scholarly journals Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Wang Li
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Storage ◽  
Telecommunications Industry ◽  
Cloud Platform ◽  
Service Model ◽  
Information Mining ◽  
Service Guarantee ◽  
Service Mode ◽  
Marketing Approach

In this article, an in-depth study and analysis of the precision marketing approach are carried out by building an IoT cloud platform and then using the technology of big data information mining. The cloud platform uses the MySQL database combined with the MongoDB database to store the cloud platform data to ensure the correct storage of data as well as to improve the access speed of data. The storage method of IoT temporal data is optimized, and the way of storing data in time slots is used to improve the efficiency of reading large amounts of data. For the scalability of the IoT data storage system, a MongoDB database clustering scheme is designed to ensure the scalability of data storage and disaster recovery capability. The relevant theories of big data marketing are reviewed and analyzed; secondly, based on the relevant theories, combined with the author’s work experience and relevant information, a comprehensive analysis and research on the current situation of big data marketing are conducted, focusing on its macro-, micro-, and industry environment. The service model combines the types of user needs, encapsulates the resources obtained by the alliance through data mining for service products, and publishes and delivers them in the form of data products. From the perspective of the development of the telecommunications industry, in terms of technology, the telecommunications industry has seen the development trend of mobile replacing fixed networks and triple play. The development of emerging technologies represented by the Internet of Things and cloud computing has also led to technological changes in the telecommunications industry. Operators are facing new development opportunities and challenges. It also divides the service mode into self-service and consulting service mode according to the different degrees of users’ cognition and understanding of the service, as well as proposes standardized data mining service guarantee from two aspects: after-sales service and operation supervision. A customized data mining service is a kind of data mining service for users’ personalized needs. And the intelligent data mining service guarantee is proposed from two aspects of multicase experience integration and group intelligence. In the empirical research part, the big data alliance in Big Data Industry Alliance, which provides data mining service as the main business, is selected as the research object, and the data mining service model of the big data alliance proposed in this article is applied to the actual alliance to verify the scientific and rationality of the data mining service model and improve the data mining service model management system.

scholarly journals Security-Driven Distributed Platforms for Intellectual Property Resource Provision - A Case Study of TSITE IP

2021 ◽  
Author(s):  
Yang Wang
Keyword(s):  
Intellectual Property ◽  
Data Storage ◽  
Cloud Storage ◽  
Information Age ◽  
Government Support ◽  
Storage Space ◽  
Cloud Platform ◽  
Property Data ◽  
Distributed Platform ◽  
Service Mode

Abstract In the information age, with the development of big data intelligence, Intellectual Property (IP) related data is growing in a geometric progression, so the demand for data storage space is also growing, and the distributed platform of intellectual property data based on cloud storage is also emerging. Cloud computing platform has huge storage space and powerful computing power, and the distributed platform of intellectual property data based on cloud storage has emerged one after another. With this, the privacy and security issues of cloud platform also get more attention. Because the biggest feature of cloud storage is that storage is a service, it puts forward higher requirements for intellectual property services. Firstly, this paper introduces the domestic IP cloud platform services from three perspectives of government support, state-owned enterprises and private enterprises. Secondly, four typical distributed platforms provided by business resources are selected to introduce their operation modes respectively, and the problems faced by domestic IP service modes are summarized emphatically. Then, it compares and discusses the current situation of domestic IP distributed platforms. In view of the current domestic intellectual property service mode, taking TSITE IP as an example, the paper puts forward the design and construction strategy of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age.

Big data audit based on financial sharing service model

2020 ◽  
Vol 39 (6) ◽  
pp. 8997-9005
Author(s):  
Linghan Li ◽  
Yan Feng ◽  
Lei Li
Keyword(s):  
Big Data ◽  
Information Technologies ◽  
Enterprise Management ◽  
Service Model ◽  
Audit Process ◽  
Continuous Development ◽  
Other Information ◽  
Service Mode ◽  
The Government ◽  
Data Audit

As the COVID-19 epidemic continues to spread, the government has managed to prevent people from gathering. The audit work can only be carried out through the network, which puts forward higher requirements for the accuracy and effectiveness of the audit work. Under the background of the continuous development of big data and other information technologies, big data audit has gained important technical support and played an increasingly important role. Units at all levels gradually attach importance to the enterprise management mode based on the financial sharing service mode. This paper analyzes the related problems of big data audit under the financial sharing service mode, involving big data flow, big data preprocessing, big data audit process and other issues, in order to provide useful reference for the implementation of big data audit by using the financial sharing service mode under the influence of COVID-19.

Analysising on the Library Services in the Age of Big Data

2014 ◽  
Vol 1044-1045 ◽  
pp. 1066-1070
Author(s):  
Chen Wei ◽  
Xiao Di Wang ◽  
Ran Ma ◽  
Bing Qi Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Social Networking ◽  
Data Storage ◽  
Rapid Development ◽  
Structured Data ◽  
The Internet ◽  
Library Services ◽  
Library Service

The advent of the age of big data brings not only the rapid development of the Internet, scientific research, social networking and other fields, but also help and challenges to the application of library. For example, the library service applications in data storage, data mining, data analysis, etc. can identify hidden values behind the data only through systematic organization and analysis of massive structured, unstructured, and semi-structured data, ​​in order to predict the future development of library and promote its better development.

