Dynamic Flow Control for Big Data Transmissions toward 5G Multi-hop Relaying Mobile Networks

The term “Big data” refers to “the high volume of data sets that are relatively complex in nature and having challenges in processing and analyzing the data using conventional database management tools”. In the digital universe, the data volume and variety that, we deal today have grown-up massively from different sources such as Business Informatics, Social-Media Networks, Images from High Definition TV, data from Mobile Networks, Banking data from ATM Machines, Genomics and GPS Trails, Telemetry from automobiles, Meteorology, Financial market data etc. Data Scientists confirm that 80% of the data that we have gathered today are in unstructured format, i.e. in the form of images, pixel data, Videos, geo-spatial data, PDF files etc. Because of the massive growth of data and its different formats, organizations are having multiple challenges in capturing, storing, mining, analyzing, and visualizing the Big data. This paper aims to exemplify the key challenges faced by most organizations and the significance of implementing the emerging Big data techniques for effective extraction of business intelligence to make better and faster decisions

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Fairness and Dynamic Flow Control in Both Unicast and Multicast Architecture Networks

IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) ◽

10.1109/tsmcc.2006.887003 ◽

2007 ◽

Vol 37 (2) ◽

pp. 206-212 ◽

Cited By ~ 2

Author(s):

Yuequan Yang ◽

Zhiqiang Cao ◽

Min Tan ◽

Jianqiang Yi

Keyword(s):

Flow Control ◽

Dynamic Flow

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FinTech and SMEs

FinTech as a Disruptive Technology for Financial Institutions - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-5225-7805-5.ch002 ◽

2019 ◽

pp. 14-41 ◽

Cited By ~ 1

Author(s):

Stefania Marrara ◽

Mirjana Pejic-Bach ◽

Sanja Seljan ◽

Amir Topalovic

Keyword(s):

Image Processing ◽

Cloud Computing ◽

Big Data ◽

Embedded Systems ◽

Mobile Networks ◽

Trust Management ◽

Continuous Growth ◽

Banking Services ◽

Data Analytic

In this chapter, a study about how Italian SMEs understand and use FinTech technologies is presented. The study focuses on FinTech-aided banking services, in particular, due to the fact that these are, at present, the most widely used FinTech technologies available in Italy. The study shows how, despite FinTech entering Italy only in recently, the Italian SMEs market is very active and fruitful for digital companies. In the last years, a continuous growth of investment has seen the development of FinTech technologies in multiple areas, such as mobile networks, big data, trust management, mobile embedded systems, cloud computing, image processing, and data analytic techniques.

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FinTech and SMEs

Research Anthology on Small Business Strategies for Success and Survival ◽

10.4018/978-1-7998-9155-0.ch034 ◽

2021 ◽

pp. 667-688

Author(s):

Stefania Marrara ◽

Mirjana Pejic-Bach ◽

Sanja Seljan ◽

Amir Topalovic

Keyword(s):

Image Processing ◽

Cloud Computing ◽

Big Data ◽

Embedded Systems ◽

Mobile Networks ◽

Trust Management ◽

Continuous Growth ◽

Banking Services ◽

Data Analytic

In this chapter, a study about how Italian SMEs understand and use FinTech technologies is presented. The study focuses on FinTech-aided banking services, in particular, due to the fact that these are, at present, the most widely used FinTech technologies available in Italy. The study shows how, despite FinTech entering Italy only in recently, the Italian SMEs market is very active and fruitful for digital companies. In the last years, a continuous growth of investment has seen the development of FinTech technologies in multiple areas, such as mobile networks, big data, trust management, mobile embedded systems, cloud computing, image processing, and data analytic techniques.

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A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

International Journal of Organizational and Collective Intelligence ◽

10.4018/ijoci.2019040102 ◽

2019 ◽

Vol 9 (2) ◽

pp. 16-30 ◽

Cited By ~ 1

Author(s):

Hind Bangui ◽

Mouzhi Ge ◽

Barbora Buhnova

Keyword(s):

Big Data ◽

Internet Of Things ◽

Mobile Networks ◽

Data Clustering ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Future Internet ◽

Research Challenges ◽

Initial Stage ◽

Big Data Technologies

Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

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Big Data-Driven Cellular Information Detection and Coverage Identification

Sensors ◽

10.3390/s19040937 ◽

2019 ◽

Vol 19 (4) ◽

pp. 937 ◽

Cited By ~ 3

Author(s):

Hai Wang ◽

Su Xie ◽

Ke Li ◽

M. Ahmad

Keyword(s):

Big Data ◽

Mobile Networks ◽

Network Optimization ◽

Base Station ◽

Data Driven ◽

Related Data ◽

Massive Number ◽

Information Detection ◽

Conventional Methods ◽

Wireless Network Optimization

As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service.

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