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Aljwari, Fatima Khalil. "External vs. Internal: An Essay on Machine Learning Agents for Autonomous Database Management Systems." European Journal of Computer Science and Information Technology 10, no. 5 (2022): 24–31. http://dx.doi.org/10.37745/ejcsit.2013/vol10n52431.
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There are many possible ways to configure database management systems (DBMSs) have challenging to manage and set.The problem increased in large-scale deployments with thousands or millions of individual DBMS that each have their setting requirements. Recent research has explored using machine learning-based (ML) agents to overcome this problem's automated tuning of DBMSs. These agents extract performance metrics and behavioral information from the DBMS and then train models with this data to select tuning actions that they predict will have the most benefit. This paper discusses two engineering approaches for integrating ML agents in a DBMS. The first is to build an external tuning controller that treats the DBMS as a black box. The second is to incorporate the ML agents natively in the DBMS's architecture.
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Pavlo, Andrew, Matthew Butrovich, Lin Ma, et al. "Make your database system dream of electric sheep." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3211–21. http://dx.doi.org/10.14778/3476311.3476411.
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Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these impediments by managing themselves automatically. Despite decades of DBMS auto-tuning research, a truly autonomous, self-driving DBMS is yet to come. But recent advancements in artificial intelligence and machine learning (ML) have moved this goal closer. Given this, we present a system implementation treatise towards achieving a self-driving DBMS. We first provide an overview of the NoisePage self-driving DBMS that uses ML to predict the DBMS's behavior and optimize itself without human support or guidance. The system's architecture has three main ML-based components: (1) workload forecasting, (2) behavior modeling, and (3) action planning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the complexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.
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Song, Jiansen, Wensheng Dou, Yu Gao, et al. "Detecting Metadata-Related Logic Bugs in Database Systems via Raw Database Construction." Proceedings of the VLDB Endowment 17, no. 8 (2024): 1884–97. http://dx.doi.org/10.14778/3659437.3659445.
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Database Management Systems (DBMSs) are widely used to efficiently store and retrieve data. DBMSs usually support various metadata, e.g., integrity constraints for ensuring data integrity and indexes for locating data. DBMSs can further utilize these metadata to optimize query evaluation. However, incorrect metadata-related optimizations can introduce metadata-related logic bugs, which can cause a DBMS to return an incorrect query result for a given query. In this paper, we propose a general and effective testing approach, Raw database construction (Radar), to detect metadata-related logic bugs in DBMSs. Given a database db containing some metadata, Radar first constructs a raw database rawDb , which wipes out the metadata in db and contains the same data as db. Since db and rawDb have the same data, they should return the same query result for a given query. Any inconsistency in their returned query results indicates a metadata-related logic bug. To effectively detect metadata-related logic bugs, we further propose a metadata-oriented testing optimization strategy to focus on testing previously unseen metadata, thus detecting more metadata-related logic bugs quickly. We implement and evaluate Radar on five widely-used DBMSs, and have detected 42 bugs, of which 38 have been confirmed as new bugs and 16 have been fixed by DBMS developers.
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Carvalho, Nuno, Alfranio Jr., José Pereira, Luís Rodrigues, Rui Oliveira, and Susana Guedes. "On the Use of a Reflective Architecture to Augment Database Management Systems." JUCS - Journal of Universal Computer Science 13, no. (8) (2007): 1110–35. https://doi.org/10.3217/jucs-013-08-1110.
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The Database Management System (DBMS) used to be a commodity software component, with well known standard interfaces and semantics. However, the performance and reliability expectations being placed on DBMSs have increased the demand for a variety add-ons, that augment the functionality of the database in a wide range of deployment scenarios, offering support for features such as clustering, replication, and self-management, among others. A well known software engineering approach to systems with such requirements is reflection. Unfortunately, standard reflective interfaces in DBMSs are very limited. Some of these limitations may be circumvented by implementing reflective features as a wrapper to the DBMS server. Unfortunately, these solutions comes at the expense of a large development effort and significant performance penalty. In this paper we propose a general purpose DBMS reflection architecture and interface, that supports multiple extensions while, at the same time, admitting efficient implementations. We illustrate the usefulness of our proposal with concrete examples, and evaluate its cost and performance under different implementation strategies.
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Camilleri, Carl, Joseph G. Vella, and Vitezslav Nezval. "HTAP With Reactive Streaming ETL." Journal of Cases on Information Technology 23, no. 4 (2021): 1–19. http://dx.doi.org/10.4018/jcit.20211001.oa10.
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In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.
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Ansari, Sakil Ahmad, and Jaychand Vishwakarma. "Survey on Database Concurrency Control in Multilevel Secure Database Management Systems." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 4 (2018): 105. http://dx.doi.org/10.23956/ijarcsse.v8i4.645.
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Transactions are vital for database management systems (DBMSs) because they provide transparency to concurrency and failure. Concurrent execution of transactions may lead to contention for access to data, which in a multilevel secure DBMS (MLSIDBMS) may lead to insecurity. In this paper we examine security issues involved in database concurrency control for MLS/DBMSs and show how a scheduler can affect security. We introduce Data Conflict Security; (DC-Security) a property that implies a system is free of convert channels due to contention for access to data. We present a definition of DC Security based on noninterference. Two properties that constitute a necessary condition for DC-Security are introduced along with two other simpler necessary conditions. We have identified a class of schedulers we call Output-State-Equivalent for which another criterion implies DC-Security. The criterion considers separately the behavior of the scheduler in response to those inputs that cause rollback and those that do not. We characterize the security properties of several existing scheduling protocols and find many to be insecure
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Zhong, Suyang, and Manuel Rigger. "Understanding and Reusing Test Suites Across Database Systems." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–26. https://doi.org/10.1145/3698829.
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Database Management System (DBMS) developers have implemented extensive test suites to test their DBMSs. For example, the SQLite test suites contain over 92 million lines of code. Despite these extensive efforts, test suites are not systematically reused across DBMSs, leading to wasted effort. Integration is challenging, as test suites use various test case formats and rely on unstandardized test runner features. We present a unified test suite, SQuaLity, in which we integrated test cases from three widely-used DBMSs, SQLite, PostgreSQL, and DuckDB. In addition, we present an empirical study to determine the potential of reusing these systems' test suites. Our results indicate that reusing test suites is challenging: First, test formats and test runner commands vary widely; for example, SQLite has 4 test runner commands, while MySQL has 112 commands with additional features, to, for example, execute file operations or interact with a shell. Second, while some test suites contain mostly standard-compliant statements (e.g., 99% in SQLite), other test suites mostly test non-standardized functionality (e.g., 31% of statements in the PostgreSQL test suite are nonstandardized). Third, test reuse is complicated by various explicit and implicit dependencies, such as the need to set variables and configurations, certain test cases requiring extensions not present by default, and query results depending on specific clients. Despite the above findings, we have identified 3 crashes, 3 hangs, and multiple compatibility issues across four different DBMSs by executing test suites across DBMSs, indicating the benefits of reuse. Overall, this work represents the first step towards test-case reuse in the context of DBMSs, and we hope that it will inspire follow-up work on this important topic.
