As a new type of production factor, data elements are becoming key to enterprise development. As an important component of national economic production, the chemical material industry must upgrade their data information system according to the needs of its construction. In this regard, a tailored data governance module is proposed for managing experimental formula data in the chemical material industry. The data governance module proposes the use of corresponding data standards and specifications according to the current business scenario of the enterprise. The system obtains data from the front end, improves its quality, stores it in a database, evaluates the data from the back end, and finally returns it to the front end for display, thereby creating a closed-loop negative feedback system.
The material research industry generates a wide variety of data from a wide range of sources. Notably, the data islanding issue pertaining to this scenario demands an application platform that integrates data collection, management, and application so that the value of the data can be better utilized for development. For this purpose, this study proposes a data management system that handles the entire data management lifecycle from collection to application for the material research industry, including thematic database, formula performance prediction, and formula correlation analysis capabilities. Itis expected that this development will promote data-driven innovations and improvements within the industry.
A hybrid transaction analytical processing (HTAP) system must concurrently support both transaction processing and query analysis. To eliminate interference between them, HTAP systems also typically assign different copies of data to both workloads, handling online transaction processing (OLTP) and online analytical processing (OLAP) requests separately, and synchronizing data between the copies based on a log replay. An HTAP system is committed to efficiently synchronizing OLTP data to OLAP, thereby providing a fresher data access service. In addition, the speed of sending and replaying the logs of the tables to be queried is a key factor affecting the freshness of the data. In this paper, using the table grouping based log parallel replay method and the characteristics of the HTAP load, a log sending and replay method is proposed based on the query frequency of the OLAP side. To ensure data consistency, this method improves the processing priority of high-frequency query table logs and achieves efficient log sending and replay capabilities along with a targeted priority display of high-frequency query table data, thereby ensuring the freshness of the HTAP system.
This study investigates the application of consortium blockchain technology to steel inspection certificates to facilitate the issuing of trustworthy, accountable, transferable, divisible, and electronic certificates for industrial internet users. With the help of blockchain technology, steel plants provided authentic certificate data to Ouyeel. They authorized Ouyeel to produce and issue electronic inspection certificates based on the trustworthy certificate data and online business transactions for internet users. The smart contracts were developed for quality data recording, ownership transferring, certificate history, and so on. The blockchain-based steel inspection certificates were tamper-proof and could be verified by scanning the 2D barcode on the certificates. Thus, the costs of printing, mailing, and archiving for manufacturers, distributors, and end users are significantly reduced. The experiments prove the high efficiency and availability of the system through sufficient function and performance tests.
Join order selection, i.e., the determination of the cheapest join order from available alternatives, is one of the most critical tasks in query optimization. The enormous search space of a join order makes it difficult to find an optimal join order in an efficient manner. Although there are many optimization algorithms for join order selection, existing benchmarks are unsuitable for evaluating these join order selection strategies because they cannot configure the depths of the joins or cover all join styles. To effectively evaluate the quality of join order selection algorithms used in an optimizer, a generic evaluation tool for join order selection is implemented in this study. The tool takes the primary key-based deterministic data generation method for portable application scenario migration, a join order sampling algorithm to reduce the investigated join spaces, and a result-guided parameter instantiation algorithm to support a valid query generation. We applied the tool on OceanBase and PostgreSQL, and the experiment results show its effectiveness in evaluating the performance of join order selection in query optimizers in a generic and efficient manner.
As the internet drives toward “digital transformation”, education equity and data trust-worthiness pose significant challenges in development. Blockchain, as a distributed ledger technology with tamperproof data, is jointly maintained by multiple parties and can solve equity and trustworthiness issues in scenarios such as educational resource allocation, intellectual property rights, and student information authentication. Although blockchain is capable of addressing the core education problems, its data immutability and transparency properties limit the upgradation process of smart contracts and disclosure of sensitive data in blockchain applications. Hence, updating educational applications and creating low privacy security of educational data becomes strenuous. To address the problem of limited smart contract upgrades, this study proposes an efficient and fully decoupled blockchain smart contract architecture. The as-proposed architecture aids in decoupling the contracts into proxy logical contracts, proxy data contracts, logical contracts, and data contracts, achieving an average reduction of 28.2% in upgradation costs compared with traditional methods. Moreover, we combined on- and off-chain collaboration to optimize transactions under the decoupled contract architecture and reduce data migration while updating contracts by integrating the underlying blockchain storage tree, optimized to reduce latency by half. To solve the problem of privacy protection, we propose a privacy data protection scheme based on permission management and LDP (Local Differential Privacy) to improve data privacy security while reducing the negative impact on blockchain performance. Finally, these solutions were integrated and implemented into an educational platform comprising a trusted knowledge exchange community and student growth system.