In the data communication system, the real-time information interaction of communication device increases the risk of privacy sensitive data being tampered with. Therefore, maintaining data security is one of the most important issues in network data communication. Because the timestamp is the most important way to authenticate data in information interaction, it is very necessary to provide timestamp service in the data communication system. However, the existing centralized timestamp mechanism is difficult to provide credible timestamp service, and users can conspire with timestamping servers to forge timestamps. Therefore, this paper designs a distributed timestamp mechanism based on continuous verifiable delay functions. It utilizes multiple independent timestamp servers to provide timestamp services in a distributed model and appends the timestamp to the data once the data is generated. Thus, it can prove that the data already exists at a certain time and ensure the accuracy of the timestamp. Moreover, a digital blind signature based on elliptic curve cryptography is utilized to solve the problem of timestamp forgery in timestamp service. Finally, the security analysis of the scheme ensures the data security of data communication system and the concurrency rate of timestamp. The experimental results also show that the scheme greatly improves the efficiency of digital signatures. 

/pdf/distributed-timestamp-mechanism-based-on-verifiable-delay-j7fx5pze.pdf

Distributed Timestamp Mechanism Based on Verifiable Delay Functions

Abstract This article addresses the elaboration of a canonical model, involving methods, techniques, metrics, tools, and Big Data, applied to the knowledge of seasonal climate prediction, aiming at greater dynamics, speed, conciseness, and scalability. The proposed model was hosted in an environment capable of integrating different types of meteorological data and centralizing data stores. The seasonal climate prediction method called M-PRECLIS was designed and developed for practical application. The usability and efficiency of the proposed model was tested through a case study that made use of operational data generated by an atmospheric numerical model of the climate area found in the supercomputing environment of the Center for Weather Forecasting and Climate Studies linked to the Brazilian Institute for Space Research. The seasonal climate prediction uses ensemble members method to work and the main Big Data technologies used for data processing were: Python language, Apache Hadoop, Apache Hive, and the Optimized Row Columnar (ORC) file format. The main contributions of this research are the canonical model, its modules and internal components, the proposed method M-PRECLIS, and its use in a case study. After applying the model to a practical and real experiment, it was possible to analyze the results obtained and verify: the consistency of the model by the output images, the code complexity, the performance, and also to perform the comparison with related works. Thus, it was found that the proposed canonical model, based on the best practices of Big Data, is a viable alternative that can guide new paths to be followed. 

/pdf/a-canonical-model-for-seasonal-climate-prediction-using-big-2eszx0l7.pdf

A canonical model for seasonal climate prediction using Big Data

https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-022-00580-9

Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.

/pdf/an-incremental-interesting-maximal-frequent-itemset-mining-72oazvd6.pdf

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm

Social reliability and sustainable financial development in metropolises have become urgent problems to be solved. Due to its solid importance and imagination, urban cultural industry management and public economy have gradually become the focus of consideration in all fields of society. Organized and coordinated improvement of the urban economy and society and imaginative undertakings have also become the focus of review at all levels of academia and government. This article aims to study and apply the improved algorithm to the urban cultural industry management system and public economic analysis. It proposed the improved algorithm and related methods, the correlation between urban cultural industry management and economy, the improved particle swarm algorithm and the SOM algorithm, and finally, applied the improved SOM algorithm to evaluate the regional economy of a city. The experimental results showed that the contribution rate of the economic evaluation level of the eight economic evaluation indicators to the economic development level of each county (district and city) in the city was different, of which, the cumulative contribution rate of the first four main components reached 83%.

/pdf/urban-cultural-industry-management-system-and-public-1n04ze51.pdf

Urban Cultural Industry Management System and Public Economic Based on Improved Algorithm

https://www.techscience.com/cmc/v66n1/40480/pdf

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

https://www.techscience.com/cmc/v65n1/39569/pdf

An Improved Algorithm for Mining Correlation Item Pairs

Nephogram could provide important information for meteorological business, and nephogram recognition is a kind of challenge in the meteorological industry. In this paper, a selective neural network ensemble method based on K-means and Hadoop processing technology is proposed. This method combines the neural network, K-means clustering and AdaBoost in cloud environment. The experimental results show that the recognition precision of the method proposed by this paper is higher than that of traditional method in a stand-alone environment.

/pdf/an-efficient-method-for-meteorological-nephogram-recognition-32l7b2ch7x.pdf

An efficient method for meteorological nephogram recognition in cloud environment

As an important task of data mining, sequential pattern mining is also an important and challenging task in many practical applications. Traditional sequential pattern mining algorithms usually use frequent patterns as interesting sequence patterns, but frequency is not always a good proxy for interestingness This paper proposes a sequential patterns mining algorithm based on interestingness, using the branch and bound method of OPUS to traversal all possible candidate sequences. Algorithm uses bitmap as data structure to reduce storage space and experimental results on synthetic data with known patterns and experimental results confirms the validity of proposed algorithm.

Tao Li

Papers

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

An Improved Algorithm for Mining Correlation Item Pairs

An efficient method for meteorological nephogram recognition in cloud environment

Sequential Pattern Mining Algorithm Based on Interestingness