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Gu, Long, Si Liu, Tiancheng Xing, Hengfeng Wei, Yuxing Chen, and David Basin. "IsoVista: Black-Box Checking Database Isolation Guarantees." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4325–28. http://dx.doi.org/10.14778/3685800.3685866.
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Transactional isolation is critical to the functional correctness of database management systems (DBMSs). Much effort has recently been devoted to finding isolation bugs and validating isolation fulfilment in production DBMSs. However, there are still challenges that existing isolation checkers have not yet fully addressed. For instance, they may overlook bugs, incur high checking overhead, and return hard-to-understand counterexamples. We present IsoVista, the first black-box isolation checking system that encompasses all the following features. It builds on faithful characterizations of a range of isolation levels, ensuring the absence of both false positives and missed bugs in collected DBMS execution histories. IsoVista exhibits superior checking efficiency, compared to the state-of-the-art, and visualizes violation scenarios, facilitating the understanding of bugs found. It also supports profiling and benchmarking the performance of isolation checkers under various workloads, assisting developers of both DBMSs and checkers. We showcase all these features through user-friendly interfaces.
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Taipalus, Toni, Hilkka Grahn, Hannu Turtiainen, and Andrei Costin. "Utilizing Vector Database Management Systems in Cyber Security." European Conference on Cyber Warfare and Security 23, no. 1 (2024): 560–65. http://dx.doi.org/10.34190/eccws.23.1.2220.
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The rising popularity of phenomena such as ubiquitous computing and IoT poses increasingly high demands for data management, and it is not uncommon that database management systems (DBMS) must be capable of reading and writing hundreds of operations per second. Vector DBMSs (VDBMS) are novel products that focus on the management of vector data and can alleviate data management pressures by storing data objects such as logs, system calls, emails, network flow data, and memory dumps in feature vectors that are computationally efficient in both storage and information retrieval. VDMBSs allow efficient nearest neighbour similarity search on complex data objects, which can be used in various cyber security applications such as anomaly, intrusion, malware detection, user behaviour analysis, and network flow analysis. This study describes VDBMSs and some of their use cases in cyber security.
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Zhang, Chi, and Manuel Rigger. "Constant Optimization Driven Database System Testing." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–24. https://doi.org/10.1145/3709674.
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Logic bugs are bugs that can cause database management systems (DBMSs) to silently produce incorrect results for given queries. Such bugs are severe, because they can easily be overlooked by both developers and users, and can cause applications that rely on the DBMSs to malfunction. In this work, we propose Constant-Optimization-Driven Database Testing (CODDTest) as a novel approach for detecting logic bugs in DBMSs. This method draws inspiration from two well-known optimizations in compilers: constant folding and constant propagation. Our key insight is that for a certain database state and query containing a predicate, we can apply constant folding on the predicate by replacing an expression in the predicate with a constant, anticipating that the results of this predicate remain unchanged; any discrepancy indicates a bug in the DBMS. We evaluated CODDTest on five mature and extensively-tested DBMSs--SQLite, MySQL, CockroachDB, DuckDB, and TiDB--and found 45 unique, previously unknown bugs in them. Out of these, 24 are unique logic bugs. Our manual analysis of the state-of-the-art approaches indicates that 11 logic bugs are detectable only by CODDTest. We believe that CODDTest is easy to implement, and can be widely adopted in practice.
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Lim, Wan Shen, Lin Ma, William Zhang, Matthew Butrovich, Samuel Arch, and Andrew Pavlo. "Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3680–93. http://dx.doi.org/10.14778/3681954.3682030.
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Autonomous database management systems (DBMSs) aim to optimize themselves automatically without human guidance. They rely on machine learning (ML) models that predict their run-time behavior to evaluate whether a candidate configuration is beneficial without the expensive execution of queries. However, the high cost of collecting the training data to build these models makes them impractical for real-world deployments. Furthermore, these models are instance-specific and thus require retraining whenever the DBMS's environment changes. State-of-the-art methods spend over 93% of their time running queries for training versus tuning. To mitigate this problem, we present the Boot framework for automatically accelerating training data collection in DBMSs. Boot utilizes macro- and micro-acceleration (MMA) techniques that modify query execution semantics with approximate run-time telemetry and skip repetitive parts of the training process. To evaluate Boot, we integrated it into a database gym for PostgreSQL. Our experimental evaluation shows that Boot reduces training collection times by up to 268× with modest degradation in model accuracy. These results also indicate that our MMA-based approach scales with dataset size and workload complexity.
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Diallo, B., J. M. Travere, and B. Mazoyer. "A Review of Database Management Systems Suitable for Neuroimaging." Methods of Information in Medicine 38, no. 02 (1999): 132–39. http://dx.doi.org/10.1055/s-0038-1634174.
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AbstractThis study comprises a technical assessment of Database Management Systems (DBMS), which may be of use in the analysis of data obtained from human brain mapping procedures. Due to the large expansion of the neuroimaging field, the use of specialized database software to store and process neuroimages and their attached components is inevitable. The advent of multiple software products, a wealth of technical terms and a wide variety of other applications make the choice of a suitable program sometimes difficult. Through the inclusion of some basic and pertinent criteria (e.g., performance, ease of opening, standardization and portability), we present a descriptive comparison of 12 DBMSs currently available in the commercial and public domain. We have compared and tested three main architecture models which are currently available and assessed their potential applications for imaging purposes: relational, object-oriented, and hybrid. The findings of our study demonstrated that the Illustra™ software was the best suited for a neuroimaging environment because of its intrinsic ability to handle complex and large objects, such as 3D volumes or geometric structures.
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Yi, Mon Win. "Object Oriented DBMSs using GIS Data." Dagon University Research Journal Vol.5, no. 2013 (2019): Pg.155–163. https://doi.org/10.5281/zenodo.3545545.
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Data management and organization have become so complex and challenging in today’s electronic age of information. In the last decade major changes have occurred in the database domain, as the result of increased interest to non-traditional database applications such as multimedia, office automation, CAD, CASE, GIS, Web databases, and others. In contrast to the relational era, the current database world is more diverse and showing contradictory tendencies. On the one hand, vendors of popular relational database management systems (DBMS), such as IBM, Informix and Oracle, extend their products with new capabilities, including support for non-traditional data types (text, graphics, sound, video, spatial data, etc.) and for object-oriented concepts, such as ADT-s, classes and inheritance. Independently, the Object Data Management Group (ODMG) proposes a standard based on a pure object model. A lot of research and development from the industry and academia is devoted to various aspects of object-relational and object-oriented DBMS. Much has been said about object orientation, and how it should change the face of information technology. But what would be the real benefits of using an object-oriented GIS? Basic concepts in object technology, such as inheritance, polymorphism and encapsulation are reviewed, and their meaning is transported to the needs of a GIS.
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Shahidinejad, Javad, Mohsen Kalantari, and Abbas Rajabifard. "3D Cadastral Database Systems—A Systematic Literature Review." ISPRS International Journal of Geo-Information 13, no. 1 (2024): 30. http://dx.doi.org/10.3390/ijgi13010030.
