Storage and retrieval of database constraints

Chapter 6 - Secondary Storage

Relational database management systems (RDBMSs) have been a common option to manage structured data over the past decades. In recent years, with the prevalence of big data applications, vast unstructured and semi-structured data are generated, deeply challenging the relational model used in RDBMSs. For this reason, a wide spectrum of NoSQL databases are developed for managing unstructured, semi-structured or structured data. For example, graph database management systems (GDBMSs) are commonly used as an important category of NoSQL databases, to manage sophisticated graph data as well as relational data. Nonetheless, as claimed in existing literatures, both RDBMSs and GDBMSs are capable of managing graph data and relational data, the boundaries of them still remain unclear. In this paper, we propose a unified benchmark for RDBMSs and GDBMSs, to evaluate them under the same metrics, and report which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join and deep recursive operations, (3) GDBMSs and RDBMSs are comparable for two-table join and shallow recursive operations.

A write blocker allows read-only access to digital data on a secondary storage device by placing a hardware or software filter between the host computer and the storage device. The filter monitors I/O commands sent from the application on the host computer, only allowing commands to the device that make no changes to its data. This paper examines the I/O commands used to access secondary storage devices and discusses their implications for BIOS-based and hardware-based write blockers.

An archiving model for a hierarchical information storage environment

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

Exploring new electrolyte materials is important to comprehend high performance secondary charge storage devices for effective utilization of renewable energy. Ionic liquids with broad electrochemical voltage window and wide range of thermal stability are prospective materials for the same. As an improved version of this class of materials, we report herein, on the synthesis of novel pyrrolidinium based ionic liquid tethered nanoparticle hybrid electrolytes and compared their conductivity and electrochemical cycling behaviors. Hybrid electrolytes are prepared by covalently grafting N-Methyl-N-propylpyrrolidinium bis(trifluoromethanesulfonyl) imide to the surfaces of pre-synthesized TiO2 nanoparticles at three different core volume fractions, ranging from 0.11 to 0.46. Uniform dispersion of the core nanoparticle within the matrix of the electrolytes improves their mechanical properties. With increasing core volume fractions, the electrical conductivity slightly decreases due to increasing viscosity. Nevertheless, nanoparticle grafting significantly improves the electrochemical performance of the electrolytes. The electrolytes show very good capacitance retentivity (>90%) and long-term cycling stability above room temperature even at the lowest grafting density within symmetric stainless steel electrodes. Impressive room temperature electrochemical performance of these materials within symmetric supercapacitor cell containing CMS graphite as electrodes further demonstrates the applicability of these hybrid materials as promising electrolytes for secondary charge storage devices.

Efficient buffering for concurrent disk and tape I/O

In IT industry research communities and data scientists have observed that Big Data has challenged the legacy of solutions. ‘Big Data’ term used for any collection of data or data sets which is so large and complex and difficult to process and manage using traditional data processing applications and existing Relational Data Base Management Systems (RDBMSs). In Big Data; the most important challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization and privacy. As the data increases in various dimensions with various features like structured, semi structured and unstructured with high velocity, high volume and high variety; the RDBMSs face another fold of challenges to be studied and analyzed. Due to the aforesaid limitations of RDBMSs, data scientists and information managers forced to rethink about alternative solutions for handling such data with 3Vs.Initially research study focused on to develop an intelligent base for decision makers so that alternative solutions for long term suitable solutions and handle the data and information with 3Vs can be designed. In this research attempts has been made to analyze the feature based capabilities of RDBMSs and then performance experimentation, observation and analysis has been done with Big Data handling tools and technologies. The features considered for scientific observation and analysis were resource consumption, execution time, on demand scalability, maximum data size, structure of the data, data visualization, and ease of deployment, cost and security. Finally the research provides a decision support metrics for decision makers in selecting the appropriate tool or technology based on the nature of data to be handled in the target organizations.

