23][24][25][26]; image processing, deep learning, and artificial intelligence (AI) for road traffic analysis and prediction [27][28][29][30]; urban logistics prototyping [31]; vehicular ad hoc networks [24,[32][33][34][35]; autonomous driving [27]; autonomic transportation systems [36][37][38]; and the use of social media for traffic event detection [39][40][41]; are but a few examples. There is a need for innovative uses of the cutting-edge technologies in transportation.…”

Section: Introductionmentioning

confidence: 99%

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

et al. 2019

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Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

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Enabling Next Generation Logistics and Planning for Smarter Societies

Cited by 72 publications

References 15 publications

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

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

Big Data Analytics Algorithm, Data Type and Tools in Smart City: A Systematic Literature Review

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

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