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Cadastral databases have been used for over 20 years, but most contain 2D data. The increasing presence of high-rise buildings with modern architecture complicates the process of determining property rights, restrictions, and responsibilities. It is, therefore, necessary to develop an efficient system for storing and managing multidimensional cadastral data. While there have been attempts to develop 3D cadastral database schemas, a comprehensive solution that meets all the requirements for effective data storage, manipulation, and retrieval has not yet been presented. This study aims to analyse the literature on 3D cadastral databases to identify approaches and technologies for storing and managing these data. Based on a systematic literature review integrated with a snowballing methodology, 108 documents were identified. During the analysis of the related documents, different parameters were extracted, including the conceptual data model, query type, and evaluation metrics, as well as the database management system (DBMS) used and technologies for visualisation, data preparation, data transformation, and the ETL (extract, transform, and load) process. The study emphasised the importance of adhering to database design principles and identified challenges associated with conceptual design, DBMS selection, logical design, and physical design. The study results provide insights for selecting the appropriate standards, technologies, and DBMSs for designing a 3D cadastral database system.
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Li, Tianyu, Matthew Butrovich, Amadou Ngom, Wan Shen Lim, Wes McKinney, and Andrew Pavlo. "Mainlining databases." Proceedings of the VLDB Endowment 14, no. 4 (2020): 534–46. http://dx.doi.org/10.14778/3436905.3436913.
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The proliferation of modern data processing tools has given rise to open-source columnar data formats. These formats help organizations avoid repeated conversion of data to a new format for each application. However, these formats are read-only, and organizations must use a heavy-weight transformation process to load data from on-line transactional processing (OLTP) systems. As a result, DBMSs often fail to take advantage of full network bandwidth when transferring data. We aim to reduce or even eliminate this overhead by developing a storage architecture for in-memory database management systems (DBMSs) that is aware of the eventual usage of its data and emits columnar storage blocks in a universal open-source format. We introduce relaxations to common analytical data formats to efficiently update records and rely on a lightweight transformation process to convert blocks to a read-optimized layout when they are cold. We also describe how to access data from third-party analytical tools with minimal serialization overhead. We implemented our storage engine based on the Apache Arrow format and integrated it into the NoisePage DBMS to evaluate our work. Our experiments show that our approach achieves comparable performance with dedicated OLTP DBMSs while enabling orders-of-magnitude faster data exports to external data science and machine learning tools than existing methods.
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Yijie Weng and Jianhao Wu. "Database management systems for artificial intelligence: Comparative analysis of postgre SQL and MongoDB." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2336–42. https://doi.org/10.30574/wjarr.2025.25.2.0586.
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The rapid evolution of artificial intelligence (AI) has amplified the need for efficient database management systems (DBMS) to handle the growing volume, variety, and velocity of data. PostgreSQL, a robust relational database, and MongoDB, a leading NoSQL solution, are two widely adopted DBMSs in AI applications, each offering unique advantages. This paper provides a comprehensive comparative analysis of PostgreSQL and MongoDB, focusing on their suitability for AI use cases. Key evaluation criteria include data modeling, query complexity, scalability, ACID compliance, indexing, and integration with AI frameworks. PostgreSQL excels in scenarios requiring strict data consistency, complex querying, and structured data, making it ideal for financial modeling, scientific research, and feature engineering. Conversely, MongoDB's schema-less design, horizontal scalability, and native support for semi-structured data align with real-time analytics, IoT, and evolving AI datasets. The study highlights that the choice between the two databases depends on specific project requirements and proposes hybrid approaches to leverage their complementary strengths. This analysis aims to guide AI practitioners in making informed database decisions to optimize performance, scalability, and flexibility in AI systems.
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Wen, Shihao, Peng Jia, Pin Yang, and Chi Hu. "Squill: Testing DBMS with Correctness Feedback and Accurate Instantiation." Applied Sciences 13, no. 4 (2023): 2519. http://dx.doi.org/10.3390/app13042519.
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Database Management Systems (DBMSs) are the core of management information systems. Thus, detecting security bugs or vulnerabilities of DBMSs is an essential task. In recent years, grey-box fuzzing has been adopted to detect DBMS bugs for its high effectiveness. However, the seed scheduling strategy of existing fuzzing techniques does not consider the seeds’ correctness, which is inefficient in finding vulnerabilities in DBMSs. Moreover, current tools cannot correctly generate SQL statements with nested structures, which limits their effectiveness. This paper proposes a fuzzing solution named Squill to address these challenges. First, we propose correctness-guided mutation to utilize the correctness of seeds as feedback to guide fuzzing. Second, Squill embeds semantics-aware instantiation to correctly fill semantics to SQL statements with nested structures by collecting the context information of AST nodes. We implemented Squill based on Squirrel and evaluated it on three popular DBMSs: MySQL, MariaDB, and OceanBase. In our experiment, Squill explored 29% more paths and found 3.4× more bugs than the existing tool. In total, Squill detected 30 bugs in MySQL, 27 in MariaDB, and 6 in OceanBase. Overall, 19 of the bugs are fixed with 9 CVEs assigned. The results show that Squill outperforms the previous fuzzer in terms of both code coverage and bug discovery.
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Falcão, Fúlvio, João Moura, Gabriel Silva, Carlos Araujo, Erica Sousa, and Eduardo Tavares. "Energy Consumption and Performance Evaluation of Multi-Model NoSQL DBMSs." Revista de Informática Teórica e Aplicada 30, no. 2 (2024): 132–40. https://doi.org/10.22456/2175-2745.136568.
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New applications have required data storage using multiple data models, which are usually known as polyglot persistence applications. Their implementation is often complex, as the system must simultaneously manage and store data in multiple database management systems (DBMS). Over the years, multi-model DBMSs have been conceived, which commonly integrate multiple NoSQL data models into a single system. To demonstrate their feasibility, some researches have evaluated multi-model NoSQL DBMSs in the context of performance, but energy consumption is usually not taken into account. Indeed, energy consumption is an issue that should not be neglected due to the respective cost and environmental sustainability. This paper presents a performance and energy consumption evaluation of multi-model and single-model NoSQL DBMSs, more specifically, ArangoDB (multi-model), OrientDB (multi-model), MongoDB (document) and Redis (key-value). The experiments are based on Yahoo! Cloud Serving Benchmark (YCSB), and results demonstrate energy consumption may vary significantly between the evaluated DBMSs for different commands (e.g., read) and workloads. The proposed evaluation contributes to the state of the art, as storage system designers have additional insights regarding the behavior of multi-model NoSQL DBMSs for distinct workloads and energy usage.
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Butrovich, Matthew, Samuel Arch, Wan Shen Lim, William Zhang, Jignesh M. Patel, and Andrew Pavlo. "BPF-DB: A Kernel-Embedded Transactional Database Management System For eBPF Applications." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725272.
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Developers rely on the eBPF framework to augment operating system (OS) behavior for the betterment of database management system (DBMS) without having to modify kernel code. But eBPF's verifier limits program complexity and data management functionality. As a result eBPF's storage options are limited to kernel-resident, non-durable data structures that lack transactional guarantees. Inspired by embedded DBMSs for user-space applications, this paper present BPF-DB, an OS-embedded DBMS that offers transactional data management for eBPF applications. We explore the storage management and concurrency control challenges associated with DBMS design in eBPF's restrictive execution environment. We demonstrate BPF-DB's capabilities with two applications based on real-world systems. The first is a Redis-compatible in-memory DBMS that uses BPF-DB as its transactional storage engine. This system matches the performance of state-of-the-art implementations while offering stronger transactional guarantees. The second application implements a stored procedure-based DBMS that provides serializable multi-statement transactions. We compare this application against VoltDB, with BPF-DB achieving 43% higher throughput. BPF-DB's robust and high-performance transactional semantics enable emerging kernel-space applications.
