Conventional Relational Database Research Articles

USING DATA WAREHOUSE TO MANAGE HEALTH FACILITY DATA

Managing a complete master list of public health facility data is deemed important in coordinating and planning of health services and achieving better service delivery. The master lists allow stakeholders to share a uniform operational health services data and ensuring availability and reliable data. This will able to support and manage patients clinical data and other health management services such as disease monitoring, facility management and monitoring service performance. However, most health facilities do not have accurate and informed data, this can affect monitoring of infrastructure and services provided. Various facilities have multiple and redundant lists of health data, the lists differ not only in the information they contain but also in the way they are named and identified. Existing studies state that the development of health facility registries store information mostly using conventional two-dimensional table methods or relational databases as in the case at the Health Informatics Center (HIC), Ministry of Health Malaysia. Hence, they faces the challenges in managing the data since it contains diverse data sources, the absence of uniform data standards and the dependence on manual processes and human intervention. To overcome the current problems, a dimensional modeling technique has been developed in an effort to design a Health Facility Data Warehouse. The proposed design of the data warehouse has been verified by the expert and they agrees that it is suitable for HIC to manage information related to health facilities due to its ability to deal with data sources, able to generate strategic information, as well as the ability to obtain a historical view of facility data. This study shows dimensional modeling techniques are effective in analyzing and designing data warehouse and can be a possible method in managing the master list of health facilities in Malaysia as well as other countries.

An analysis of blockchain versus relational databases for digitalising information flows in global supply chains using the analytic network process

Global trade is plagued by slow and inefficient manual processes associated with physical documents. Firms are constantly looking for new ways to improve transparency and increase the resilience of their supply chains. This can be solved by the digitalisation of supply chains and the automation of document- and information-sharing processes. Blockchain is touted as a solution to these issues due to its unique combination of features, such as immutability, decentralisation and transparency. A lack of business cases that quantify the costs and benefits causes uncertainty regarding the truth of these claims. This paper explores how the costs and benefits of a blockchain-based solution for digitalising and automating documentation flows in cross-border supply chains compare to a conventional centralised relational database solution. The research described in this paper uses primary data collected through semi-structured interviews with industry experts, as well as secondary data from literature. Two models based on existing services were developed and the costs and benefits compared and then analysed using the Architecture Trade-off Analysis Method (ATAM) and the Analytic Network Process (ANP). Findings from the analysis show that a consortium blockchain solution like TradeLens is the favourable solution for digitalising and automating information flows in cross-border supply chains.

Air Quality Data Acquisition Through Temporal Data Modeling

The paper presents a novel temporal data model for acquiring air quality data. The proposed model uses the free and open-source Postgres database with tuple time stamping, resulting in rapid and effective data extraction. The conventional relational database SQL server for data extraction is used in this paper as the foundation for an air quality temporal database model. The air quality data set collected for MDU Rohtak (source: Central Control Room for Air Quality Management - All India) is utilized to test and determine the outcomes of the proposed model. Valid time is used as the time dimension for capturing data values, together with tuple timestamping. The Postgres "tsrange" datatype is used to record the timestamp. To eliminate data duplication, the dataset is cleaned up and normalized. Using the air quality temporal data model, AQI data at various time points is effectively extracted. The present model data extraction is compared with the conventional relational data model using SQL Server database. The air quality data extraction process takes a remarkably minimal amount of time, with an average of 23% lesser time than SQL server database. It takes only a few milliseconds to extract the AQI data, and the results of the experiment are ready immediately after that. The suggested model is helpful in decision-making and offers decision-makers useful information and the tool they need to enhance air quality. The results are relevant to the study of the AQI components by different pollution management agencies like central and state pollution control boards, national green tribunal and Ministry of the Environment for efficient air pollution control measures.

Machine-Learning Approach for Predicting Harmful Diseases using Big Data and IoT: A Review

Abstract: The magnitude of the enormous quantity of data in the healthcare sector is currently on the order of petabytes, and it is expanding exponentially. The massive increase of data has resulted in a number of issues with computing, storage, and transfer. With the aid of a conventional relational database system, this type of data may be evaluated and processed. Moreover, structured data can only be processed by standard database systems. Big data, on the other hand, stores unstructured data. ICT would be able to assist society in a better storage with invention of new and effective mechanisms for the storage and accessing of the information. ICT implementation in the healthcare sector is referred to as eHealth. The processing of data, subsequent analysis, and enhancement of the decision-making processes would all benefit from application in the health care sector in order to gather better treatment options for diseases' symptoms.

