In the digital education application domain, developers of platforms such as online classrooms face the challenges of privacy issues and existing datasets’ insufficient size in their pursuit of data-driven optimization. To address this, a set of heterogeneous data models adapted to the characteristics of education were constructed, and corresponding data generation tools (E-Tools) that can be used to simulate data interactions in complex educational scenarios were implemented. Experimental results have shown that the tool can maintain an efficient data generation speed (64–74 $ {\rm{MB}}\cdot {{\rm{s}}^{-1}} $) under a variety of data sizes, demonstrating good linear scaling ability, which validates the model’s effectiveness and the tool’s ability to generate larger data volumes. A heterogeneous data query load reflecting students’ learning behaviors was also designed to provide strong support for performance evaluation and the education platform’s optimization.
Conventional education big data management is faced with security risks such as privacy data leakage, questionable data credibility, and unauthorized access. To avoid the above risks, a novel type of education big data security management and privacy protection method, Algorithm for security management and privacy protection of education big data based on smart contracts (ASPES), is proposed. It integrates an improved key splitting and sharing algorithm based on the secret sharing of Shamir, a hybrid encryption algorithm based on SM2-SHA256-AES, and a smart contract management algorithm based on hierarchical data access control. Experiments are conduced on the real dataset of MOOCCube and the results indicate that the execution efficiency and security of ASPES are significantly improved when compared with the state-of-the-art methods, which can effectively store and manage education big data and realize the reasonable distribution of educational resources. By embedding smart contracts into the blockchain and inputting operations like data reading and writing into the blockchain, ASPES can optimize the management path, improve management efficiency, ensure the fairness of education, and considerably improve the quality of education.
Query optimization can significantly enhance the analysis efficiency of online analytical processing (OLAP) database systems for massive educational data, providing fast and accurate data support for intelligent educational systems. The optimizer mainly consists of three modules: cardinality estimation, space enumeration, and cost models. Specifically, cardinality estimation determines the results of the cost model and guides the selection of query plans. Therefore, the evaluation of the cardinality estimation module of the optimizer plays a crucial role in the optimization of OLAP database systems. This study designs and implements an effective workload generation tool based on primary key-driven diversified data distribution and data relationship construction. The tool includes data generation technology with custom relationships, workload template generation technology based on finite state machines, and parameter instantiation technology driven by target cardinality. Experiments were conducted on three databases: OceanBase, TiDB, and PostgreSQL, analyzing the issues of their optimizers and providing suggestions.
In the modern educational environment, efficient and reliable data management systems are essential for the operation of online education platforms and student information management systems. With the continuous growth of educational data and the increase in the frequency of multi-user access, database systems face the challenge of high throughput requirements owing to concurrent conflict operations. Among the many concurrency control strategies, the lock-based control strategy is commonly used in database systems. However, the blocking caused by locks affects the performance of concurrent execution of transactions in the database. Existing work mainly reduces lock contention by scheduling the execution order between transactions or optimizing stored procedures. To improve transaction throughput further, this study conducts blocking analysis and cost modeling within transactions based on lock avoidance, and proposes an intra-transaction scheduling strategy. The scheduling cost is estimated by analyzing the blocking of the workload, and then the operation order is exchanged to a limited extent within the transaction according to certain rules to reduce the delay caused by lock blocking, thereby improving performance. Finally, comparing the conventional and proposed scheduling strategies, the latter is verified to improve throughput and reduce the average transaction delay.
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