Algorithms for Big Data in Advanced Communication Systems and Cloud Computing

Stergiou, Christos; Psannis, Kostas E.

doi:10.1109/cbi.2017.28

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…In some cases, only a few percentage of the data set actually can be used for the data modeling. The limitation is due to cost of the proprietary machine storage and computation [9]. Most of the traditional database resides on a single machine.…”

Section: Conventional Approach and Hadoop Approachmentioning

confidence: 99%

Adopting Big Data Analytics Strategy in Telecommunication Industry

Yayah¹,

Ghauth²,

Ting³

2017

JCSCM

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Nowadays, adopting big data is a reality. Telecommunication company or telco must find the right solution to store all information available across the organization to maximize revenue using the analytics. The solution must be able to harness the large volume, variety, and velocity of the data available. One of the challenging actions is how to perform decision making and analysis in real-time. Some of the operational decisions may not comply with the corporation policy which makes it hard to keep up with the modern evolving business environment. Telco needs a platform to improve the business process and sustainable and profitable growth. The significant impacts should involve improvements of the customer experience and more reliable network quality, thereby reducing the customer churn rate. Big data and machine learning represent today's trends for the analytics. With big data analytics, the service provider can utilize the full potential of their data set by correlating, processing, and deciphering the hidden information from it. The conventional machine learning tools without big data are becoming inadequate as the trends shift towards distributed and real-time processing. The service provider needs the solution big data-driven which supports them to achieve timely manner and more accurate insights via the predictive analytics, text mining, and optimization. This paper also explains the characteristics of big data, and several uses of case implementing machine learning inside the big data platform related to telco operation such as mobile fraud detection. A well-known big data processing framework such as Hadoop indicated that there is an integration with machine learning tools such as Mahout, H2O.ai, R-Hadoop components, and KNIME. The advantages of these tools are evaluated based on their scalability, ease of use and extensibility features.

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Section: Conventional Approach and Hadoop Approachmentioning

confidence: 99%

Adopting Big Data Analytics Strategy in Telecommunication Industry

Yayah¹,

Ghauth²,

Ting³

2017

JCSCM

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“…The realization of a regular issue of IoT, which is interoperability of the information and services that must be resolved within the IoT design racing based on the architecture of the Future Internet, academic in addition to the communities of Information and Communication Technology (ICT) 6,7 industry. The new developments and problems upon the interoperability are reviewed, and the process of supporting the interoperability 8 information within the Future Internet designing, considering IoT and cloud computing 9 is discussed by the information models, open service frameworks, and semantic technologies in this article. With the continual progressions in the wireless sensor and actuator networks 10 and with the manufacture of energy effective and less cost hardware for sensor 11 and device communications, primary support is provided by IoT.…”

Section: Introductionmentioning

confidence: 99%

Importance of semantic interoperability in smart agriculture systems

Khatoon

Ahmed

2022

Trans Emerging Tel Tech

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The Internet of Things (IoT) connects people with real-world objects to exchange data. It is performed using services and devices present in the user's activities based on the IoT. Based on a common vocabulary or mappings, knowledge and information can be exchanged by different agents, services, and devices in this scenario. The heterogeneous sources can be represented and integrated by an ontology. The Semantic Web offers semantic interoperability that facilitates communication between heterogeneous devices and technology platforms. The state-of-the-art of IoT semantic interoperability is assessed and reviewed in this work. The importance and challenges of interoperability are discussed in detail with an in-depth analysis of the requirements. IoT based semantic interoperability model is discussed with semantic annotations of data required for heterogeneous IoT devices. Additionally, it has been presented which Semantic Web technologies are incorporated, and the challenges have been studied in this research area. Agriculture is a domain where IoT applications have a lot of potentials. The market is filled with several devices that collect data from the farms and send it to the cloud. Semantic Web technology for applications in agriculture has been discussed in detail with data integration. Semantic resources for agriculture have been enlisted with linked data hubs and semantic data standards. The article aims to address the need and requirement of IoT with semantic interoperability in the field of agriculture. The use of IoT interoperability in agriculture can bring long term benefits to the farmers and increase productivity while reducing the overall costs incurred.

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“…Though big data has no definite size because of its increasing volume from petabytes to exabytes but still many organizations have collected this huge volume of data by conducting reviews and surveys all over the world to advance their procedure of decision-making to maintain a good future for their organizational business. Therefore, cloud comes for processing, storing and managing this large volume of data on internet instead of traditional IT systems [12,13,15,69,71,72,83,84].…”

Section: Introductionmentioning

confidence: 99%

Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach

Khan

et al. 2021

IEEE Access

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Recently, its becomes easy to track down the data due to its availability in a large number. Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet. Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing. To find the security issues/challenges that are faced by software vendors' organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions. This relevant data was comprised from different databases e.g. Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink. Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis. We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency. Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data. The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods. The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms.

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Algorithms for Big Data in Advanced Communication Systems and Cloud Computing

Cited by 15 publications

References 16 publications

Adopting Big Data Analytics Strategy in Telecommunication Industry

Adopting Big Data Analytics Strategy in Telecommunication Industry

Importance of semantic interoperability in smart agriculture systems

Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach

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