A Study on Traffic Big Data Mapping Using the Grid Index Method

<abstract> <p>Big data has attracted a lot of attention in many domain sectors. The volume of data-generating today in every domain in form of digital is enormous and same time acquiring such information for various analyses and decisions is growing in every field. So, it is significant to integrate the related information based on their similarity. But the existing integration techniques are usually having processing and time complexity and even having constraints in interconnecting multiple data sources. Many of these sources of information come from a variety of sources. Due to the complex distribution of many different data sources, it is difficult to determine the relationship between the data, and it is difficult to study the same data structures for integration to effectively access or retrieve data to meet the needs of different data analysis. In this paper, proposed an integration of big data with computation of attribute conditional dependency (ACD) and similarity index (SI) methods termed as ACD-SI. The ACD-SI mechanism allows using of an improved Bayesian mechanism to analyze the distribution of attributes in a document in the form of dependence on possible attributes. It also uses attribute conversion and selection mechanisms for mapping and grouping data for integration and uses methods such as LSA (latent semantic analysis) to analyze the content of data attributes to extract relevant and accurate data. It performs a series of experiments to measure the overall purity and normalization of the data integrity, using a large dataset of bibliographic data from various publications. The obtained purity and NMI ratio confined the clustered data relevancy and the measure of precision, recall, and accurate rate justified the improvement of the proposal is compared to the existing approaches.</p> </abstract>

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Intelligent Classification Method of Low Occupancy Big Data Based on Grid Index

Scientific Programming ◽

10.1155/2021/7965297 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Haiyan Zhao ◽

Shuangxi Li

Keyword(s):

Big Data ◽

Data Storage ◽

Load Balance ◽

Virtual Machines ◽

Classification Method ◽

Storage Efficiency ◽

Storage Process ◽

Grid Index ◽

Grid Application ◽

Big Data Storage

In order to enhance the load balance in the big data storage process and improve the storage efficiency, an intelligent classification method of low occupancy big data based on grid index is studied. A low occupancy big data classification platform was built, the infrastructure layer was designed using grid technology, grid basic services were provided through grid system management nodes and grid public service nodes, and grid application services were provided using local resource servers and enterprise grid application services. Based on each server node in the infrastructure layer, the basic management layer provides load forecasting, image backup, and other functional services. The application interface layer includes the interfaces required for the connection between the platform and each server node, and the advanced access layer provides the human-computer interaction interface for the operation of the platform. Finally, based on the obtained main structure, the depth confidence network is constructed by stacking several RBM layers, the new samples are expanded by adding adjacent values to obtain the mean value, and the depth confidence network is used to classify them. The experimental results show that the load of different virtual machines in the low occupancy big data storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that this method can enhance the load balance in the data storage process and improve the storage efficiency.

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