Standard Relational Database Management Systems Research Articles

A STUDY OF THE BENEFITS AND DRAWBACKS OF NOSQL DATABASES

NoSQL database management solutions are a departure from standard relational database management systems (RDBMS). Non-tabular NoSQL databases store data in a distinct way when compared to relational database tables. The data model of NoSQL databases is used to classify them. The most popular types are document, key-value, wide-column, and graph. They feature customizable schemas and can easily handle large amounts of data as well as significant user loads. They have adaptable schemas and can handle big amounts of data and high user loads with ease. Furthermore, their data models' flexibility and ease of use, in comparison to the relational model, can faster the development, especially in the field of cloud computing. This paper discusses the benefits of NoSQL databases, such as the ability to handle large volumes of data at high speeds with a scale-out architecture, the ability to store unstructured, semi-structured, or structured data, and the ability to easily update schemas and fields, making them developer friendly. Furthermore, this paper discusses the downsides, such as the lack of reliability features found in Relational Databases.

Dissociation and propagation for approximate lifted inference with standard relational database management systems

Probabilistic inference over large data sets is a challenging data management problem since exact inference is generally #P-hard and is most often solved approximately with sampling-based methods today. This paper proposes an alternative approach for approximate evaluation of conjunctive queries with standard relational databases: In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known PTIME self-join-free conjunctive queries: A query is in PTIME if and only if our algorithm returns one single plan. Furthermore, our approach is a generalization of a family of efficient ranking methods from graphs to hypergraphs. We also adapt three relational query optimization techniques to evaluate all necessary plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers. We also note that the techniques developed in this paper apply immediately to lifted inference from statistical relational models since lifted inference corresponds to PTIME plans in probabilistic databases.

Comparative Analysis of Real Time Replication Service Efficiency on Enterprise Level

In today's growing of enterprise data, in order to make a full use of the capabilities of hardware, increasing application performance, reducing cost of ownership and enabling new scenarios that were not possible before such as are today's developed in-memory data platforms is essential. In this paper, an in-depth analysis of benefits and features of in-memory database comparing to standard relational database management system is presented. Moreover, an implementation of real-time data replication from a production ERP landscape system to database is conducted, where the experimental results shows the flow of data replication and the key benefits for an enterprise.

DCMDSM: a DICOM decomposed storage model

To design, build, and evaluate a storage model able to manage heterogeneous digital imaging and communications in medicine (DICOM) images. The model must be simple, but flexible enough to accommodate variable content without structural modifications; must be effective on answering query/retrieval operations according to the DICOM standard; and must provide performance gains on querying/retrieving content to justify its adoption by image-related projects. The proposal adapts the original decomposed storage model, incorporating structural and organizational characteristics present in DICOM image files. Tag values are stored according to their data types/domains, in a schema built on top of a standard relational database management system (RDBMS). Evaluation includes storing heterogeneous DICOM images, querying metadata using a variable number of predicates, and retrieving full-content images for different hierarchical levels. When compared to a well established DICOM image archive, the proposal is 0.6-7.2 times slower in storing content; however, in querying individual tags, it is about 48.0% faster. In querying groups of tags, DICOM decomposed storage model (DCMDSM) is outperformed in scenarios with a large number of tags and low selectivity (being 66.5% slower); however, when the number of tags is balanced with better selectivity predicates, the performance gains are up to 79.1%. In executing full-content retrieval, in turn, the proposal is about 48.3% faster. DCMDSM is a model built for the storage of heterogeneous DICOM content, based on a straightforward database design. The results obtained through its evaluation attest its suitability as a storage layer for projects where DICOM images are stored once, and queried/retrieved whenever necessary.

Oblivious bounds on the probability of boolean functions

This article develops upper and lower bounds for the probability of Boolean functions by treating multiple occurrences of variables as independent and assigning them new individual probabilities. We call this approach dissociation and give an exact characterization of optimal oblivious bounds , that is, when the new probabilities are chosen independently of the probabilities of all other variables. Our motivation comes from the weighted model counting problem (or, equivalently, the problem of computing the probability of a Boolean function), which is #P-hard in general. By performing several dissociations, one can transform a Boolean formula whose probability is difficult to compute into one whose probability is easy to compute, and which is guaranteed to provide an upper or lower bound on the probability of the original formula by choosing appropriate probabilities for the dissociated variables. Our new bounds shed light on the connection between previous relaxation-based and model-based approximations and unify them as concrete choices in a larger design space. We also show how our theory allows a standard relational database management system (DBMS) to both upper and lower bound hard probabilistic queries in guaranteed polynomial time.

