All about that - a URI profiling tool for monitoring and preserving linked data

All About That (AAT) is a URI Profiling tool which allows users to monitor and preserve Linked Data in which they are interested. Its design is based upon the principle of adapting ideas from hypermedia link integrity in order to apply them to the Semantic Web. As the Linked Data Web expands it will become increasingly important to maintain links such that the data remains useful and therefore this tool is presented as a step towards providing this maintenance capability

A Multi-key Transactions Model for NoSQL Cloud Database Systems

NoSQL cloud database systems are new types of databases that are built across thousands of cloud nodes and are capable of storing and processing Big Data. NoSQL systems have increasingly been used in large scale applications that need high availability and efficiency but with weaker consistency. Consequently, such systems lack support for standard transactions which provide stronger consistency. This paper proposes a new multi-key transactional model which provides NoSQL systems with standard transaction support and stronger level of data consistency. The strategy is to supplement current NoSQL architecture with an extra layer that manages transactions. The proposed model is configurable where consistency, availability and efficiency can be adjusted based on application requirements. The proposed model is validated through a prototype system using MongoDB. Preliminary experiments show that it ensures stronger consistency and maintains good performance

A Primer on NoSQL Databases for Enterprise Architects: The CAP Theorem and Transparent Data Access with MongoDB and Cassandra

MongoDB and Apache Cassandra are the dominant Not Only SQL (NoSQL) database management systems for persisting structured records. Moreover, the pair are respectively in the top-five and top-ten of database management systems generally. Therefore this work seeks to present the two leading systems, along with the underlying principle of the CAP Theorem, in the context of creating transparent data access tiers capable of supporting flexible enterprise architectures

Scalable and Dynamic Regeneration of Big Data Volumes

A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. A rich body of work exists on synthetic database regeneration, but suffers critical limitations with regard to: (a) maintaining statistical fidelity to the client’s query processing, and/or (b) scaling to large data volumes. In this paper, we present HYDRA, a workload-dependent database regenerator that leverages a declarative approach to data regeneration to assure volumetric similarity, a crucial aspect of statistical fidelity, and materially improves on the prior art by adding scale, dynamism and functionality. Specifically, Hydra uses an optimized linear programming (LP) formulation based on a novel regionpartitioning approach. This spatial strategy drastically reduces the LP complexity, enabling it to handle query workloads on which contemporary techniques fail. Second, Hydra incorporates deterministic post-LP processing algorithms that provide high efficiency and improved accuracy. Third, Hydra introduces the concept of dynamic regeneration by constructing a minuscule database summary that can on-the-fly regenerate databases of arbitrary size during query execution, while obeying volumetric specifications derived from the query workload. A detailed experimental evaluation on standard OLAP benchmarks demonstrates that Hydra can efficiently and dynamically regenerate large warehouses that accurately mimic the desired statistical characteristics

The Data Lakehouse: Data Warehousing and More

Relational Database Management Systems designed for Online Analytical Processing (RDBMS-OLAP) have been foundational to democratizing data and enabling analytical use cases such as business intelligence and reporting for many years. However, RDBMS-OLAP systems present some well-known challenges. They are primarily optimized only for relational workloads, lead to proliferation of data copies which can become unmanageable, and since the data is stored in proprietary formats, it can lead to vendor lock-in, restricting access to engines, tools, and capabilities beyond what the vendor offers. As the demand for data-driven decision making surges, the need for a more robust data architecture to address these challenges becomes ever more critical. Cloud data lakes have addressed some of the shortcomings of RDBMS-OLAP systems, but they present their own set of challenges. More recently, organizations have often followed a two-tier architectural approach to take advantage of both these platforms, leveraging both cloud data lakes and RDBMS-OLAP systems. However, this approach brings additional challenges, complexities, and overhead. This paper discusses how a data lakehouse, a new architectural approach, achieves the same benefits of an RDBMS-OLAP and cloud data lake combined, while also providing additional advantages. We take today's data warehousing and break it down into implementation independent components, capabilities, and practices. We then take these aspects and show how a lakehouse architecture satisfies them. Then, we go a step further and discuss what additional capabilities and benefits a lakehouse architecture provides over an RDBMS-OLAP

Transactions and data management in NoSQL cloud databases

NoSQL databases have become the preferred option for storing and processing data in cloud computing as they are capable of providing high data availability, scalability and efficiency. But in order to achieve these attributes, NoSQL databases make certain trade-offs. First, NoSQL databases cannot guarantee strong consistency of data. They only guarantee a weaker consistency which is based on eventual consistency model. Second, NoSQL databases adopt a simple data model which makes it easy for data to be scaled across multiple nodes. Third, NoSQL databases do not support table joins and referential integrity which by implication, means they cannot implement complex queries. The combination of these factors implies that NoSQL databases cannot support transactions. Motivated by these crucial issues this thesis investigates into the transactions and data management in NoSQL databases. It presents a novel approach that implements transactional support for NoSQL databases in order to ensure stronger data consistency and provide appropriate level of performance. The novelty lies in the design of a Multi-Key transaction model that guarantees the standard properties of transactions in order to ensure stronger consistency and integrity of data. The model is implemented in a novel loosely-coupled architecture that separates the implementation of transactional logic from the underlying data thus ensuring transparency and abstraction in cloud and NoSQL databases. The proposed approach is validated through the development of a prototype system using real MongoDB system. An extended version of the standard Yahoo! Cloud Services Benchmark (YCSB) has been used in order to test and evaluate the proposed approach. Various experiments have been conducted and sets of results have been generated. The results show that the proposed approach meets the research objectives. It maintains stronger consistency of cloud data as well as appropriate level of reliability and performance

Secure Transaction Model for NoSQL Database Systems: Review

NoSQL cloud database frameworks would consist new sorts of databases that would construct over many cloud hubs and would be skilled about storing and transforming enormous information. NoSQL frameworks need to be progressively utilized within substantial scale provisions that require helter skelter accessibility. What’s more effectiveness for weaker consistency? Consequently, such frameworks need help for standard transactions which give acceptable and stronger consistency. This task proposes another multi-key transactional model which gives NoSQL frameworks standard for transaction backing and stronger level from claiming information consistency. Those methodology is to supplement present NoSQL structural engineering with an additional layer that manages transactions. The recommended model may be configurable the place consistency, accessibility Furthermore effectiveness might make balanced In view of requisition prerequisites. The recommended model may be approved through a model framework utilizing MongoDB. Preliminary examinations show that it ensures stronger consistency Furthermore supports great execution

Database Design and Implementation

The book of Database Design and Implementation is a comprehensive guide that provides a thorough introduction to the principles, concepts, and best practices of database design and implementation. It covers the essential topics required to design, develop, and manage a database system, including data modeling, database normalization, SQL programming, and database administration. The book is designed for students, database administrators, software developers, and anyone interested in learning how to design and implement a database system. It provides a step-by-step approach to database design and implementation, with clear explanations and practical examples. It also includes exercises and quizzes at the end of each chapter to help reinforce the concepts covered. The book begins by introducing the fundamental concepts of database systems and data modeling. It then discusses the process of database design and normalization, which is essential for creating a well-structured and efficient database system. The book also covers SQL programming, which is used for querying, updating, and managing data in a database. Additionally, it includes a comprehensive discussion on database administration, including security, backup and recovery, and performance tuning.https://orc.library.atu.edu/atu_oer/1002/thumbnail.jp