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Kakkar, Gaurav Tarlok, Jiashen Cao, Aubhro Sengupta, Joy Arulraj, and Hyesoon Kim. "Aero: Adaptive Query Processing of ML Queries." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725408.
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Query optimization is critical in relational database management systems (DBMSs) for ensuring efficient query processing. The query optimizer relies on precise selectivity and cost estimates to generate optimal query plans for execution. However, this static query optimization approach falls short for DBMSs handling machine learning (ML) queries. ML-centric DBMSs face distinct challenges in query optimization. First, performance bottlenecks shift to user-defined functions (UDFs), often encapsulating deep learning models, making it difficult to estimate UDF statistics without profiling the query. Second, optimal query plans for ML queries are data-dependent, requiring dynamic plan adjustments during execution. To address these challenges, we introduce Aero, an ML-centric DBMS that utilizes adaptive query processing (AQP) for efficiently processing ML queries. Aero optimizes the evaluation of UDF-based query predicates by dynamically adjusting predicate evaluation order and enhancing UDF execution scalability. By integrating AQP, Aero continuously monitors UDF statistics, routes data to predicates in an optimal order, and dynamically allocates resources for evaluating predicates. Aero achieves up to 6.4x speedup compared to a state-of-the-art ML-centric DBMS across four diverse use cases, with no impact on accuracy.
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Stonebraker, Michael, and Andrew Pavlo. "What Goes Around Comes Around... And Around..." ACM SIGMOD Record 53, no. 2 (2024): 21–37. http://dx.doi.org/10.1145/3685980.3685984.
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Two decades ago, one of us co-authored a paper commenting on the previous 40 years of data modelling research and development [188]. That paper demonstrated that the relational model (RM) and SQL are the prevailing choice for database management systems (DBMSs), despite efforts to replace either them. Instead, SQL absorbed the best ideas from these alternative approaches. We revisit this issue and argue that this same evolution has continued since 2005. Once again there have been repeated efforts to replace either SQL or the RM. But the RM continues to be the dominant data model and SQL has been extended to capture the good ideas from others. As such, we expect more of the same in the future, namely the continued evolution of SQL and relational DBMSs (RDBMSs). We also discuss DBMS implementations and argue that the major advancements have been in the RM systems, primarily driven by changing hardware characteristics.
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Győrödi, Cornelia A., Diana V. Dumşe-Burescu, Doina R. Zmaranda, Robert Ş. Győrödi, Gianina A. Gabor, and George D. Pecherle. "Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage." Applied Sciences 10, no. 23 (2020): 8524. http://dx.doi.org/10.3390/app10238524.
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In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.
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Vishwakarma, Jaychand. "Transaction Processing Environment Kernelized Architecture in Multilevel Secure Application Policies." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (2018): 87. http://dx.doi.org/10.23956/ijarcsse.v8i2.577.
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Multilevel security poses many challenging problems for transaction processing. The challenges are due to the conflicting requirements imposed by confidentiality, integrity, and availability} the three components of security. We identify these requirements on transaction processing in Multilevel Secure (MLS) database management systems (DBMSs) and survey the efforts of a number of researchers to meet these requirements .While our emphasis on centralized system based on kernelized Architecture, we briefly overview the research in the distributed MLS DBMSs as well.
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Tang, Xiu, Sai Wu, Dongxiang Zhang, Ziyue Wang, Gongsheng Yuan, and Gang Chen. "A Demonstration of DLBD: Database Logic Bug Detection System." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3914–17. http://dx.doi.org/10.14778/3611540.3611584.
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Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.
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Mohr-Daurat, Hubert, Georgios Theodorakis, and Holger Pirk. "Hardware-Efficient Data Imputation through DBMS Extensibility." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3497–510. http://dx.doi.org/10.14778/3681954.3682016.
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The separation of data and code/queries has served Data Management Systems (DBMSs) well for decades. However, while the resulting soundness and rigidity are the basis for many performance-oriented optimizations, it lacks the flexibility to efficiently support modern data science applications: data cleansing, data ingestion/augmentation or generative models. To support such applications without sacrificing performance, we propose a new logical data model called Homoiconic Collection Processing (HCP). HCP is based on a well-known Meta-Programming concept called Homoiconicity (a unified representation for code and data). In a DBMS, HCP supports the storage of "classic" relational data but also allows the storage and evaluation of code fragments we refer to as "Homoiconic Expressions". Homoiconic Expressions enable applications such as data imputation directly in the database kernel. Implemented naïvely, such flexibility would come at a prohibitive cost in terms of performance. To make HCP performance-competitive with highly-tuned in-memory DBMSs, we develop a novel storage and processing model called Shape-Wise Microbatching (SWM) and implement it in a system called BOSS. BOSS is performance-competitive with high-performance DBMSs while offering unprecedented extensibility. To demonstrate the extensibility, we implement an extension for impute-and-query workloads: BOSS outperforms state-of-the-art homoiconic runtimes and data imputation systems by two to five orders of magnitude.
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Cheung, Alvin, Maaz Bin Safeer Ahmad, Brandon Haynes, et al. "Towards Auto-Generated Data Systems." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4116–29. http://dx.doi.org/10.14778/3611540.3611635.
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After decades of progress, database management systems (DBMSs) are now the backbones of many data applications that we interact with on a daily basis. Yet, with the emergence of new data types and hardware, building and optimizing new data systems remain as difficult as the heyday of relational databases. In this paper, we summarize our work towards automating the building and optimization of data systems. Drawing from our own experience, we further argue that any automation technique must address three aspects: user specification, code generation, and result validation. We conclude by discussing a case study using videos data processing, along with opportunities for future research towards designing data systems that are automatically generated.
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Terwilliger, James, Rafael Fernández-Moctezuma, Lois M. L. Delcambre, and David Maier. "Support for Schema Evolution in Data Stream Management Systems." JUCS - Journal of Universal Computer Science 16, no. (20) (2010): 3073–101. https://doi.org/10.3217/jucs-016-20-3073.
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Unlike Database Management Systems (DBMSs), Data Stream Management Systems (DSMSs) do not evaluate queries over static data sets — rather, they continuously produce result streams to standing queries, and often operate in a context where any interruption can lead to data loss. Support for schema evolution in such an environment is currently unaddressed. In this work we address evolution in DSMSs by introducing a new element to streams, called an accent, that precedes and describes an evolution. We characterize how a subset of commonly used query operators in DSMS act on and propagate accents with respect to three evolution primitives: Add Attribute, Drop Attribute, and Alter Data.
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Arora, Pankaj, Surajit Chaudhuri, Sudipto Das, et al. "Flexible Resource Allocation for Relational Database-as-a-Service." Proceedings of the VLDB Endowment 16, no. 13 (2023): 4202–15. http://dx.doi.org/10.14778/3625054.3625058.