A complex network models relationships among data elements in several applications, such as social networks, urban street organization, disease monitoring and treatment co-occurrences, and communication and transport infrastructures. To store and manage large volumes of data, the Relational Database Management Systems (RDBMSs) provide efficient disk-based data management, being present in the most diverse application domains, including enterprise information, finance, medical or clinical support, and in airline reservations and scheduling. However, RDBMSs still face challenges to efficiently process queries that "navigate" over complex networks represented as graphs. Those queries, named in this work as graph queries, are often expressed by recursive queries, which execute multiple join operations between an internal, temporary table that stores intermediary results and the table that stores the graph's edges. This Ph.D. dissertation focuses on developing methods and specialized data structures based on adjacency-lists aiming at improving graph query performance in an RDBMS through recursive queries. This project's main goal targets the question: "How can we improve the performance of queries over graph data executed in a RDBMS?". To answer it, we proposed specializations over each table involved in a recursive query: the table of edges E, and the temporary result table T. The modifications in table T consists of adding extra measurement columns to enable employing it in multiple graph queries executed in a single database connection. In its turn, table E is specialized to allow storing multiple edges into a single row optimized for the execution of each join operation of the recursive query. Experiments using data from RDBMSs modeled as a graph allow concluding that: (i) enriching table T provides increased efficiency to execute multiple graph queries, such as the query types Single-Source Shortest Path (SSSP), Connected Components (CC), and PageRank (PR); (ii) grouping multiple edges from table E into a single row significantly improves the performance of individual recursive queries, reducing not only their elapsed time but also the required size to store the graph data; and (iii) employing Clustered Tables to group the edges into a single page also enables improved performance for recursive queries, with the advantage of requiring just a few changes in the SQL statements. Overall, specializing both tables T and E helps diminishing the gap of efficiently executing graph queries in a RDBMS.

One of the key components of a hybrid electric vehicle (HEV) drive train is its secondary energy storage device. The automotive industry is still in the process of debating on the fact, as to which device provides the best option in HEVs, for the purpose of load leveling. This paper aims at providing a fair idea with regards to the selection of secondary energy sources, based on vehicle performance characteristics and overall drive train efficiency. The performances of lead-acid (PBA), lithium-ion (Li-Ion), nickel-cadmium (Ni-Cd), nickel-metal hydride (Ni-MH), and nickel-zinc (Ni-Zn) batteries, as well as ultra-capacitors (UC) are investigated over city and highway driving schedules for a heavy-duty diesel-parallel hybrid transit bus application. Based on the simulation studies for the above-mentioned storage devices, the Ni-MH, PBA, and the ultra-capacitor technologies demonstrate best results in terms of fuel economy and percentage drive train efficiency. On the other hand, the Ni-Zn and Li-Ion batteries show much promise, but still demand a great deal of research and development work, before they become a viable option for HEV applications. Finally, the paper compares and summarizes critical performance characteristics for the energy storage devices under investigation.

A very fast parallel object store for very fast applications

Performance analysis of an Injera baking machine by using solar energy

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

Relational Database Management Systems (RDBMS) have been very successful at managing structured data with well-defined schemas. Despite this, relational systems are generally not the first choice for management of data where schemas are not pre-defined or must be flexible in the face of variations and changes. Instead, No-SQL database systems supporting JSON are often selected to provide persistence to such applications. JSON is a light-weight and flexible semi-structured data format supporting constructs common in most programming languages. In this paper, we analyze the way in which requirements differ between management of relational data and management of JSON data. We present three architectural principles that facilitate a schema-less development style within an RDBMS so that RDBMS users can store, query, and index JSON data without requiring schemas. We show how these three principles can be applied to industry-leading RDBMS platforms, such as the Oracle RDBMS Server, with relatively little effort. Consequently, an RDBMS can unify the management of both relational data and JSON data in one platform and use SQL with an embedded JSON path language as a single declarative language to query both relational data and JSON data. This SQL/JSON approach offers significant benefits to application developers as they can use one product to manage both relational data and semi-structured flexible schema data.

In Memory Computing is moving data into dynamic memory which is traditionally stored in secondary storage devices. In this paper we try to improve the capability of Java Virtual Machine (JVM) to process large data collections/application by pertinence of In-Memory Computing concepts. It helps JVM/processor to process huge data sets efficiently due to high-speed access and reduction of latency of slow secondary storage devices. It is implemented by providing more dynamic memory to JVM or by the implementation of In Memory Database concepts. In this paper we correlate Actual machine with JVM and try to improve ability of JVM to process large data collections by eliminating disk access and replacing Database with JAVA Collections in the Model Layer hence using `in memory' concepts, Henceforth a high performance system is designed.