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Journal articles on the topic 'Scalable Databases'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Shekhar Mishra. "Building Scalable Cloud Databases with Database Reliability Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1322–33. https://doi.org/10.32628/cseit251112125.

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This comprehensive article explores the evolution and implementation of Database Reliability Engineering (DBRE) in cloud environments, focusing on the transformation from traditional database management to modern cloud-based solutions. The article examines key aspects of scalable database architectures, including elastic scalability, serverless solutions, and advanced scaling techniques. The article investigates various strategies for ensuring database reliability, performance optimization, and cost management while addressing challenges in data distribution and consistency maintenance. Throug

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Bhatewara, Ankita, and Kalyani Waghmare. "Highly Scalable Network Management Solution Using Cassandra." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5085–89. http://dx.doi.org/10.24297/ijct.v13i10.2330.

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With the current emphasis on Big Data, NOSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. The traditional database is designed for the structured data and the complex query. In the environment of the cloud, the scale of data is very large, the data is non-structured, the request of the data is dynamic, these characteristics raise new challenges for the data storage and administration, in this context, the NOSQL database comes into picture. This paper discusses about some non-structured databases. It also shows how Cassandra is used to i

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Sethu, Sesha Synam Neeli. "Serverless Databases: A Cost-Effective and Scalable Solution." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 6 (2019): 1–7. https://doi.org/10.5281/zenodo.15362290.

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Traditional database management systems (DBMS) require significant operational overhead, involving complex infrastructure provisioning, configuration, and ongoing maintenance. This approach presents challenges for modern applications demanding scalability, agility, and cost-efficiency. Serverless databases address these limitations by abstracting away the underlying infrastructure management, providing a fully managed service.This paper examines the key advantages of serverless databases, including their auto-scaling capabilities (dynamic resource allocation based on workload demands), reduced

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Bouguettaya, Athman, Zaki Malik, Abdelmounaam Rezgui, and Lori Korff. "A Scalable Middleware for Web Databases." Journal of Database Management 17, no. 4 (2006): 20–46. http://dx.doi.org/10.4018/jdm.2006100102.

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Pang, HweeHwa, Jilian Zhang, and Kyriakos Mouratidis. "Scalable verification for outsourced dynamic databases." Proceedings of the VLDB Endowment 2, no. 1 (2009): 802–13. http://dx.doi.org/10.14778/1687627.1687718.

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Hambardzumyan, Sasun. "Sharding Strategies for Scalable Vector Databases." International Journal of Latest Research in Engineering and Technology (IJLRET) 11, no. 6 (2025): 43–47. https://doi.org/10.56581/ijlret.11.06.43-47.

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Jain, Amit Kumar. "Architecting Scalable and Distributed Cloud Database Systems." International Journal of Technology, Management and Humanities 11, no. 02 (2025): 1–9. https://doi.org/10.21590/ijtmh.11.02.04.

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The rapid growth of data-intensive applications has led to an increasing demand for scalable, distributed cloud-based databases capable of ensuring high availability, fault tolerance, and efficient data management. Scalable architectures in this domain are essential for meeting diverse workload requirements while maintaining optimal performance and costefficiency. This paper explores various architectural designs and techniques employed to achieve scalability in cloud-based databases, including horizontal scaling, sharding, and replication. Emphasis is placed on the balance between consistency

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Qin, Dai, Angela Demke Brown, and Ashvin Goel. "Scalable replay-based replication for fast databases." Proceedings of the VLDB Endowment 10, no. 13 (2017): 2025–36. http://dx.doi.org/10.14778/3151106.3151107.

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Bernecker, Thomas, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, and Andreas Zuefle. "Scalable Probabilistic Similarity Ranking in Uncertain Databases." IEEE Transactions on Knowledge and Data Engineering 22, no. 9 (2010): 1234–46. http://dx.doi.org/10.1109/tkde.2010.78.

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Kumar, Tilak, Surya Surya, and Kushagra Deep. "NoSQL Database Integration with Scalable Machine Learning Pipelines." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40362.

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The increasing requirement to process large amounts of diverse and high-velocity data has made the integration of NoSQL databases with scalable machine learning (ML) pipelines an important research topic. With a focus on scalability, flexibility, and real-time processing, this article examines the architectural and technical obstacles to a smooth integration between NoSQL databases and ML workflows. We look into the suitability of different NoSQL database models (such as document-based, columnar, and graph-based) for handling and storing the variety of data types needed for machine learning tr

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Vipul, Kumar Bondugula. "MANAGING CONFLICT RATE REDUCTION IN SCALABLE DISTRIBUTED DATABASE SYSTEMS." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 1 (2023): 1–23. https://doi.org/10.5281/zenodo.15356925.

