Uncertainty in big data analytics: survey, opportunities, and challenges

Hariri, Reihaneh H.; Fredericks, Erik M.; Bowers, Kate M.

doi:10.1186/s40537-019-0206-3

Cited by 407 publications

(273 citation statements)

References 58 publications

(92 reference statements)

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“…AI paradigms Machine learning [26]; [63]; [67][68][69][70]; Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67][68][69][70][71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142]…”

Section: Category Element Referencementioning

confidence: 99%

“…Deep learning has already had success recognizing patterns from a wide range of data sources including images, audio, video, and other sensors [104]. The application of AI has the potential to remove the need for humans to complete many repetitive business tasks-particularly those relying on observation-,potentially reducing costs and freeing up resources for more productive or innovative fields [112,130].…”

Section: Category Element Referencementioning

confidence: 99%

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Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

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Section: Category Element Referencementioning

confidence: 99%

Section: Category Element Referencementioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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“…The authors' previous mouse tracking session [31] also reaches the same level of Internet traffic if over 100 students are present. Other than volume, mouse tracking met the other 5Vs criteria of big data [17]: velocity, the amount of data generated especially in real-time which is explained in further sections, veracity; meaning that data loss may often occur due to limited connectivity, which can lead to inconsistent data; variety, which is discussed in further sections and previous work [31]; and value, which is discussed in the next paragraph.…”

Section: Default Eye and Mouse Tracking Generates Big Datamentioning

confidence: 99%

“…The biggest problem with default mouse tracking (as well as eye tracking) is the huge volume of data generated, which can be categorized as big data [17,18]. This high volume is due to the use of geometrical data where each event that occurs on each point of the webpage is recorded.…”

mentioning

confidence: 99%

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Purnama

Usagawa

2020

J Big Data

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IntroductionWe are living in the age of information where information and communication technologies are becoming mainstream [1]. Computers and the Internet have become inseparable from our daily lives. It is now possible to do many things without the need to travel and wait; examples include, long-distance communication through online text, voice and video calls, social interaction via social media, online learning, ecommerce or online shopping, and many forms of entertainments such as online music, online videos, and online games. Most importantly, the Internet has become the primary source of information around the world.Online analytics has become a major area of interest because it enables the study of users' online behaviors. These include, users' attentions and, students' performance in online learning. Businesses also use online analytics to capture consumers' interests in AbstractPageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.

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“…Big data collected by the surveillance system are characterized by high Volume, low Veracity, high Variety, high Velocity, and high Value [15]. The navigation data collected during sailing have the same 5V characteristics.…”

Section: Big Data Analyticsmentioning

confidence: 99%

Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

Sun

Wang

Liu³

et al. 2019

JMSE

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The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship's main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship "YUMING" are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed.Harilaos N. Psarafits et al. stated that the ship speed is a decision variable for fuel consumption and emissions [5]. In fact, most methods applied to improve ship efficiency are to adjust ship speed. Route and speed optimization are operational procedures that may be used to improve shipping efficiency and EEOI. Inge Norstad et al. used a recursive smoothing algorithm to optimize the speed for ship routing and scheduling for a tramp ship [6]. Wang Shuai'an et al. conducted an in-depth analysis on the relationship between fuel consumption and ship speed based on the historical operational data of a container liner and optimized the ship speed by using nonlinear programming [7]. Ming-Chung Fang et al. applied a heuristic method to optimize ship routing in different weather conditions [8].Lower steaming is another kind of validated method for decreasing fuel consumption and the EEOI. However, such operational strategy is limited by many factors, including the ETA (estimated time of arrival), fuel price, charter rates, influence of speed reduction on engine efficiency, and so forth [9,10].Joan P. Peterse developed a fuel consumption model by way of a machine learning method and ship navigation data [11]. Benjamin applied a neural network (NN) trained by noon report data to establish a propulsion power model under hydrostatic conditions [12]. Wang Kai et al. used the wavelet packet neural network to predict the sea conditions for a short-journey of a sailing ship and introduced a real-time energy efficiency model under predicted sea conditions [13].This paper suggests a method that could be used to identify lower EEOI using real time operational data. An EEOI during a fixed time span is introduced to indicate fuel efficiency. A back propagation neural network, which is trained by navig...

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Uncertainty in big data analytics: survey, opportunities, and challenges

Cited by 407 publications

References 58 publications

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

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