Trajectory big data:data,applications and techniques by Jia-jie XU, Kai ZHENG, Ming-min CHI, Yang-yong ZHU, Xiao-hui YU, Xiao-fang ZHOU

“…,and tremendous efforts have been made to support effective trajectory data management,including trajectory data pre-processing,which handles issues such as high redundancy,low precision and inconsistency of sampling; trajectory database technologies,concerning the efficient and effective storage of trajectory data and query processing; trajectory data warehousing,which supports the analytics on large-scale trajectory data;knowledge discovery,by which useful patterns can be extracted from trajectory data.A survey of trajectory big data analytics from three different aspects:data,applications and techniques is provided.…”
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A comprehensive review of big data applications by Soheil Fakheri

Subjects: “…big data application…”
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Research progress of triangle counting in big data by Hongqiao JIN, Yihong DONG

“…Counting triangles in a graph is an important step to calculate the clustering coefficient and the transitivity ratio of the network，which is widely used in important role identification，spam detection，community discovery，biological detection etc.Counting triangles algorithm is mainly faced with two major problems of space-time consumption and accuracy.The representative algorithm of the counting triangles in the big graph was introduced.There existed two kinds of algorithms，which were exact counting algorithm and approximate counting algorithm.Exact counting algorithms were divided into internal memory algorithm，external memory algorithm and distributed algorithm.The space-time consumption or I/O consumption of exact counting algorithm was very large.Approximate counting algorithms were divided into auxiliary algorithm，static algorithm and streaming algorithm.In the end，the counting triangles algorithms were summarized.…”
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Complexity Problems Handled by Big Data Technology by Zhihan Lv, Kaoru Ota, Jaime Lloret, Wei Xiang, Paolo Bellavista

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Teach Aquaculture Curriculum: How Big Is That Pond? by Cortney L. Ohs, Amber L. Garr, R. Leroy Creswell, Elisa J. Livengood, Brian E. Myers, Carlos V. Martinez, Frank A. Chapman, Craig S. Kasper

“…FA170/FA170: Teach Aquaculture Curriculum: How big is that pond? (ufl.edu) …”
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Big Data Deep Learning: Challenges and Perspectives by Xue-Wen Chen, Xiaotong Lin

“…With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. …”
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Big Data Technology：Current Applications and Prospects by Jianxin Liao

Subjects: “…big data…”
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Review of prime issues in big data storage by Zhi Zhou, Javad Pourqasem, Shadi Sayadmanesh

Subjects: “…big data…”
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Big Data Application in Network Security Analysis by Shuai Wang, Laifu Wang, Huamin Jin, Jun Shen

Subjects: “…big data…”
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Research on operators' big data technologies and applications by Tao CHEN, Meng LU, HENYanming C

Subjects: “…big data processing…”
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Discussion on Integration of Data Warehouse and Big Data by Juan Yu

Subjects: “…big data…”
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A practice of user profile in big data by Xiaoxia WANG, Jingsha LIU, Dandan XU

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Digital Economy, Big Data and Competition Law by Roberto Augusto Castellanos Pfeiffer

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Big Data Development Strategy for Telecom Operators by Yongjun Huang, Ming Feng, Shengyong Ding, Yongbing Fan

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Political Advertising by Big Business after Rubicon by Paul Vorster

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AI, big data, and robots for the evolution of biotechnology by Haseong Kim

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Big Data in Ubiquitous Wireless Sensor Networks by Fu Xiao, Chongsheng Zhang, Zhijie Han

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Big data security features and operation practices by Zhiyong LIU, Zhongjiang HE, Yilong RUAN, Junfeng SHAN, Chao ZHANG

Subjects: “…big data…”
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The history of science, the ‘big picture’ and the politics of scale by James Poskett, James Poskett

“…What does it mean to write the history of science and the ‘big picture’? In this introduction, I argue that ‘scale’ is a crucial but relatively underutilized concept for addressing this question. …”
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Ontologies in modelling and analysing of big genetic data by N. L. Podkolodnyy, O. A. Podkolodnaya, V. A. Ivanisenko, M. A. Marchenko

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