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Databases are foundational components in modern computing systems, used to store, manage, and retrieve structured data efficiently. As data volumes and access requirements grow, traditional centralized databases often struggle to meet performance, availability, and fault tolerance demands. To address these limitations, distributed databases have emerged as a scalable solution, spreading data across multiple nodes or geographic locations. This architecture improves system resilience and enables faster access to data by colocating it closer to users. However, distributed databases also introduce

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Kazheen, S. . Muhammad, and Maseeh Yasin Hajar. "Scalable Database Solutions in the Cloud Era: Challenges and Best Practices." Engineering and Technology Journal 10, no. 05 (2025): 5192–204. https://doi.org/10.5281/zenodo.15532508.

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Cloud computing has revolutionized data management, creating demand for highly scalable and adaptive database systems. Traditional architecture has given way to cloud-native databases that offer elasticity, modularity, and real-time responsiveness. This paper reviews modern approaches to building scalable cloud databases, highlighting critical challenges and emerging solutions. Key advancements include microservices-based architecture and intelligent tuning systems like CDBTune and HUNTER, which use AI to optimize performance under dynamic workloads. Security is addressed through homomorphic e

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Dheerendra, Yaganti. "Scalable Data Management: A Comparative Study of SQL, NewSQL, NoSQL with .NET Framework." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 114–18. https://doi.org/10.5281/zenodo.15240969.

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Relational database management systems (RDBMS), historically dominant for data management, are increasingly challenged by newer database paradigms such as NewSQL and NoSQL. With growing data volumes and performance demands, especially in web applications, developers frequently seek databases offering scalability, flexibility, and performance. This study presents a comparative overview of traditional SQL databases, NewSQL, and NoSQL systems, highlighting their suitability based on specific application scenarios. Furthermore, it discusses integrating these database systems with the .NET framewor

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Nagaraju Thallapally. "Unlocking peak performance: Advanced techniques for optimizing database efficiency." International Journal of Science and Research Archive 3, no. 1 (2021): 209–14. https://doi.org/10.30574/ijsra.2021.3.1.0081.

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As the data is growing at a blazing rate, database performance has never been more important in achieving performant, scalable, and stable systems. We need to optimize for big data, speed up queries, and keep things afloat. The purpose of this article is to review different indexing, query optimization, database design, caching, and hardware-optimization techniques and techniques for performance-enhancing databases. There’s also discussion about database types (relational, NoSQL, in-memory databases) and the effect they have on performance as well as new technologies such as machine learning t

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Wick, Michael, Andrew McCallum, and Gerome Miklau. "Scalable probabilistic databases with factor graphs and MCMC." Proceedings of the VLDB Endowment 3, no. 1-2 (2010): 794–804. http://dx.doi.org/10.14778/1920841.1920942.

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Xu, Yanwei. "Scalable top-k keyword search in relational databases." Cluster Computing 22, S1 (2017): 731–47. http://dx.doi.org/10.1007/s10586-017-1232-6.

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Choudhury, Farhana Murtaza, Mohammed Eunus Ali, Sarah Masud, Suman Nath, and Ishat E. Rabban. "Scalable visibility color map construction in spatial databases." Information Systems 42 (June 2014): 89–106. http://dx.doi.org/10.1016/j.is.2013.12.002.

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Lo, Yu-Lung, Chu-Hui Lee, and Chun-Hsiung Wang. "Scalable multi-feature index structure for music databases." Information Sciences 179, no. 15 (2009): 2662–75. http://dx.doi.org/10.1016/j.ins.2009.03.019.

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Priyanka, Gowda Ashwath Narayana Gowda. "SQL vs. NoSQL Databases: Choosing the Right Option for FinTech." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 100–104. https://doi.org/10.5281/zenodo.13950855.

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The paper discusses the critical decision-making in choosing between SQL and NoSQL databases for FinTech applications. FinTech, founded on large-scale data processing, transactional integrity, and real-time analytics, warrants robust and highly scalable database solutions. SQL databases are very suitable for applications such as payment processing, customer relationship management, and core banking systems because of their strong consistency, reliability, and mature ecosystem. On the other hand, NoSQL databases offer flexibility in handling unstructured data, horizontal scalability, and high a

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Neiheiser, Ray, Roland Schmitz, Luciana Rech, and Manfredo Manfredini. "Efficient Fault-Tolerant Transactions for Distributed Graph Database." Singular Engenharia, Tecnologia e Gestão 1, no. 2 (2019): 14–20. http://dx.doi.org/10.33911/singular-etg.v1i2.59.