Graph data management for molecular and cell biology

As high-throughput biology begins to generate large volumes of systems biology data, the need grows for robust, efficient database systems to support investigations of metabolic and signaling pathways, chemical reaction networks, gene regulatory networks, and protein interaction networks. Network data is frequently represented as graphs, and researchers need to navigate, query and manipulate this data in ways that are not well supported by standard relational database management systems (RDBMSs). Current approaches to managing graphs in an RDBMS rely on either external procedural logic to execute the graph algorithms or clumsy and inefficient algorithms implemented in Structured Query Language (SQL). In this paper we describe the Systems Biology Graph Extender, a research prototype that extends the IBM RDBMS—DB2® Universal Database software—with graph objects and operations to support declarative SQL queries over biological networks and other graph structures. Supported operations include neighborhood queries, shortest path queries, spanning trees, graph transposition, and graph matching. In a federated database environment, graph operations may be applied to data stored in any format, whether remote or local, relational or nonrelational. A single federated query may include both graph-based predicates and predicates over related data sources, such as microarray expression levels, clinical prognosis and outcome, or the function of orthologous proteins (i.e., proteins that are evolutionarily related to those in another species) in mouse disease models.

Implementing a Graph Transformation Engine in Relational Databases

We present a novel approach to implement a graph transformation engine based on standard relational database management systems (RDBMSs). The essence of the approach is to create database views for each rule and to handle pattern matching by inner join operations while handling negative application conditions by left outer join operations. Furthermore, the model manipulation prescribed by the application of a graph transformation rule is also implemented using elementary data manipulation statements (such as insert, delete). As a result, we obtain a robust and fast transformation engine especially suitable for (1) extending modeling tools with an underlying RDBMS repository and (2) embedding model transformations into large distributed applications where models are frequently persisted in a relational database and transaction handling is required to handle large models consistently.

A spatial-temporal system for dynamic cadastral management

A practical spatio-temporal database (STDB) technique for dynamic urban land management is presented. One of the STDB models, the expanded model of Base State with Amendments (BSA), is selected as the basis for developing the dynamic cadastral management technique. Two approaches, the Section Fast Indexing (SFI) and the Storage Factors of Variable Granularity (SFVG), are used to improve the efficiency of the BSA model. Both spatial graphic data and attribute data, through a succinct engine, are stored in standard relational database management systems (RDBMS) for the actual implementation of the BSA model. The spatio-temporal database is divided into three interdependent sub-databases: present DB, history DB and the procedures-tracing DB. The efficiency of database operation is improved by the database connection in the bottom layer of the Microsoft SQL Server. The spatio-temporal system can be provided at a low-cost while satisfying the basic needs of urban land management in China. The approaches presented in this paper may also be of significance to countries where land patterns change frequently or to agencies where financial resources are limited.

Graph Transformation in Relational Databases

We present a novel approach to implement a graph transformation engine based on standard relational database management systems (RDBMSs). The essence of the approach is to create database views for each rules and to handle pattern matching by inner join operations while negative application conditions by left outer join operations. Furthermore, the model manipulation prescribed by the application of a graph transformation rule is also implemented using elementary data manipulation statements (such as insert, delete, update).

The Impact of the Australian Bibliographic Network on Australian Libraries

From its small beginnings in 1981 of six pilot users and the National Library of Australia (NLA), the Australian Bibliographic Network (ABN) has grown to be a truly national system, with 1,315 users at May 1995. The National Bibliographic Database has expanded to over 11 million records and 22 million holdings statements. It includes records from the USA, the UK, Canada, New Zealand, Singapore and Vietnam. It has come to be the single union list of holdings of Australian libraries, and the first point of reference for the majority of interlibrary loan transactions. The ABN is seen as both an NLA business and a cooperative undertaking of Australian libraries. Management consists of a Network Committee, which advises the Director General of NLA on all aspects of operation, and a Standards Committee, whose role is to make recommendations to NLA on cataloguing standards for the network. Annual Users' Meetings are held. Since 1987 NLA has been developing a database host for Australian libraries called OZLINE, in parallel with ABN. In 1990 it was decided to go for complete redevelopment using a text retrieval product and an industry standard Relational Database Management System. Following discussions with the National Library of New Zealand, which had indicated broadly similar requirements, it was agreed that the two libraries would jointly seek a system. The Australian service is to be known in future as WORLD 1.

Exploiting relational database technology in a GIS

All systems for managing data face common problems such as backup, recovery, auditing, security, data integrity, and concurrent update. Other challenges include the ability to share data easily between applications and to distribute data across several computers, whereas continuing to manage the problems already mentioned. Geographic information systems are no exception, and need to tackle all these issues. Standard relational database-management systems (RDBMSs) provide many features to help solve the issues mentioned so far. This paper describes how the IBM geoManager product approaches these issues by storing all its geographic data in a standard RDBMS in order to take advantage of such features. Areas in which standard RDBMS functions need to be extended are highlighted, and the way in which geoManager does this is explained. The performance implications of storing all data in the relational database are discussed. An important distinction is made between the storage and management of geographic data and the manipulation and analysis of geographic data, which needs to be made when considering the applicability of relational database technology to GIS.

Implementation of extended indexes in POSTGRES

The vaunted "Spartan simplicity" of the relational model can limit the usefulness of the relational database management system (RDBMS) for non-traditional applications. For example, there is no natural way to model a keyword index for bibliographic informational retrieval (IR), or any other index whose key values are computed from column values, in a standard RDBMS. The extended indexing proposed in [LYNC88a] is intended to support applications that require indexes on computed values. This paper reports on an implementation of this type of indexing using the POSTGRES extensible database management system [STON86a], focusing on two issues: general problems, and the features in POSTGRES that proved helpful in the solution of these problems.