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Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.
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ElDahshan, Kamal A., AbdAllah A. AlHabshy, and Gaber E. Abutaleb. "Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data." PeerJ Computer Science 6 (September 10, 2020): e297. http://dx.doi.org/10.7717/peerj-cs.297.
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Background As the COVID-19 crisis endures and the virus continues to spread globally, the need for collecting epidemiological data and patient information also grows exponentially. The race against the clock to find a cure and a vaccine to the disease means researchers require storage of increasingly large and diverse types of information; for doctors following patients, recording symptoms and reactions to treatments, the need for storage flexibility is only surpassed by the necessity of storage security. The volume, variety, and variability of COVID-19 patient data requires storage in NoSQL database management systems (DBMSs). But with a multitude of existing NoSQL DBMSs, there is no straightforward way for institutions to select the most appropriate. And more importantly, they suffer from security flaws that would render them inappropriate for the storage of confidential patient data. Motivation This paper develops an innovative solution to remedy the aforementioned shortcomings. COVID-19 patients, as well as medical professionals, could be subjected to privacy-related risks, from abuse of their data to community bullying regarding their medical condition. Thus, in addition to being appropriately stored and analyzed, their data must imperatively be highly protected against misuse. Methods This paper begins by explaining the five most popular categories of NoSQL databases. It also introduces the most popular NoSQL DBMS types related to each one of them. Moreover, this paper presents a comparative study of the different types of NoSQL DBMS, according to their strengths and weaknesses. This paper then introduces an algorithm that would assist hospitals, and medical and scientific authorities to choose the most appropriate type for storing patients’ information. This paper subsequently presents a set of functions, based on web services, offering a set of endpoints that include authentication, authorization, auditing, and encryption of information. These functions are powerful and effective, making them appropriate to store all the sensitive data related to patients. Results and Contributions This paper presents an algorithm to select the most convenient NoSQL DBMS for COVID-19 patients, medical staff, and organizations data. In addition, the paper proposes innovative security solutions that eliminate the barriers to utilizing NoSQL DBMSs to store patients’ data. The proposed solutions resolve several security problems including authentication, authorization, auditing, and encryption. After implementing these security solutions, the use of NoSQL DBMSs will become a much more appropriate, safer, and affordable solution to storing and analyzing patients’ data, which would contribute greatly to the medical and research effort against COVID-19. This solution can be implemented for all types of NoSQL DBMSs; implementing it would result in highly securing patients’ data, and protecting them from any downsides related to data leakage.
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Kuznetsov, Sergey Dmitrievich, Pavel Evgenievich Velikhov, and Qiang Fu. "Real-Time Analytics, Hybrid Transactional/Analytical Processing, In-Memory Data Management, and Non-Volatile Memory." Proceedings of the Institute for System Programming of the RAS 33, no. 3 (2021): 171–98. http://dx.doi.org/10.15514/ispras-2021-33(3)-13.
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These days, real-time analytics is one of the most often used notions in the world of databases. Broadly, this term means very fast analytics over very fresh data. Usually the term comes together with other popular terms, hybrid transactional/analytical processing (HTAP) and in-memory data processing. The reason is that the simplest way to provide fresh operational data for analysis is to combine in one system both transactional and analytical processing. The most effective way to provide fast transactional and analytical processing is to store an entire database in memory. So on the one hand, these three terms are related but on the other hand, each of them has its own right to life. In this paper, we provide an overview of several in-memory data management systems that are not HTAP systems. Some of them are purely transactional, some are purely analytical, and some support real-time analytics. Then we overview nine in-memory HTAP DBMSs, some of which don't support real-time analytics. Existing real-time in-memory HTAP DBMSs have very diverse and interesting architectures although they use a number of common approaches: multiversion concurrency control, multicore parallelization, advanced query optimization, just in time compilation, etc. Additionally, we are interested whether these systems use non-volatile memory, and, if yes, in what manner. We conclude that an emergence of new generation of NVM will greatly stimulate its use in in-memory HTAP systems.
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Kahn, M. G., L. M. Fagan, and S. Tu. "Extensions to the Time-Oriented Database Model to Support Temporal Reasoning in Medical Expert Systems." Methods of Information in Medicine 30, no. 01 (1991): 04–14. http://dx.doi.org/10.1055/s-0038-1634816.
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Physicians faced with diagnostic and therapeutic decisions must reason about clinical features that change over time. Database-management systems (DBMS) can increase access to patient data, but most systems are limited in their ability to store and retrieve complex temporal information. The Time-Oriented Databank (TOD) model, the most widely used data model for medical database systems, associates a single time stamp with each observation. The proper analysis of most clinical data requires accounting for multiple concurrent clinical events that may alter the interpretation of the raw data. Most medical DBMSs cannot retrieve patient data indexed by multiple clinical events. We describe two logical extensions to TOD-based databases that solve a set of temporal reasoning problems we encountered in constructing medical expert systems. A key feature of both extensions is that stored data are partitioned into groupings, such as sequential clinical visits, clinical exacerbations, or other abstract events that have clinical decision-making relevance. The temporal network (TNET) is an object-oriented database that extends the temporal reasoning capabilities of ONCOCIN, a medical expert system that provides chemotherapy advice. TNET uses persistent objects to associate observations with intervals of time during which “an event of clinical interest” occurred. A second object-oriented system, called the extended temporal network (ETNET), is both an extension and a simplification of TNET. Like TNET, ETNET uses persistent objects to represent relevant intervals; unlike the first system, however, ETNET contains reasoning methods (rules) that can be executed when an event “begins”, and that are withdrawn when that event “concludes”. TNET and ETNET capture temporal relationships among recorded information that are not represented in TOD-based databases. Although they do not solve all temporal reasoning problems found in medical decision making, these new structures enable patient database systems to encode complex temporal relationships, to store and retrieve patient data based on multiple clinical contexts and, in ETNET, to modify the reasoning methods available to an expert system based on the onset or conclusion of specific clinical events.
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Klassen, R. K., and I. A. Kazantsev. "AUTOMATICAL PRETRANSLATION OF SQL-QUERIES TO A REGULAR PLAN IN CLUSTERIX-LIKE DBMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 196 (October 2020): 11–20. http://dx.doi.org/10.14489/vkit.2020.10.pp.011-020.
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Previously completed designs for pretranslating SQL queries did not bring significant success. Nevertheless, such a pretranslator is necessary for the possibility of using Clusterix-like database management systems (DBMSs) by a wide circle of specialists. The article proposes an original approach to pretranslating SQL queries without writing operations to the regular plan for Clusterix-like DBMSs. The possibility of implementing such a pretranslator is discussed. The concepts of a hard and a simple SQL query are given. The basis of the pretranslator is a library for parsing arbitrary grammars antlr4, which is used to build a SQL query tree. A software-implemented pretranslator uses it to build queries that meet the regular plan.
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Klassen, R. K., and I. A. Kazantsev. "AUTOMATICAL PRETRANSLATION OF SQL-QUERIES TO A REGULAR PLAN IN CLUSTERIX-LIKE DBMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 196 (October 2020): 11–20. http://dx.doi.org/10.14489/vkit.2020.10.pp.011-020.