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Through the ongoing trend in graph technologies due to the massive growth of linked data produced by social networks graph databases gained popularity. Replication, a common approach to increase availability in databases, is also used by diverse graph database solutions. Few approaches implementing fault-tolerance in graph databases have been proposed yet.This paper considers deferred update replication using atomic broadcast in order to implement fault-tolerance in distributed graph databases. The main contribution of this paper is a deferred update algorithm adapted to graph databases offeri

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Behjat, U. Nisa. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 845–48. https://doi.org/10.31142/ijtsrd11214.

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Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured non relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa "A Comparison between Relational Databases and NoSQL Databases" Published in International

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Mo, Dingheng, Junfeng Liu, Fan Wang, and Siqiang Luo. "Aster: Enhancing LSM-structures for Scalable Graph Database." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–26. https://doi.org/10.1145/3709662.

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There is a proliferation of applications requiring the management of large-scale, evolving graphs under workloads with intensive graph updates and lookups. Driven by this challenge, we introduce Poly-LSM , a high-performance key-value storage engine for graphs with the following novel techniques: (1) Poly-LSM is embedded with a new design of graph-oriented LSM-tree structure that features a hybrid storage model for concisely and effectively storing graph data. (2) Poly-LSM utilizes an adaptive mechanism to handle edge insertions and deletions on graphs with optimized I/O efficiency. (3) Poly-L

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23

Kaur, Pankaj Deep, and Gitanjali Sharma. "Architectures for Scalable Databases in Cloud – And Application Specifications." Procedia Computer Science 58 (2015): 622–34. http://dx.doi.org/10.1016/j.procs.2015.08.081.

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Morand, Cl, J. Benois-Pineau, J. Ph Domenger, J. Zepeda, E. Kijak, and Ch Guillemot. "Scalable object-based video retrieval in HD video databases." Signal Processing: Image Communication 25, no. 6 (2010): 450–65. http://dx.doi.org/10.1016/j.image.2010.04.004.

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Xu, Yanwei, Jihong Guan, Fengrong Li, and Shuigeng Zhou. "Scalable continual top-k keyword search in relational databases." Data & Knowledge Engineering 86 (July 2013): 206–23. http://dx.doi.org/10.1016/j.datak.2013.03.004.

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Gupta, Sonali, and Komal Kumar Bhatia. "Design of a Parallel and Scalable Crawler for the Hidden Web." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijirr.289612.

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The WWW contains huge amount of information from different areas. This information may be present virtually in the form of web pages, media, articles (research journals / magazine), blogs etc. A major portion of the information is present in web databases that can be retrieved by raising queries at the interface offered by the specific database and is thus called the Hidden Web. An important issue is to efficiently retrieve and provide access to this enormous amount of information through crawling. In this paper, we present the architecture of a parallel crawler for the Hidden Web that avoids

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Singhal, Harshul, Arpit Saxena, Nitesh Mittal, Chetna Dabas, and Parmeet Kaur. "PolyGlot Persistence for Microservices-Based Applications." International Journal of Information Technologies and Systems Approach 14, no. 1 (2021): 17–32. http://dx.doi.org/10.4018/ijitsa.2021010102.

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Traditionally, applications have used a single database to fulfill their storage requirements. However, limiting storage to a specific type of database system may result in a compromise in some functionalities of the application due to database features. This paper proposes an architectural framework for an application to exploit heterogeneous databases with a polyglot approach. A working application to demonstrate the use of different databases for various modules of an application is presented. Two instances of MongoDB and a single instance of MySQL have been used in the proposed application

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Xu, Quanqing, Chuanhui Yang, and Aoying Zhou. "Native Distributed Databases: Problems, Challenges and Opportunities." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4217–20. http://dx.doi.org/10.14778/3685800.3685839.

