Reality mining: A prediction algorithm for disease dynamics based on mobile big data

2017 ◽  
Vol 379 ◽  
pp. 82-93 ◽  
Author(s):  
Yuanfang Chen ◽  
Noel Crespi ◽  
Antonio M. Ortiz ◽  
Lei Shu
Keyword(s):  
Big Data ◽  
Prediction Algorithm ◽  
Disease Dynamics ◽  
Mobile Big Data ◽  
Reality Mining

Mobile Big Data Query Based on Double R-Tree and Double Indexing

2013 ◽  
Vol 756-759 ◽  
pp. 916-921
Author(s):  
Ye Liang
Keyword(s):  
Big Data ◽  
Dynamic Condition ◽  
Index Structure ◽  
Limited Area ◽  
Universal Method ◽  
Mobile Objects ◽  
Data Query ◽  
Mobile Big Data ◽  
Structure Query ◽  
Window Query

The amount of data in our industry and the world is exploding. Data is being collected and stored at unprecedented rates. The challenge is not only to store and manage the vast volume of data, which is also called big data, but also to analyze and query from it. In order to put forward the universal method to response mobile big data query, queries are separated and grouped according to kinds of query for massive mobile objects in the space. The indexing method for grouping the mobile objects with Grid (GG TPR-tree) has great efficiency to manage a massive capacity of mobile objects within a limited area, but it only could meet a part of requirements for mobile big data query if the GG TPR-tree was used solely. This thesis offers solutions to simple immediate query, simple continuous query, active window query, and continuous window query, dynamic condition query and other query requests by employing DTDI index structure. The experiments prove that with the support of DTDI index structure, query of massive mobile objects has higher precision and better query performance.

Protecting lightweight block cipher implementation in mobile big data computing

2016 ◽  
Vol 11 (2) ◽  
pp. 252-264
Author(s):  
Weidong Qiu ◽  
Bozhong Liu ◽  
Can Ge ◽  
Lingzhi Xu ◽  
Xiaoming Tang ◽  
...  
Keyword(s):  
Big Data ◽  
Block Cipher ◽  
Mobile Big Data ◽  
Lightweight Block Cipher ◽  
Big Data Computing

An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

2021 ◽  
pp. 002072092098399
Author(s):  
Zhang Xiao-Wen ◽  
Zeng Min
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Fuzzy Model ◽  
Reference Value ◽  
Developed Countries ◽  
Theoretical Research ◽  
Prediction Algorithm ◽  
Real Economy ◽  
Stock Volatility ◽  
The Us

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.

Mobile Big Data

2016 ◽  
pp. 138-158
Author(s):  
Shivom Aggarwal ◽  
Abhishek Nayak
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Value Chain ◽  
Mobile Technologies ◽  
Complex Concept ◽  
Current Trends ◽  
Mobile Big Data ◽  
Challenges And Opportunities ◽  
New Challenges ◽  
Data Ecosystem

Mobile technologies have given rise to tremendous amounts of data in real-time, which can be unstructured and uncertain. This growth can be attributed as Mobile Big Data and provides new challenges and opportunities for innovation. This chapter attempts to define the concept of Mobile Big Data, provide description of various sources of Mobile Big Data and discuss SWAI (Sources Warehousing Analytics Insights) model of Big Data processing. To understand this complex concept, it is important to visualize the Big Data ecosystem, respective players. Moreover, mobile computing, Internet of things, and other associated technologies have been discussed in light of marketing and communications based applications. The current trends in Mobile Big Data and associated value chain help us understand where the next frontiers of innovation are and how one can create value. This is linked to the future aspects of the Mobile Big Data and evolution of technologies from now onwards.

Assessing Prime-Time for Geotargeting with Mobile Big Data

2015 ◽  
Author(s):  
Syagnik (Sy) Banerjee ◽  
Vijay Viswanathan ◽  
Kalyan Raman ◽  
Hao Ying
Keyword(s):  
Big Data ◽  
Prime Time ◽  
Mobile Big Data

Mobile Big Data: The Fuel for Data-Driven Wireless

2017 ◽  
Vol 4 (5) ◽  
pp. 1489-1516 ◽  
Author(s):  
Xiang Cheng ◽  
Luoyang Fang ◽  
Liuqing Yang ◽  
Shuguang Cui
Keyword(s):  
Big Data ◽  
Data Driven ◽  
Mobile Big Data
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