Big data management in participatory sensing: Issues, trends and future directions

Karim, Ahmad; Siddiqa, Aisha; Safdar, Zanab; Razzaq, Maham; Gillani, Syeda Anum; Tahir, Huma; Kiran, Sana; Ahmed, Ejaz; Imran, Muhammad

doi:10.1016/j.future.2017.10.007

Cited by 33 publications

(12 citation statements)

References 46 publications

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“…The emerging mismatch between social demand and supply leads to destabilization of the strategic sustainability of such projects. Therefore, there must be information security controls to ensure privacy, integrity, and limited access to the information that is submitted to the crowdsourcing system [23].…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing

Liu

Panfilova

Mikhaylov

et al. 2021

Wireless Pers Commun

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This research reviews challenges in building sustainable relationships between the parties involved in the crowdfunding and crowdsourcing projects, which are running in extreme situations, such as the COVID-19 pandemic. This study aims to solve problems that generate the crowdsourcing concerns and to find better alternatives to increase trust for crowdfunding among donors, as this impacts their strategic sustainability in the conditions of turbulence and COVID-induced financial crisis. It was found that factors influence donor decisions in different ways, yet the common tendency for donor activity is non-monotonicity. Future development in the field of sustainable relationships should focus on creating a donor classification system.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing

Liu

Panfilova

Mikhaylov

et al. 2021

Wireless Pers Commun

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“…For this reason, high performance is pressing in business domain when depending on artificial intelligence. On the other hand, dealing with big data in the business domain requires solving the performance issue [6].…”

Section: Problem Statementmentioning

confidence: 99%

Artificial Intelligence based Recommendation System for Analyzing Social Bussiness Reviews

Alanazi¹,

Alseid²

2021

IJACSA

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Recently, analysing reviews presented by clients to products that are provided by e-commerce companies, such as Amazon, to produce efficient recommendations has been receiving a lot of attention. However, ensuring and generating effective recommendations on time is a challenge. This research paper proposes an artificial intelligence-based system. The proposed system uses the Incremental Learning -based Method (ILbM) to learn a neural network classifier. The ILbM uses the bagging technique in the process of training the classifier. To ensure a high degree of performance, the ILbM is implemented on the Hadoop since it allows the execution in parallel. Compared to a similar system, the proposed system shows better results in terms of accuracy (97.5%), precision (95.7%), recall (91.5%), and time of response (36 seconds).

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“…Multimedia sensors like the microphone, dual-camera, and finger print sensors integrated in smartphone allow to employ this devices in a wide variety of applications (García, Tomás, Parra, & Lloret, 2018). All these data generated from sensors needs to be stored and processed in cloud due to resource shortage in the mobile device (Karim et al, 2017). Cloud computing technologies for mobile devices offers an innovative method of delivering IT services efficiently like IT services that are available at all times and from all location (O.…”

Section: Mobile Cloudmentioning

confidence: 99%

Real-time big data processing for anomaly detection: A Survey

Habeeb¹,

Nasaruddin²,

Gani³

et al. 2019

International Journal of Information Management

Self Cite

338

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The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed.

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Big data management in participatory sensing: Issues, trends and future directions

Cited by 33 publications

References 46 publications

COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing

COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing

Artificial Intelligence based Recommendation System for Analyzing Social Bussiness Reviews

Real-time big data processing for anomaly detection: A Survey

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