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Native distributed databases, crucial for scalable applications, offer transactional and analytical prowess but face data intricacies and network challenges. Under the CAP theorem's constraints, latency and replication issues necessitate creative approaches to maintenance, security, and upgrades. Progress in consistency algorithms, network technology, automation, and machine learning for optimization presents significant potential. Embracing hybrid transactional/analytical processing (HTAP), these databases represent an evolutionary leap in data management, aiming to reconcile performance with

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Sharma, Yashraj, and Yashasvi Sharma. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research -GRANTHAALAYAH 7, no. 7 (2019): 351–59. http://dx.doi.org/10.29121/granthaalayah.v7.i7.2019.777.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also som

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Yashraj, Sharma, and Sharma Yashasvi. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research - Granthaalayah 7, no. 7 (2019): 351–59. https://doi.org/10.5281/zenodo.3364448.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features o

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Choi, Dong-Wan, Chin-Wan Chung, and Yufei Tao. "A scalable algorithm for maximizing range sum in spatial databases." Proceedings of the VLDB Endowment 5, no. 11 (2012): 1088–99. http://dx.doi.org/10.14778/2350229.2350230.

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Dharavath, Ramesh, and Chiranjeev Kumar. "A scalable generic transaction model scenario for distributed NoSQL databases." Journal of Systems and Software 101 (March 2015): 43–58. http://dx.doi.org/10.1016/j.jss.2014.11.037.

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Bacry, Emmanuel, Stéphane Gaïffas, Fanny Leroy, et al. "SCALPEL3: A scalable open-source library for healthcare claims databases." International Journal of Medical Informatics 141 (September 2020): 104203. http://dx.doi.org/10.1016/j.ijmedinf.2020.104203.

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Miranker, Daniel P. "Metric-Space Indexes as a Basis for Scalable Biological Databases." OMICS: A Journal of Integrative Biology 7, no. 1 (2003): 57–60. http://dx.doi.org/10.1089/153623103322006616.

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Filip, Ion-Dorinel, Cristian-Mihai Iliescu, and Florin Pop. "Assertive, Selective, Scalable IoT-Based Warning System." Sensors 22, no. 3 (2022): 1015. http://dx.doi.org/10.3390/s22031015.

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With the evolution of technology, developed systems have become more complex and faster. Thirty years ago, there were no protocols or databases dedicated to developing and implementing IoT projects. We currently have protocols such as MQTT, AMQP, CoAP, and databases such as InfluxDB. They are built to support a multitude of data from an IoT system and scale very well with the system. This paper presents the design and implementation of an IoT alert system that uses MQTT and InfluxDB to collect and store data. We design a scalable system to display assertive alerts on a Raspberry Pi. Each user

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Hajidehi, Milad Rezaei, Sraavan Sridhar, and Margo Seltzer. "CUTTANA: Scalable Graph Partitioning for Faster Distributed Graph Databases and Analytics." Proceedings of the VLDB Endowment 18, no. 1 (2024): 14–27. https://doi.org/10.14778/3696435.3696437.

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Graph partitioning plays a pivotal role in various distributed graph processing applications, including graph analytics, graph neural network training, and distributed graph databases. A "good" graph partitioner reduces workload execution time, worker imbalance, and network overhead. Graphs that require distributed settings are often too large to fit in the main memory of a single machine. This challenge renders traditional in-memory graph partitioners infeasible, leading to the emergence of streaming solutions. Streaming partitioners produce lower-quality partitions, because they work from pa

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Rathore, Mukesh, and Sikha S. Bagui. "MongoDB: Meeting the Dynamic Needs of Modern Applications." Encyclopedia 4, no. 4 (2024): 1433–53. http://dx.doi.org/10.3390/encyclopedia4040093.

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This entry reviews MongoDB’s fundamentals, architectural features, advantages, and limitations, providing a comprehensive understanding of its capabilities. MongoDB’s impact on the database landscape is profound, challenging traditional relational databases and influencing the adoption of NoSQL solutions globally. With its continued growth, innovation, and commitment to addressing evolving market needs, MongoDB remains a pivotal player in modern data management, empowering organizations to build scalable, efficient, and high-performance applications.

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Swapnil, Raj, and Kumar Raghav Anuj. "Elasticity in the cloud related to database autonomies and scalability." i-manager’s Journal on Cloud Computing 9, no. 1 (2022): 26. http://dx.doi.org/10.26634/jcc.9.1.18719.

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Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller d

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Davardoost, Farnaz, Amin Babazadeh Sangar, and Kambiz Majidzadeh. "An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier." Mathematical Problems in Engineering 2022 (April 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/2860735.

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Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new databases known as NOSQL have been introduced. The issue is that such a database is difficult to analyze. Online analytical processing (OLAP) is the foundational technology for data analysis in business intelligence. Because these technologies were designed primarily for relational database systems, performing OLAP in NOSQL is difficult. We present a model for extracting OLAP cubes from a document-oriented NOSQL database in this article. A scalable Naïve Bayes classi

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Ju, Cheng, Susan Gruber, Samuel D. Lendle, et al. "Scalable collaborative targeted learning for high-dimensional data." Statistical Methods in Medical Research 28, no. 2 (2017): 532–54. http://dx.doi.org/10.1177/0962280217729845.