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Previously completed designs for pretranslating SQL queries did not bring significant success. Nevertheless, such a pretranslator is necessary for the possibility of using Clusterix-like database management systems (DBMSs) by a wide circle of specialists. The article proposes an original approach to pretranslating SQL queries without writing operations to the regular plan for Clusterix-like DBMSs. The possibility of implementing such a pretranslator is discussed. The concepts of a hard and a simple SQL query are given. The basis of the pretranslator is a library for parsing arbitrary grammars antlr4, which is used to build a SQL query tree. A software-implemented pretranslator uses it to build queries that meet the regular plan.
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Никольский, Д. Р., В. Ф. Барабанов, Н. И. Гребенникова, С. А. Коваленко, and А. М. Нужный. "ANALYSIS OF GRAPH DATABASE MANAGEMENT SYSTEMS." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА 19, no. 6(-) (2023): 13–20. http://dx.doi.org/10.36622/vstu.2023.19.6.002.
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в современном информационном обществе обработка данных стала важным инструментом для принятия обоснованных решений и достижения успеха во многих областях деятельности. Для хранения данных используют разные виды систем управления базами данных (СУБД), каждая из которых обладает своими уникальными функциями, преимуществами и вариантами использования. Графовые СУБД предлагают большое количество инструментов и методов анализа и обработки данных. Был произведен обзор графовой СУБД Neo4j, мультимодальных СУБД Virtuoso и ArangoDB, с поддержкой графовой модели данных и графовой СУБД Memgraph. Каждая из СУБД обладает своим рядом особенностей и преимуществ при использовании. Были освещены основные свойства каждой из этих СУБД и приведены основные сценарии их использования. Рассмотрены преимущества и недостатки существующих предложенных на рынке СУБД и предпосылки к разработке метаграфовой СУБД. Предлагается модель данных для разрабатываемой СУБД и архитектура ее программного обеспечения, включая некоторые особенности ее реализации на уровне хранилища объектов. Разработанная модель предполагает оптимальное моделирование сложных процессов и обработку больших объемов сложных сетевых данных in the modern information society, data processing has become an important tool for making informed decisions and achieving success in many areas of activity. Different types of DBMS are used for data storage, each of which has its own unique functions, advantages and use cases. Graph DBMS offer a large number of tools and methods for data analysis and processing. This paper reviews the Neo4j graph DBMS, Virtuoso and ArangoDB multimodal DBMS with the support of the graph data model and the Memgraph graph DBMS. Each of the DBMS has its own number of special features and advantages when used. This paper highlights the main properties of each of these DBMS and provides the main scenarios for their use. The advantages and imperfections of the existing DBMS offered on the market and the prerequisites for the development of a metagraphic DBMS are considered. A data model for the DBMS being developed and its software architecture are proposed, including some features of its implementation at the object storage level. The developed model provides optimal modeling of complex processes and processing of large volumes of complex network data
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Zhang, Xinyi, Zhuo Chang, Yang Li, et al. "Facilitating database tuning with hyper-parameter optimization." Proceedings of the VLDB Endowment 15, no. 9 (2022): 1808–21. http://dx.doi.org/10.14778/3538598.3538604.
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Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a comprehensive evaluation of configuration tuning techniques from a broader perspective, hoping to better benefit the database community. In particular, we summarize three key modules of database configuration tuning systems and conduct extensive ablation studies using various challenging cases. Our evaluation demonstrates that the hyper-parameter optimization algorithms can be borrowed to further enhance the database configuration tuning. Moreover, we identify the best algorithm choices for different modules. Beyond the comprehensive evaluations, we offer an efficient and unified database configuration tuning benchmark via surrogates that reduces the evaluation cost to a minimum, allowing for extensive runs and analysis of new techniques.
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Lu, Kuan, Zhihui Yang, Sai Wu, Ruichen Xia, Dongxiang Zhang, and Gang Chen. "Adda: Towards Efficient in-Database Feature Generation via LLM-based Agents." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725262.
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Integrating machine learning (ML) analytics into existing database management systems (DBMSs) not only eliminates the need for costly data transfers to external ML platforms but also ensures compliance with regulatory standards. While some DBMSs have integrated functionalities for training and applying ML models for analytics, these tasks still present challenges, particularly due to limited support for automatic feature engineering (AutoFE), which is crucial for optimizing ML model performance. In this paper, we introduce Adda, an agent-driven in-database feature generation tool designed to automatically create high-quality features for ML analytics directly within the database. Adda interprets ML analytics tasks described in natural language and generates code for feature construction by leveraging the power of large language models (LLMs) integrated with specialized agents. This code is then translated into SQL statements using a predefined set of operators and compiled just-in-time (JIT) into user-defined functions (UDFs). The result is a seamless, fully in-database solution for feature generation, specifically tailored for ML analytics tasks. Extensive experiments across 14 public datasets, with five ML tasks per dataset, show that Adda improves the AUC by up to 33.2% and reduces end-to-end latency by up to 100x compared to Madlib.
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Krasnikov, I. L., A. K. Babichenko, and D. V. Snurnykov. "GENERAL CHARACTERISTICS AND SELECTION OF A DATABASE MANAGEMENT SYSTEM IN DISTANCE LEARNING." Integrated Technologies and Energy Saving, no. 4 (December 14, 2023): 58–66. http://dx.doi.org/10.20998/2078-5364.2023.4.06.
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The relevance of studying the discipline "Databases" for students of computer and engineering specialties is determined and their special needs and emphasis in learning are shown.
 The definition of the data model stored in the database is given. The features of the relational data model are considered, the main advantage of which is simplicity and accessibility for the user to understand and the availability of a fairly simple and at the same time powerful mathematical apparatus of set theory and relational algebra. The main educational elements of studying databases based on the relational data model are highlighted.
 The client-server architecture of modern DBMSs is described. The advantages of client-server technologies in the process of learning to work with databases are noted.
 The results of the study of the functionality of existing software products for working with databases have shown the expediency of choosing MySQL Community Edition as a training tool, which is free and in its functionality is not inferior to commercial systems by any criteria. The disadvantages of the system, which include some limitations when executing complex queries and creating analytical reports that require a high load and a large number of simultaneous queries, are not critical for training purposes. The MySQL server can be accessed both from a console client running any of the modern operating systems and from many GUI clients. The paper describes the software product MySQL Workbench Community Edition, which is offered by Oracle for free use both as part of the MySQL server and as a separate GUI client.
 As a result of the study, it was concluded that the free MySQL Community Edition DBMS together with the MySQL Workbench Community Edition GUI client fully develops the competencies of students of both engineering and computer specialties in working with databases and can be used in the study of the educational component "Databases" in the context of distance education.
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TIMAKOV, A. A. "SCENARIO OF INFORMATION FLOW ANALYSIS IMPLEMENTATION IN PL/SQL PROGRAM UNITS WITH PLIF PLATFORM." Программирование, no. 4 (July 1, 2023): 39–57. http://dx.doi.org/10.31857/s0132347423040118.
