Comprehensive Review of Challenges &amp; Technologies for Big Data Analytics

Jamil, Abid; Abdullah, Muhammad Ibrahim; Javed, Muhammad Asad; Hassan, Muhammad

doi:10.1109/ccet.2018.8542219

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Video sharing has increased over the past decade. Statistics concerning upload volume on YouTube platforms show the indescribable interest of end users towards video modalities [1]. However, this diversity and variety make it difficult for creators to compel users to watch the content, let alone click for fast viewing.…”

Section: Introductionmentioning

confidence: 99%

“…The demand for generating animated GIFs in an automated manner has been instigated by streaming platforms, which aim to deliver a succinct preview of video content within a time span of 3 to 15 seconds [5]. As shown in Figure I, this encompasses the presentation of artistic media pertaining to sports videos: (1) an animated GIF when the user hovers the mouse on a static thumbnail (below) and (2) the selection of prominent frames from the feature-length video (above). It is noteworthy that the animated GIF serves as the primary gateway through which users make informed decisions about whether to watch a feature-length video.…”

Section: Introductionmentioning

confidence: 99%

“…• We achieved state-of-the-art results, both in terms of computational efficiency and accuracy, i.e., 3.69 times faster and > 10% accuracy, respectively. To the best of our knowledge, this is the first attempt at generating personalized GIFs using LTC and frame-ranking methods on end-user devices for streaming platforms 1 .…”

Section: Introductionmentioning

confidence: 99%

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FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

Mujtaba,

Khowaja,

Jarwar

et al. 2024

IEEE Trans. Big Data

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Generating video highlights in the form of animated graphics interchange formats (GIFs) has significantly simplified the process of video browsing. Animated GIFs have paved the way for applications concerning streaming platforms and emerging technologies. Existing studies have led to large computational complexity without considering user personalization. This paper proposes lightweight method to attract users and increase views of videos through personalized artistic media, i.e., static thumbnails and animated GIF generation. The proposed method analyzes lightweight thumbnail containers (LTC) using the computational resources of the client device to recognize personalized events from feature-length sports videos. Next, the thumbnails are then ranked through the frame rank pooling method for their selection. Subsequently, the proposed method processes small video segments rather than considering the whole video for generating artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data; thus, the proposed method complies with sustainable development goals. Furthermore, the proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data. Experiments reveal that the computational complexity of our method is 3.73 times lower than that of the state-of-the-art method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

Mujtaba,

Khowaja,

Jarwar

et al. 2024

IEEE Trans. Big Data

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“…An increasing number of Internet of Things (IoT) initiatives have been proposed in recent times to improve the quality of human life. Those initiatives pose real-time challenges which have been the focus of many researchers and practitioners in recent times [1][2][3][4][5]. Indeed, IoT and big data sources can be found in a number of applications, e.g., smart homes [6,7], smart buildings [8,9], smart grids [10], transportation [11], healthcare [12], disaster management [13], financial sector [14], retail management [15], and smart cities [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Reference Architecture for IoT-Enabled Smart Buildings

2022

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The management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.

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