Big Data Mining

2018 ◽  
pp. 36-42
Author(s):  
V. Sucharita ◽  
P. Venkateswara Rao ◽  
A. Satya Kalyan ◽  
P. Rajarajeswari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
The Self ◽  
Information Mining ◽  
Error Handling ◽  
Big Data Mining ◽  
Accessible Information ◽  
Tools And Techniques ◽  
Information Streams ◽  
Mining Tools

At present in Big Data era mining of Big Data can help us find learning which nobody has possessed the capacity to find some time recently. There is a developing interest for tools and techniques which can prepare and investigate Big Data effectively and proficiently. In this chapter, the accessible information mining tools and techniques which can deal with Big Data have been abridged. This paper additionally concentrates on tools and techniques for mining of data and information streams. Through better analysis of the vast volumes of information that are getting to be accessible, there is the potential for making speedier progresses in numerous scientific areas what's more, making strides the productivity what's more, victory of numerous organizations. The challenges incorporate not just the self-evident issues of scale, be that as it may too heterogeneity, need of structure, error handling, protection, opportunities at all stages of the analysis from acquisition of data to obtaining to result.

scholarly journals Model of Cloud-Based Services for Data Mining Analysis

2015 ◽  
Vol 8 (4) ◽  
pp. 40
Author(s):  
Aleksandar Karadimce
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Storage ◽  
Data Mining Algorithm ◽  
Analysis Model ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Major Benefit ◽  
Efficient Execution ◽  
Data Mining Analysis

<p class="zhengwen"><span lang="EN-GB">New cloud-based services are being developed constantly in order to meet the need for faster, reliable and scalable methods for knowledge discovery. The major benefit of the cloud-based services is the efficient execution of heavy computation algorithms in the cloud simply by using Big Data storage and processing platforms. Therefore, we have proposed a model that provides data mining techniques as cloud-based services that are available to users on their demand. The widely known data mining algorithms have been implemented as Map/Reduce jobs that are been executed as services in cloud architecture. The user simply chooses or uploads the dataset to the cloud, makes appropriate settings for the data mining algorithm, executes the job request to be processed and receives the results. The major benefit of this model of cloud-based services is the efficient execution of heavy computation data mining algorithm in the cloud simply by using the Ankus - Open Source Big Data Mining Tool and StarfishHadoop Log Analyzer. The expected outcome of this research is to offer the integration of the cloud-based services for data mining analysis in order to provide researchers with reliable collaborative data mining analysis model.<strong></strong></span></p>

scholarly journals BIG DATA TELECOMMUNICATION COMPANY TOOLS TO ENHANCE DECISION-MAKING EFFICIENCY IN COMPLEX ECONOMIC SYSTEMS

2021 ◽  
Author(s):  
Nataliia Geseleva ◽  
Anastasiia Yaroslavtseva
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Customer Service ◽  
Mobile Communication ◽  
Telecommunications Industry ◽  
World Market ◽  
Global Trends ◽  
The World ◽  
Very Large Datasets ◽  
Big Data Technologies

The paper examines the telecommunications industry, its development and impact on economic growth in countries including Ukraine. The characteristics of mobile communication, as a segment of the telecommunications industry that is most actively progressing, both in the world as a whole and in Ukraine, are given. It’s examined a current state of the Ukrainian mobile communication market. Its importance for the national economy is reviewed. The Ukrainian mobile market has been studied; the changes that have taken place in recent years in the direction of global trends in the field of communications. Development trends that encourage mobile operators to develop their own platforms, introduce new products and services are considered. Examples of current developments and services of operators such as virtual mobile automatic telephone exchange, Big Data Scoring, Vodafone Analytics and others are given. The article pays special attention to Big Data processing and analysis technologies. Big data is defined as very large datasets that can be analyzed computationally to reveal patterns, trends, and associations – especially in connection with human behavior and interactions. A big data revolution has arrived with the growth of the Internet, wireless networks, smartphones, social media and other technology. These features of Big Data are the ability to use Data Mining. Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection, warehousing, and computer processing. Data mining processes are used to build machine learning models that power applications including search engine technology and website recommendation programs. Also describes how Big Data affects the retail industry, namely helping to optimize merchandising tactics, personalize customer service, increase advertising effectiveness, target offline shoppers (remarketing) and expand cross-selling. Also in the field of telecommunications, Big Data helps providers to automate and optimize the provision of their services. Thus, the introduction of Big Data technologies will allow Ukraine to become a more competitive country on the world market.

scholarly journals Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

2020 ◽  
Vol 25 (5) ◽  
pp. 645-653
Author(s):  
Qin Xiao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Storage ◽  
Information Service ◽  
Classification Model ◽  
Support Vector ◽  
Aggregation Model ◽  
New Information ◽  
Resource Classification ◽  
Relationship Model

The traditional knowledge service systems have nonuniform data structures. Some data are structured, while some are semi-structured and even non-structured. Big data technology helps to optimize the integration and retrieval of the massive data on library and information (L&I), making it possible to classify the resources and optimize the configuration of L&I resource platforms according to user demand. Therefore, this paper introduces the new information service model of big data resources and knowledge services to the processing of L&I data. Firstly, the data storage structure and relationship model of the L&I resource platform were established, and used to sample and integrate the keywords of resource retrieval. Next, an L&I resource classification model was constructed based on support vector machine (SVM), and applied to extract and quantify the attributes of the keywords of resource retrieval. After that, a knowledge aggregation model was developed for a complex network of multiple L&I resource platforms. Experimental results demonstrate the effectiveness of the proposed knowledge aggregation model. The research findings provide a reference for the application of data mining in resource classification.

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