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1. DBCompliant: Extending Database Management Systems to Support Compliance Functionality

   Rasin, Alexander; Scope, Nick; Lenard, Ben; Reyad, Moaz; Wagner, James (July 2024, The 29th International Conference on Database Systems for Advanced Applications (DASFAA))

   Data privacy policy requirements are a quickly evolving part of the data management domain. Healthcare (e.g., HIPAA), financial (e.g., GLBA), and general laws such as GDPR or CCPA impose controls on how personal data should be managed. Relational databases do not offer built-in features to support data management features to comply with such laws. As a result, many organizations implement ad-hoc solutions or use third party tools to ensure compliance with privacy policies. However, external compliance framework can conflict with the internal activity in a database (e.g., trigger side-effects or aborted transactions). In our prior work, we introduced a framework that integrates data retention and data purging compliance into the database itself, requiring only the support for triggers and encryption, which are already available in any mainstream database engine. In this demonstration paper, we introduce DBCompliant – a tool that demonstrates how our approach can seamlessly integrate comprehensive policy compliance (defined via SQL queries). Although we use PostgreSQL as our back-end, DBCompliant could be adapted to any other relational database. Finally, our approach imposes low (less than 5%) user query overhead. 

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2. A Quantitative Analysis of Student Solutions to Graph Database Problems

   https://doi.org/10.1145/3430665.3456314  

   Chen, Mei; Poulsen, Seth; Alkhabaz, Ridha; Alawini, Abdussalam (June 2021, Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)

   null (Ed.)

   As data grow both in size and in connectivity, the interest to use graph databases in the industry has been proliferating. However, there has been little research on graph database education. In response to the need to introduce college students to graph databases, this paper is the first to analyze students' errors in homework submissions of queries written in Cypher, the query language for Neo4j---the most prominent graph database. Based on 40,093 student submissions from homework assignments in an upper-level computer science database course at one university, this paper provides a quantitative analysis of students' learning when solving graph database problems. The data shows that students struggle the most to correctly use Cypher's WITH clause to define variable names before referencing in the WHERE clause and these errors persist over multiple homework problems requiring the same techniques, and we suggest a further improvement on the classification of syntactic errors. 

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3. Learning database security with hands-on mobile labs

   https://doi.org/10.1109/FIE.2017.8190716  

   Qian, Kai; Lo, Dan; Shahriar, Hossain; Li, Lei; Wu, Fan; Bhattacharya, Prabir (December 2017, 2017 IEEE Frontiers in Education Conference (FIE))

   As mobile computing is becoming more and more popular, the security threats to mobile applications are simultaneously increasing explosively. Most malicious activities hack the user’s private information, such as contact and location information, hijack the user’s transactions and communications, and exploit the confidential enterprise data stored in mobile databases or in cache on mobile devices. Database security is one of the most important security areas to be addressed. Many schools are integrating database security topics into database and cybersecurity education. This paper addresses the needs for pedagogical learning materials for database security education and the challenges of building database security capacity through effective, engaging, and investigative learning approaches, through transferrable and integratable mobile-based learning modules with hands-on companion labs based on the OWASP recommendations, such as input validation, data encryption, data sharing, auditing, and others. The primary goal of this learning approach is to create a motivating learning environment that encourages and engages all students in database security concepts and practices learning. The preliminary feedback from students was positive. Students gained hands-on real world learning experiences on Mobile Database Security (MDS) with Android mobile devices, which also greatly promoted students’ self-efficacy and confidence in their mobile security learning. 

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4. Demonstration of Chestnut: An In-memory Data Layout Designer for Database Applications

   https://doi.org/10.1145/3318464.3384712  

   Mingwei Samuel, Cong Yan (June 2020, ACM SIGMOD Conference)

   null (Ed.)

   This demonstration showcases Chestnut, a data layout generator for in-memory object-oriented database applications. Given an application and a memory budget, Chestnut generates a customized in-memory data layout and the corresponding query plans that are specialized for the application queries. Our demo will let users design and improve simple web applications using Chestnut. Users can view the Chestnut-generated data layouts using a custom visualization system, which will allow users to see how the application parameters affect Chestnut's design. Finally, users will be able to run queries generated by the application via the customized query plans generated by Chestnut or traditional relational query engines, and can compare the results and observe the speedup achieved by the Chestnut-generated query plans. 

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5. ColumnBurst: a near-storage accelerator for memory-efficient database join queries

   https://doi.org/10.1145/3409963.3410494  

   Sun, Gongjin; Jun, Sang-Woo (August 2020, roceedings of the 11th ACM SIGOPS Asia-Pacific Workshop on Systems)

   We present ColumnBurst, a memory-efficient, near-storage hardware accelerator for database join queries. While the paradigm of near-storage computation has demonstrated performance and efficiency benefits on many workloads by reducing data movement overhead, memory-bound operations such as relational joins on unsorted data have been relatively inefficient with fast modern storage devices, due to the limited capacity and performance of memory available on the near-storage processing engine. ColumnBurst delivers very high performance even on such complex queries, while staying within the memory performance and capacity budget of what is typically already available on off-the-shelf storage devices. ColumnBurst achieves this via a compact, hardware implementation of sorting-based group-by aggregation and join algorithms, instead of the conventional hash-based algorithms. We evaluate ColumnBurst using an FPGA-based prototype with 1 GB of slow on-device DDR3 DRAM, and show that on benchmarks including TPC-H queries with join queries on unsorted columns, it outperforms MonetDB on a 6-core i7 with 32 GB of DRAM by over 7x, and ColumnBurst using a near-storage hash join algorithm by 2x. 

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