Big Data: A Transition from Conventional Databases

The primary programming language created to manage data contained in database systems is called Structured Query Language (SQL). SQL was initially solely used with relational database management systems (RDBMS), but as additional categories of database systems have emerged, its use has substantially expanded. When processing Big Data, or datasets with great volume, velocity, and variety, highly distributed and scalable systems have been proven to be particularly effective at using SQL as a query language. Although conventional relational databases currently only make up a small portion of the database systems landscape, the majority of database courses that cover SQL only take traditional relational systems into account when using SQL. In this study, we suggest that SQL be taught as a general language, which can be applied to a variety of database systems, ranging from conventional RDBMSs to Big Data systems. In the context of emerging database systems like MapReduce, NoSQL, and NewSQL, this paper offers well-organized suggestions for introducing SQL. The description of a variety of course tools, such as virtual machines, sample projects, and in-class exercises, to provide hands-on experience with SQL across a wide range of contemporary database systems is a fundamental addition of this work. Descriptors for Categories and Subjects General terms for computer and information science education in K.3.2 [Computers and Education] Creating and experimenting Keywords Curriculums for databases; SQL; Query Language; Big Data 1.

RL-instances: An alternative to conjunctive fuzzy sets of tuples for flexible querying in relational databases

In this paper we introduce RL-instances as a way to represent and manipulate gradual conjunctive collections of tuples. Our proposal, based on the theory of Representation by Levels, overcomes the limitations of fuzzy instances in keeping all the properties of conventional Relational Algebra operations. We provide a proof of concept implementation in a conventional relational database management system, where flexible queries can be performed on crisp tables using the same patterns that are used in conventional querying.

Compression Methodologies for Columnar Database Optimization

Today’s life is completely dependent on data. Conventional relational databases take longer to respond to queries because they are built for row-wise data storage and retrieval. Due to their efficient read and write operations to and from hard discs, which reduce the time it takes for queries to produce results, columnar databases have recently overtaken traditional databases. To execute Business Intelligence and create decision-making systems, vast amounts of data gathered from various sources are required in data warehouses, where columnar databases are primarily created. Since the data are stacked closely together, and the seek time is reduced, columnar databases perform queries more quickly. With aggregation queries to remove unnecessary data, they allow several compression techniques for faster data access. To optimise the efficiency of columnar databases, various compression approaches, including NULL Suppression, Dictionary Encoding, Run Length Encoding, Bit Vector Encoding, and Lempel Ziv Encoding, are discussed in this work. Database operations are conducted on the compressed data to demonstrate the decrease in memory needs and speed improvements.

MULTI-JOIN-ORDERING QUERY OPTIMIZATION ALGORITHM FOR HIVE WAREHOUSE WITH MAPREDUCE

According to the Digital Report of July, 2021, Billions of users around the world uses Mobile Phones, Internet, social media every second. This huge range of heterogeneous digital data is called Big Data, and is measured in terms of terabytes or petabytes. It is difficult to the conventional relational databases to handle these heterogeneous data for data analytics, but is still in use significantly in the growth of Big Data. To handle SQL-based structured queries, Hadoop is one of the prominent and well-suited solution that allows Big Data to be stored and processed. Hive support SQL queries on Hadoop. Hive warehouse, is the oldest SQL-engine on the top of the Hadoop framework and to store the processed data, it uses HDFS (Hadoop Distributed File System). On the Hadoop, MapReduce is an execution engine that executes SQL-based queries. In the Query Optimization, join ordering always plays a significant role because when the order of tables in joining operation is changed, execution time of the query is reduced to a greater extent. The main problem of the Hive is that it does not enhance the order of the join for an SQL-query and also does not give assurance for an optimal execution plan. Its time complexity is measured in exponential (Shan, Y., & Chen, Y., 2015).The main focus of this paper is to discover the finest join ordering solution for a Hive query optimization problem through appropriate search algorithms and to improve SQL-based Hive queries performance with MapReduce–based system.