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Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well-behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation procedure. The original instant

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Pranith Kumar Reddy Myeka. "Data modeling best practices for AI-driven applications: Architectures for scale and efficiency." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1263–74. https://doi.org/10.30574/wjaets.2025.15.2.0633.

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This article examines best practices for designing scalable and efficient data models to support artificial intelligence applications. It explores the evolution from traditional database architectures to AI-optimized systems, highlighting fundamental modeling decisions regarding normalization, performance optimization, and data integration. The text details technical approaches for scaling AI infrastructure, including partitioning strategies, specialized indexing methodologies, vector databases, and feature stores. Industry case studies demonstrate practical implementations in recommendation e

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., Syamily K. R. "SURVEY ON SCALABLE CONTINUAL TOP-K KEYWORD SEARCH IN RELATIONAL DATABASES." International Journal of Research in Engineering and Technology 02, no. 12 (2013): 312–15. http://dx.doi.org/10.15623/ijret.2013.0212054.

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McClean, S., B. Scotney, and K. Greer. "A scalable approach to integrating heterogeneous aggregate views of distributed databases." IEEE Transactions on Knowledge and Data Engineering 15, no. 1 (2003): 232–36. http://dx.doi.org/10.1109/tkde.2003.1161592.

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Huidong Jin, Man-Leung Wong, and K. S. Leung. "Scalable model-based clustering for large databases based on data summarization." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 11 (2005): 1710–19. http://dx.doi.org/10.1109/tpami.2005.226.

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B, Tejaswini, Induja R, Navyashree M, and Amreen Kowsar. "Block Chain-Based Distributed NoSQL Databases with secure and Scalable Framework." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40361.

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This paper explores the convergence of block chain technology with distributed NoSQL databases to address the growing demand for secure and scalable decentralized systems. Block chain ensures tamper- resistant and auditable data records, while NoSQL databases offer high- speed data operation for large- scale operations. By integrating these technologies, the proposed system leverages block chain’s agreement- driven synchronization and NoSQL’s effective storage capabilities to produce a flexible frame. The architecture addresses common challenges analogous as data redundancy, quiescence, and pe

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Kementsietsidis, Anastasios, Frank Neven, Dieter Van de Craen, and Stijn Vansummeren. "Scalable multi-query optimization for exploratory queries over federated scientific databases." Proceedings of the VLDB Endowment 1, no. 1 (2008): 16–27. http://dx.doi.org/10.14778/1453856.1453864.

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Cordeiro, Robson L. F., Fan Guo, Donna S. Haverkamp, et al. "QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases." Information Sciences 264 (April 2014): 211–29. http://dx.doi.org/10.1016/j.ins.2013.11.013.

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Hong, Zicong, Song Guo, Enyuan Zhou, Wuhui Chen, Huawei Huang, and Albert Zomaya. "GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard Mechanism." Proceedings of the VLDB Endowment 16, no. 7 (2023): 1685–98. http://dx.doi.org/10.14778/3587136.3587143.

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Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain cross-shard database services. Each cross-shard database service (e.g., cross-shard queries or inter-shard load balancing) involves massive cross-shard data exchanges, while the existing cross-shard mechanisms need to process each cross-shard data exchange via the consensus of all nodes in the related shards (i.e., on-chain) to resist a Byzantine en

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Kiran, Rage Uday, Pamalla Veena, Penugonda Ravikumar, et al. "Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases." Electronics 11, no. 10 (2022): 1523. http://dx.doi.org/10.3390/electronics11101523.

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Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired

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Isaev, Movladi I., Muhammed K. Tlastankulov, and Rayana A. M. Aibueva. "FUNCTIONAL PROGRAMMING IN SCALABLE MICROSERVICE ARCHITECTURES." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 12/9, no. 153 (2024): 162–68. https://doi.org/10.36871/ek.up.p.r.2024.12.09.018.

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The article discusses the use of functional programming (FP) for developing scalable microservice architectures. The main attention is paid to the key principles of FP: pure functions, data immutability, and higher-order functions. Their advantages for building fault-tolerant and predictable microservice systems are analyzed. The architecture of microservices using asynchronous data processing and reactive streams is described. Performance testing has shown an increase in throughput and a decrease in response time. Practical approaches to working with databases and asynchronous interaction bet

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