Big Data Challenges & Applications in Multimedia

Big Data Challenges and Applications Techhyme

Multimedia big data is the theoretical concept. There is no particular description for multimedia big data. Multimedia big data concept differs from big data in terms of heterogeneous, human-centric, different forms of media, and larger size as related to the typical big data.

Some of the features of multimedia big data are given below:

Multimedia big data comprises an enormous number of data types as compared to traditional big data. Multimedia datasets are more understandable by a human as compared to the machines.
The multimedia big data is more difficult to processing as compared to traditional big data because which consists of different types of audio, and videos data such as interactive videos, stereoscopic three-dimensional videos, social videos and so forth.
It is challenging to model and characterize the multimedia big data as these data are collected from diverse (heterogeneous) sources such as pervasive portable mobile devices, the sensor-embedded devices, the Internet of Things (IoT), Internet, digital games, virtual world, and social media.
It is thought-provoking to analyze the content and context of multimedia big data, which is not constant over a period of time and space.
Security of multimedia big data is complicated due to rapid increases in the sensitive video data on communication.
There is a necessity to process the multimedia big data swiftly and uninterruptedly in order to cope with the transmission speed of the network. For real-time computing, the multimedia big data is needed to be stored in order to transfer the enormous amount of data in real time.

The big data provides more challenges such as data storage, to manage the data, data acquisition, and analysis. Traditional Relational Database Management System (RDBMS) is not suitable for unstructured and semi-structured data. The database management and analysis relies on RDBMS, which uses more expensive hardware.

The traditional relational database management system could not manage the large capacity and diversity of big data concerning different types of data and sources. On a different perspective, the research community has proposed a solution to handle a large volume of big data. For example, distributed file system and NoSQL databases provide the permanent solution to store and manage the large-scale chaotic datasets, and the cloud computing provides a solution to satisfy the needs on infrastructure for big data. Various technologies are developed for the applications of big data applications.

Also Read: 100+ Data Structures and Algorithms Questions

Some of the big data challenges are as follows:

Data Representation: The different levels of big datasets such as structure, semantics, granularity, and openness. The main goal of data representation is that the data is more significant for computer analysis and user comprehensible. The inappropriate way of data representation reduces the originality of data and analysis. An efficient data representation achieves an efficient data operation on datasets.

Redundancy reduction and data reduction: Big datasets have a large number of redundant data. It is an efficient method to decrease the highly redundant data generated by sensor networks from IoT applications and reduces the cost of the whole system.

Analytical mechanism: Within the limited amount of period, the analytical mechanisms of big data process the vast volume of heterogeneous data. Traditional RDBMS has the limitation of scalability and expandability, which could not encounter the performance requirements. The non-relational databases system could process the unstructured data. It is the unique advantage of non-relational databases system; still, some problems are encountered in terms of performance and specific applications. The best solution to overcome the tradeoff of relational and non-relational databases for big data is mixed database architecture (Facebook and Twitter), which integrates the advantages of both.

Expendability and Scalability: The logical scheme and algorithm for big data should sustain the current as well as forthcoming datasets and process the enormous growth of complex data.

Energy Management: The energy consumption is a significant problem, which brings the attention of economy of the country. The different operations of multimedia big data such as acquisition, processing, analysis, storing, and broadcasting of the huge volume of big data consumes more energy. The system-level power depletion and managing established to ensure the expandability and accessibility of big data.

As compared to the traditional big data (text-based big data), the multimedia big data has more challenges related to basic operations like storing of enormous datasets, processing, transmission, and analysis of data.

1. Multimedia Data Abstraction

Data Types: Videos, audio, text, IoT devices, Social networks, etc.
Challenges: Volume, real-time, unstructured, noisy, uncertainity, etc.

2. Multimedia Database

Data storage: RDBMS, MMDBMS, NoSQL, Graph DBS, ORDBMS, Key value stores, etc.
Challenges: store, manage, extract/retrive, unstructured data and heterogenous data sets

3. Multimedia data sharing

Sharing system: Cloud, online file sharing system, wireless data sharing
Challenges: More storage, Bandwidth, maximum file size, data types, human efforts

4. Multimedia Data Mining

Data Processing: Data cleaning, Data transformation, data reduction, etc.
Feature Analysis: Videos, Audios, textual, motion, spatiotemporal, etc.
Machine learning: Supervised, unsupervised, semi-structured, etc.
Challenges: Multimodality data representation, Complexity, noisy, semistructured data efficiency, real time, accuracy

Big Data Applications in Multimedia Big Data

The multimedia big data management system depends on the big data techniques to process and manipulate the multimedia big data efficiency. The application of big data in multimedia big data analytics are as follows,

Social Networks: Many research works have been performed on social network big data analysis.

Tufeki et al., analyses the challenges of social activities and behaviors of people on Twitter hashtags, which has a large number of datasets, visibility, and ease of access.
Ma et al. address the new emerging technology called social recommender system, and it is mainly used in social networks to share multimedia information.
Davidson et al. presented YouTube video framework activities in which it integrates social information and personalizes videos in a recommendation system.

Smartphones: Recently, smartphones have overhauled the usage of other electronic devices such as personal computers, and laptops. The smartphones have advanced technologies and capabilities such as Bluetooth, Camera, network connection, Global Positioning System (GPS), and high potential Central Processing Unit (CPU), etc. Using smartphones, the user can manipulate, process, and access the heterogeneous multimedia data.

Mobile sensing issues of smartphones sensors and data analyses such as data sharing, influence, security, and privacy issues are addressed by Lane et al. The other challenges of smartphones are investigated such as the large volume of data, security, and multimedia cloud computing.

Surveillance Videos: The significant sources of multimedia big data is surveillance videos.

Xu et al. present the dawn of big data innovative solutions for multimedia big data such as volume, velocity, variety, and value of multimedia generates from surveillance sources such as traffic control, IoT, and criminal investigation.
Shyu et al. present the concept of how to detect semantic concept from the surveillance videos. One of the promising applications of multimedia big data is smart city surveillance.

Other applications: The applications of multimedia big data can be categorized as health informatics, smart TVs, Internet of Things (IoT), disaster management system, etc. The biomedicine data and healthcare data are considered as the primary origin of the multimedia big data. It consists of variety and a huge size of data such as patient records, medical images, physician prescription, etc. Kumari et al. examined the part of IoT, fog computing, and cloud computing for health care service.

Source: Multimedia Big Data Computing for IoT Applications (Intelligent Systems Reference Library)

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