NoSQL Database Technologies

As cloud computing continues to evolve, organizations are finding new ways to store the massive amounts of big data that are collected. Big data storage often require greater flexibility and scalability which can be provided by incorporating NoSQL technologies. NoSQL (Not Only SQL) is quickly becoming a popular approach to store large and unstructured data. This paper looks at the various classifications of NoSQL technologies as well as many of the notable characteristics of the technologies. The authors also describe some deficiencies of using NoSQL and give some explanation to why companies are adopting the technology. The paper concludes with suggestions for future research of NoSQL technologies and a content analysis of current articles in database management is provided in the appendix

Reusable Prime Number Labeling Scheme for Hierarchical Data Representation in Relational Databases

Hierarchical data structures are important for many computing and information science disciplines including data mining, terrain modeling, and image analysis. There are many specialized hierarchical data management systems, but they are not always available. Alternatively, relational databases are far more common and offer superior reliability, scalability, and performance. However, relational databases cannot natively store and manage hierarchical data. Labeling schemes resolve this issue by labeling all nodes with alphanumeric strings that can be safely stored and retrieved from a database. One such scheme uses prime numbers for its labeling purposes, however the performance and space utilization of this method are not optimal. We propose a more efficient and compact version of this approach

An efficient management system for large digital object collections

Includes abstract.Includes bibliographical references (leaves 87-91)Cultures evolve continuously, and it is therefore vital to track and record these changes, and most importantly of all, manage the resulting huge mass of data such as images, video clips, audio recordings and documents. This thesis examines the design of a Web-based solution, hereafter referred to as the Information Management System (IMS), to handle the efficient, accurate and secure management of a large number of objects

Using a Relational Database for Scalable XML Search

XML is a flexible and powerful tool that enables information and security sharing in heterogeneous environments. Scalable technologies are needed to effectively manage the growing volumes of XML data. A wide variety of methods exist for storing and searching XML data; the two most common techniques are conventional tree-based and relational approaches. Tree-based approaches represent XML as a tree and use indexes and path join algorithms to process queries. In contrast, the relational approach utilizes the power of a mature relational database to store and search XML. This method relationally maps XML queries to SQL and reconstructs the XML from the database results. To date, the limited acceptance of the relational approach to XML processing is due to the need to redesign the relational schema each time a new XML hierarchy is defined. We, in contrast, describe a relational approach that is fixed schema eliminating the need for schema redesign at the expense of potentially longer runtimes. We show, however, that these potentially longer runtimes are still significantly shorter than those of the tree approach. We use a popular XML benchmark to compare the scalability of both approaches. We generated large collections of heterogeneous XML documents ranging in size from 500MB to 8GB using the XBench benchmark. The scalability of each method was measured by running XML queries that cover a wide range of XML search features on each collection. We measure the scalability of each method over different query features as the collection size increases. In addition, we examine the performance of each method as the result size and the number of predicates increase. Our results show that our relational approach provides a scalable approach to XML retrieval by leveraging existing relational database optimizations. Furthermore, we show that the relational approach typically outperforms the treebased approach while scaling consistently over all collections studied.