Modern Relational Database Management Systems Research Articles

The main categories of NewSQL databases and their features

In the modern world, the problem of working with big data and workloads is becoming more and more acute. For more than forty years, relational databases have been the main leading systems for storing, searching and managing data. However, despite their great popularity, application experience and universality, traditional relational DBMS, due to the growing needs for scalability and performance, often cannot meet modern requirements. This has led to the emergence of new alternative data management systems, including NewSQL systems. NewSQL is a class of modern relational database management systems that provide performance comparable to NoSQL systems while maintaining the data consistency guarantees inherent in traditional database systems. The growing interest in NewSQL technology in recent times has led to an increase number of evaluations and comparisons among competing NewSQL technologies. However, today there is still a certain lack of work devoted to the study of the features of NewSQL solutions and their capabilities in comparison with other technologies. This paper discusses the main features of the most famous NewSQL products of different categories and the identified problems associated with them. To overcome the certain ambiguity in the names and translations of some terms related to the subject area under consideration, which takes place in numerous relevant sources, additional explanations are given. For comparison, the paper presents the values of important characteristics inherent in NewSQL, traditional relational and NoSQL database systems. This paper can help researchers and people from the industry choose the best storage solutions for their needs.

Storing Dynamical Data Using Natural Language Addressing

The present paper refers to the possibilities for storing dynamical data from tracking moving objects. The urgency of this problem is determined primarily by the need for electronic registration, storage and processing of large volumes of semi-structured dynamical data in various fields of practice. The new problem raised, is the way in which the relationships between the data are described. The capabilities of modern relational database management systems do not allow rapid readjustment in accordance with the changes in practice. The emergence of new types of data connections requires readjustment, which takes time and requires a certain level of skill. In this article we consider an approach based on “Natural language addressing”, which allows solving these problems and we will offer a conceptual design (prototype) of an extension to existing systems, which facilitates the work, especially with dynamical semi-structured data and implicit relationships between them.

RDBMS Current Challenges and Oppurtunities with NoSQL to NewSQL

Relational Database Management Systems (RDBMS) are designed to manage well-structured data, requiring users to design a schema before storing and querying data with the help of SQL (Structured Query Language). The evolution of database management system undergoes many developments for more than forty years to meet the challenges by modern applications. In this fast Growing science and technological period, where advancements in web technology, mobile devices and sensors (IoT) need to accommodate huge amount of structured and unstructured data, called Big Data. The demand for technologies with large processing and storage requirements are rising enormously. Customized RDBMS has to confront challenges against Big Data’s scalability and performances. To meet these requirements such as volume, variety, velocity and variability, enterprises are adopting alternative technologies like NoSQL and NewSQL. Currently, NewSQL scalability is classified as modern RDBMS which carries NoSQL for OLTP (Online Transaction Processing) to read as well write data, whereas satisfying the Atomicity, Consistency, Isolation, and Durability (ACID) properties of a traditional DB system. Further my research paper introduces NoSQL and its key attributes and NewSQL (NuoDB, VoltDB)

NewSQL: Towards Next-Generation Scalable RDBMS for Online Transaction Processing (OLTP) for Big Data Management

One of the key advances in resolving the “big-data” problem has been the emergence of an alternative database technology. Today, classic RDBMS are complemented by a rich set of alternative Data Management Systems (DMS) specially designed to handle the volume, variety, velocity and variability of Big Data collections; these DMS include NoSQL, NewSQL and Search-based systems. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. This paper discusses about NewSQL data management system; and compares with NoSQL and with traditional database system. This paper covers architecture, characteristics, classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list of popular NoSQL as well as NewSQL databases in separate categorized tables. This paper compares SQL based RDBMS, NoSQL and NewSQL databases with set of metrics; as well as, addressed some research issues of NoSQL and NewSQL.

A Wider Concept for Similarity Joins

Join is one of the most studied and employed retrieval operators made available by the modern relational database management systems (RDBMSs). This binary operator is algebraically defined as a Cartesian product followed by the selection operator that specifies the join condition. In modern RDBMS, the join condition employs comparison operators based both on equality and on the Total Ordering Relationship. The resulting selection and join operators based on them are commutative, so they can be executed in any order, regardless of the expression ordering in the query command, allowing the RDBMS to look for optimized query evaluation strategies. However, more complex data do not follow equality nor TOR properties. In fact, they are better compared by similarity. Join operators based on similarity comparison operators are called 'similarity joins'. There exist similarity joins that can be executed directly as a similarity selection following a Cartesian product, but others demand extra and/or intermediate operations before they can compute the final result. In this paper, we analyze the existing types of similarity join in an algebraic way and we claim that in fact some of them generate operators that are not a join, but other binary operators. We identify a novel binary operator that embraces them and provide three variations of the new proposed operator. As an important follow up, the new operator allows relationally expressing and answering queries that previously could be processed only in sequential ways. The experiments performed on real data sets support our claim that our new operator is fast and flexible enough to be represented in the relational algebra and included into the RDBMSs, and it meets the demands for similarity queries from modern applications.

Adaptive XML to relational mapping: an integrated approach

Storing and querying XML (eXtensible Markup Language) data in relational form can exploit various services offered by modern relational database management systems (RDBMSs). Due to structural complexity of XML, there are many equivalent relational mapping schemes for the same XML data and queries. In this paper, we propose the adaptive XML to relational mapping (AX2RM) system, which considers finding optimal XML to relational (X2R) mapping as four separate but correlated procedures: logical database design, data scale estimation, workload transformation, and physical database design. We view the whole process as an autonomic computing problem and formalize the adaptive X2R mapping problem. Search spaces for each procedure are investigated individually, and five approaches for finding the optimal mapping are studied. We propose an integrated approach with greedy pruning (IT-GP), which views the mapping procedures as a whole and exploits heuristic rules in each procedure to prune impossible mappings as early as possible. Evaluation of these approaches shows the validity and high efficiency of IT-GP.

A fast scalable classifier tightly integrated with RDBMS

In this paper, we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational database management systems (RDBMS). In addition to high classification accuracy, the unique features of the approach include its high training speed, linear scalability, and simplicity in implementation. More importantly, the major computation required in the approach can be implemented using standard functions provided by the modern relational DBMS. Besides, with the effective rule pruning strategy, the algorithm proposed in this paper can produce a compact set of classification rules. The results of experiments conducted for performance evaluation and analysis are presented.