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Formal proof of security measure effectiveness and computation security is vitally important for trust in critical information systems. It should be realized that formal security verification must be carried out at each infrastructural level (from the hardware level to the application level) in the process of system design. Currently, computation security analysis on the application level remains the major challenge as it requires complex labeling of computing environment elements. Traditionally, to solve this problem, information flow control (IFC) methods are employed. Unlike access control mechanisms widely used in modern operating systems (OSs) and database management systems (DBMSs), IFC has limited application in software design and mostly comes down to trivial taint tracking. This paper describes an approach to full-fledged implementation of IFC in PL/SQL program units with the use of the PLIF platform. In addition, a general scheme of computation security analysis for enterprise applications that work with relational DBMSs is considered. The key advantage of our approach is the explicit separation of functions between software developers and security analysts.
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Zhang, William, Wan Shen Lim, Matthew Butrovich, and Andrew Pavlo. "The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3373–87. http://dx.doi.org/10.14778/3681954.3682007.
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Existing machine learning (ML) approaches to automatically optimize database management systems (DBMSs) only target a single configuration space at a time (e.g., knobs, query hints, indexes). Simultaneously tuning multiple configuration spaces is challenging due to the combined space's complexity. Previous tuning methods work around this by sequentially tuning individual spaces with a pool of tuners. However, these approaches struggle to coordinate their tuners and get stuck in local optima. This paper presents the Proto-X framework that holistically tunes multiple configuration spaces. The key idea of Proto-X is to identify similarities across multiple spaces, encode them in a high-dimensional model, and then synthesize "proto-actions" to navigate the organized space for promising configurations. We evaluate Proto-X against state-of-the-art DBMS tuning frameworks on tuning PostgreSQL for analytical and transactional workloads. By reasoning about configuration spaces that are orders of magnitude more complex than other frameworks (both in terms of quantity and variety), Proto-X discovers configurations that improve PostgreSQL's performance by up to 53% over the next best approach.
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FERREIRA, RENATO, TAHSIN KURC, MICHAEL BEYNON, CHIALIN CHANG, ALAN SUSSMAN, and JOEL SALTZ. "OBJECT-RELATIONAL QUERIES INTO MULTIDIMENSIONAL DATABASES WITH THE ACTIVE DATA REPOSITORY." Parallel Processing Letters 09, no. 02 (1999): 173–95. http://dx.doi.org/10.1142/s0129626499000190.
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As computational power and storage capacity increase, processing and analyzing large volumes of multi-dimensional datasets play an increasingly important role in many domains of scientific research. Scientific applications that make use of very large scientific datasets have several important characteristics: datasets consist of complex data and are usually multi-dimensional; applications usually retrieve a subset of all the data available in the dataset; various application-specific operations are performed on the data items retrieved. Such applications can be supported by object-relational database management systems (OR-DBMSs). In addition to providing functionality to define new complex datatypes and user-defined functions, an OR-DBMS for scientific datasets should contain runtime support that will provide optimized storage for very large datasets and an execution environment for user-defined functions involving expensive operations. In this paper we describe an infrastructure, the Active Data Repository (ADR), which provides framework for building databases that enables integration of storage, retrieval and processing of multi-dimensional datasets on a parallel machine. The system architecture of ADR provides the functionality required from runtime support for an OR-DBMS that stores and processes scientific multi-dimensional datasets. We present the system architecture of the ADR, and experimental performance results for three applications implemented using ADR.
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Matanat Hasanguliyeva, Asif Guluyev, Matanat Hasanguliyeva, Asif Guluyev. "DATABASE MANAGEMENT SYSTEMS SECURITY: RECENT THREATS AND PREVENTION METHODS." ETM - Equipment, Technologies, Materials 25, no. 01 (2024): 41–48. https://doi.org/10.36962/etm25012025-41.
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The core functionality of a Database Management System (DBMS) is to store and secure critical personal and corporate data. Number of web based applications using databases accessed through internet are increasing day by day. Preventing this environment and applications most of the time is not enough and possible, the counter measures, security threats and breaches are increasingly faced in general. In addition to the technical prevention methods, human factors and user awareness from beginners to experts must be taken into account. Extra effort should be spent to raise user awareness to a desired minimum level. This article explains the system components interacting with any DBMS directly and indirectly and presents a solution framework handling all components from a multi layered point of view. It is concluded that DBMS security can be achieved through to the presented solution framework to protect any particular DBMS from security threats and to provide DBMS security awareness. Keywords: database security, information security, protection methods of database security, recent threats.
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Markevich, D. V., V. V. Kharlanova, and A. D. Khomonenko. "INTEGRATION OF BUSINESS INTELLIGENCE SYSTEMS WITH DATABASE MANAGEMENT SYSTEMS IN TRANSPORT." H&ES Research 15, no. 2 (2023): 41–48. http://dx.doi.org/10.36724/2409-5419-2023-15-2-41-48.
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At present, the solution of various transport problems using information technologies is of great practical interest. To work with databases containing huge amounts of information, their structuring, thorough analysis and making optimal decisions, researchers and analysts actively use various systems, such as database management systems (DBMS) or business intelligence systems. Despite this, the integration of such systems, which can improve the quality of decisions, is still not widely used. Purpose: is to develop the integration technology between the business intelligence system and the DBMS, which allows you to work effectively with a huge amount of data when solving transport tasks. Methods: The integration of the systems under consideration is implemented using the PostgreSQL 11 DBMS and the Loginom Community analytical platform. Results: a full cycle of system integration has been completed, including the creation and filling of a database in the PostgreSQL 11 system, as well as its connection to the Loginom Community analytical platform for data structuring, analysis and further decision-making. Practical relevance: the main distinguishing feature of the implemented technology is the full compatibility of two systems initially responsible for different tasks: a database with an extensive ecosystem of available tools, and a platform that provides deep analytics capabilities for making optimal management decisions. The reasons and advantages of the integration of the above systems, as well as their application in solving specific transport problems, are presented. Discussion: when solving the problem of ensuring integration, the practical application of a DBMS and a business intelligence system is considered. Using PostgreSQL 11, two databases are formed, which are integrated with the Loginom Community analytical platform. It is advisable to continue further research in the areas of practical use of integration for the analytics of large database arrays, as well as in the interests of improving the quality of managerial decision-making in transport problems.
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Anjard, Ronald P. "The Basics of Database Management Systems (DBMS)." Industrial Management & Data Systems 94, no. 5 (1994): 11–15. http://dx.doi.org/10.1108/02635579410063261.
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Nguyen, Lam-Duy, Adnan Alhomssi, Tobias Ziegler, and Viktor Leis. "Moving on From Group Commit: Autonomous Commit Enables High Throughput and Low Latency on NVMe SSDs." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–24. https://doi.org/10.1145/3725328.
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Achieving both high throughput and low commit latency has long been a difficult challenge for Database Management Systems (DBMSs). As we show in this paper, existing commit processing protocols fail to fully leverage modern NVMe SSDs to deliver both high throughput and low-latency durable commits. We therefore propose autonomous commit , the first commit protocol that fully utilizes modern NVMe SSDs to achieve both objectives. Our approach exploits the high parallelism and low write latency of SSDs, enabling workers to explicitly write logs in smaller batches, thereby minimizing the impact of logging I/O on commit latency. Additionally, by parallelizing the acknowledgment procedure, where the DBMS iterates through a set of transactions to inspect their commit state, we mitigate excessive delays resulting from single-threaded commit operations in high-throughput workloads. Our experimental results show that autonomous commit achieves exceptional scalability and low-latency durable commits across a wide range of workloads.
