Research on Data Storage System of E-Commerce Purchasing Based on Big Data Technology

The Big Data is unavoidable considering the place of the digital is the predominant form of communication in the daily life of the consumer. The control of its stakes and the quality its data must be a priority in order not to distort the strategies arising from their treatment in the aim to derive profit. In order to achieve this, a lot of research work has been carried out companies and several platforms created. MapReduce, is one of the enabling technologies, has proven to be applicable to a wide range of fields. However, despite its importance recent work has shown its limitations. And to remedy this, the Distributed Hash Tables (DHT) has been used. Thus, this document not only analyses the and MapReduce implementations and Top-Level Domain (TLD)s in general, but it also provides a description of a model of DHT as well as some guidelines for the planification of the future research.

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A Logistic Based Mathematical Model to Optimize Duplicate Elimination Ratio in Content Defined Chunking Based Big Data Storage System

Symmetry ◽

10.3390/sym8070069 ◽

2016 ◽

Vol 8 (7) ◽

pp. 69 ◽

Cited By ~ 2

Author(s):

Longxiang Wang ◽

Xiaoshe Dong ◽

Xingjun Zhang ◽

Fuliang Guo ◽

Yinfeng Wang ◽

...

Keyword(s):

Mathematical Model ◽

Big Data ◽

Data Storage ◽

Storage System ◽

Data Storage System ◽

Big Data Storage

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Overview of Big-Data-Intensive Storage and Its Technologies

Advances in Data Mining and Database Management - Handbook of Research on Big Data Storage and Visualization Techniques ◽

10.4018/978-1-5225-3142-5.ch002 ◽

2018 ◽

pp. 33-74

Author(s):

Richard S. Segall ◽

Jeffrey S. Cook

Keyword(s):

Big Data ◽

Data Storage ◽

Storage Systems ◽

Storage System ◽

Management Strategies ◽

Sensor Data ◽

Data Intensive Computing ◽

Data Intensive ◽

Future Challenges ◽

Data Storage System

This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about data-intensive computing and types of parallel processing approaches. It also highlights the points that display how data-intensive computing systems differ from other forms of computing. A discussion on the importance of Big Data computing is put forth. The current and future challenges of storage in genomics are discussed in detail. Also, storage and data management strategies are given. The chapter's focus is then on the software challenges for storage. Storage use cases are provided like DataDirect Networks, SDSC, etc. The list of storage tools and their details are provided. A small section discusses the sensor data storage system. Then a table is provided that shows the top 10 cloud storage systems for data-intensive computing using Big Data in the world. Top 500 Big Data storage servers statistics are also displayed effectively by the images from Top500 website.

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