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Celesti, Antonio, Maria Fazio, and Massimo Villari. "A Study on Join Operations in MongoDB Preserving Collections Data Models for Future Internet Applications." Future Internet 11, no. 4 (2019): 83. http://dx.doi.org/10.3390/fi11040083.
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Presently, we are observing an explosion of data that need to be stored and processed over the Internet, and characterized by large volume, velocity and variety. For this reason, software developers have begun to look at NoSQL solutions for data storage. However, operations that are trivial in traditional Relational DataBase Management Systems (DBMSs) can become very complex in NoSQL DBMSs. This is the case of the join operation to establish a connection between two or more DB structures, whose construct is not explicitly available in many NoSQL databases. As a consequence, the data model has to be changed or a set of operations have to be performed to address particular queries on data. Thus, open questions are: how do NoSQL solutions work when they have to perform join operations on data that are not natively supported? What is the quality of NoSQL solutions in such cases? In this paper, we deal with such issues specifically considering one of the major NoSQL document oriented DB available on the market: MongoDB. In particular, we discuss an approach to perform join operations at application layer in MongoDB that allows us to preserve data models. We analyse performance of the proposes approach discussing the introduced overhead in comparison with SQL-like DBs.
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Wang, Qichen, Qiyao Luo, and Yilei Wang. "Relational Algorithms for Top-k Query Evaluation." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–27. http://dx.doi.org/10.1145/3654971.
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The evaluation of top-k conjunctive queries, a staple in business analysis, often requires evaluating the conjunctive query prior to filtering the top-k results, leading to a significant computational overhead within Database Management Systems (DBMSs). While efficient algorithms have been proposed, their integration into DBMSs remains arduous. We introduce relational algorithms, a paradigm where each algorithmic step is expressed by a relational operator. This allows the algorithm to be represented as a set of SQL queries, enabling easy deployment across different systems that support SQL. We introduce two novel relational algorithms, level-k and product-k, specifically designed for evaluating top-k conjunctive queries and demonstrate that level-k achieves optimal running time for top-k free-connex queries. Furthermore, these algorithms enable easy translation into an oblivious algorithm for secure query evaluations. The presented algorithms are not only theoretically optimal but also exhibit eminent efficiency in practice. The experiment results show significant improvements, with our rewritten SQL outperforming the baseline by up to 6 orders of magnitude. Moreover, our secure implementations not only achieve substantial speedup compared to the baseline with secure guarantees but even surpass those baselines that have no secure guarantees.
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Lee, Kitaek, Insoon Jo, Jaechan Ahn, et al. "Deploying Computational Storage for HTAP DBMSs Takes More Than Just Computation Offloading." Proceedings of the VLDB Endowment 16, no. 6 (2023): 1480–93. http://dx.doi.org/10.14778/3583140.3583161.
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Hybrid transactional/analytical processing (HTAP) would overload database systems. To alleviate performance interference between transactions and analytics, recent research pursues the potential of in-storage processing (ISP) using commodity computational storage devices (CSDs). However, in-storage query processing faces technical challenges in HTAP environments. Continuously updated data versions pose two hurdles: (1) data items keep changing, and (2) finding visible data versions incurs excessive data access in CSDs. Such access patterns dominate the cost of query processing, which may hinder the active deployment of CSDs. This paper addresses the core issues by proposing an a nalyt i c offloa d e ngine (AIDE) that transforms engine-specific query execution logic into vendor-neutral computation through a canonical interface. At the core of AIDE are the canonical representation of vendor-specific data and the separate management of data locators. It enables any CSD to execute vendor-neutral operations on canonical tuples with separate indexes, regardless of host databases. To eliminate excessive data access, we prescreen the indexes before offloading; thus, host-side prescreening can obviate the need for running costly version searching in CSDs and boost analytics. We implemented our prototype for PostgreSQL and MyRocks, demonstrating that AIDE supports efficient ISP for two databases using the same FPGA logic. Evaluation results show that AIDE improves query latency up to 42× on PostgreSQL and 34× on MyRocks.
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Sibaram, Prasad Panda. "Comparative Analysis of Azure Cosmos DB vs. Traditional RDBMS on Cloud." International Journal of Research in Engineering & Science ISSN:(P) 2572-4274 (O) 2572-4304 9, no. 2 (2025): 8–34. https://doi.org/10.5281/zenodo.15481723.
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Big Data is one of the most significant emerging technologies that allow organizations to capture, store, analyze, and visualize data, thus enabling them to extract useful information for better decision-making. To manage Big Data, distributed databases are an inevitable and inseparable part of distributed systems. The increasing amount of data produced by different sources calls for constrained resources to address the problem of data deluge. Therefore, Cloud computing provides advantages not only in terms of Lower Total Cost of Ownership (TCO), but also in terms of massive storage, computing resources, scalability, on-demand access and maintenance-free systems. The increasing amount of data produced in different fields of life has brought difficulties to data management. Growing interest in data management has led to the study of performance and scalability of DBMSs on Clouds.
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Zhu, Yuxuan, Tengjun Jin, Stefanos Baziotis, Chengsong Zhang, Charith Mendis, and Daniel Kang. "PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3725335.
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After decades of research in approximate query processing (AQP), its adoption in the industry remains limited. Existing methods struggle to simultaneously provide user-specified error guarantees, eliminate maintenance overheads, and avoid modifications to database management systems. To address these challenges, we introduce two novel techniques, TAQA and BSAP. TAQA is a two-stage online AQP algorithm that achieves all three properties for arbitrary queries. However, it can be slower than exact queries if we use standard row-level sampling. BSAP resolves this by enabling block-level sampling with statistical guarantees in TAQA. We implement TAQA and BSAP in a prototype middleware system, PilotDB, that is compatible with all DBMSs supporting efficient block-level sampling. We evaluate PilotDB on PostgreSQL, SQL Server, and DuckDB over real-world benchmarks, demonstrating up to 126X speedups when running with a 5% guaranteed error.
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Qi, Kaiyang, Jiong Yu, and Zhenzhen He. "A Cardinality Estimator in Complex Database Systems Based on TreeLSTM." Sensors 23, no. 17 (2023): 7364. http://dx.doi.org/10.3390/s23177364.
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Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates. However, the learning-based cardinality estimation methods still have large errors when an SQL query involves multiple tables or is very complex. To address this problem, we propose a sampling-based tree long short-term memory (TreeLSTM) neural network to model queries. The proposed model addresses the weakness of traditional methods when no sampled tuples match the predicates and considers the join relationship between multiple tables and the conjunction and disjunction operations between predicates. We construct subexpressions as trees using operator types between predicates and improve the performance and accuracy of cardinality estimation by capturing the join-crossing correlations between tables and the order dependencies between predicates. In addition, we construct a new loss function to overcome the drawback that Q-error cannot distinguish between large and small cardinalities. Extensive experimental results from real-world datasets show that our proposed model improves the estimation quality and outperforms traditional cardinality estimation methods and the other compared deep learning methods in three evaluation metrics: Q-error, MAE, and SMAPE.