Interoperability and Synchronization Management of Blockchain-Based Decentralized e-Health Systems

E-health systems have witnessed widespread usage in the last few years, mainly due to advancements in health monitoring hardware and the availability of remote diagnostic services. Given the numerous benefits, there are two main challenges: management of the e-health ecosystem and security and privacy of sensitive data. The former results from nonunified, redundant, and often replicated information across numerous independent e-health service providers. While the later stems from sensitive information stored in centralized systems that can be compromised. Blockchain has emerged as a promising technology, which can be used to secure access and privacy of data and provide an umbrella management solution to large-scale distributed and decentralized enterprise systems. In this article, we present a unified system for migrating independent conventional e-health systems to a single blockchain-based ecosystem. More specifically, we address the issues of difference in data structures for conventional relational databases and blockchain file databases. The solution describes the conversion process and synchronization of information in a unified system for large-scale e-health data. The implementation and analysis show that significant improvements in data storage, access control, and seamless migration can be achieved.

Big data for better Indian healthcare

Present healthcare breeds an immense amount of data with an exponentially growing rate. This data is not only voluminous but also complex in terms of the different formats because of the diverse sources of the data like Electronic Health Record, clinical data, streaming data from sensors, medical image data, biomedical signal data, laboratory data, etc., which have strained the capabilities of existing conventional relational database management systems. In such a scenario, big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information. To integrate and find the relationship among these unstructured types of data is a hot topic nowadays. Big data is the hope which can come to rescue in triumphing the insights from these oceanic quantities of data. Big data is a term which is expected to solve the problems of volume, velocity, and variety, mostly present in the healthcare data. This paper presents a new framework for implementing big data solutions in the healthcare sector, the potential opportunities and challenges are also discussed, particularly in the context of Indian healthcare.

Improving the energy efficiency of relational and NoSQL databases via query optimizations

As big data becomes the norm of various industrial applications, the complexity of database workloads and database system design has increased significantly. To address these challenges, conventional relational databases have been constantly improved and NoSQL databases such as MongoDB and Cassandra have been proposed and implemented to compete with SQL databases. In addition to traditional metrics such as response time, throughput, and capacity, modern database systems are posing higher requirements on energy efficiency due to the large volume of data that need to be stored, queried, updated, and analyzed. While decades of research in the database and data processing communities has produced a wealth of literature that optimize for performance, research on optimizations for energy efficiency has been historically overlooked and only a few studies have investigated the energy efficiency of database systems. To the best our knowledge, there are currently no comprehensive studies that analyze the impact of query optimizations on performance and energy efficiency across both relational and NoSQL databases. In fact, the energy behavior of many basic database operations (e.g. insertion, deletion, searching, update, indexing, etc) remains largely unknown due to the lack of accurate power measurement methodologies for various databases and queries. In this paper, we investigate a series of query optimization techniques for improving the energy-efficiency of relational databases and NoSQL databases. We use both widely acceptable benchmarks (e.g. Yahoo! Cloud Server Benchmark) and customized datasets (converted from ˜100GB of Twitter data) in our experiments to evaluate the effectiveness of various optimization techniques. We conduct cross database analysis on relational database (MySQL) and NoSQL based databases (MongoDB and Cassandra) to compare their performance and energy efficiency. Additionally, we study a variety of optimization techniques that can improve energy efficiency without compromising performance on the databases derived from the Twitter data. Using these techniques, we are able to achieve significant energy savings without performance degradation. Moreover, we investigate the impact of Dynamic Voltage and Frequency Scaling (DVFS) on the performance and energy efficiency of MySQL, MongoDB and Cassandra.

A Survey on Reducing Costs Vs Improving Outcomes and Opportunities in Health Care Field Using Big Data

In recent years, the total amount of medical data collected has grown exponentially, mainly because of the introduction of new medical technologies and the increased use of IT to manage medical health records of patients. These technologies have been introduced not only to provide higher quality of care but also to help solve some of the major healthcare problems in developed countries: the lack of productivity and efficiency, the increasing costs and the relatively low accessibility of the system. Big Data, or the capability to develop, analyze and visualize huge and complex data sets for various research interests and applications, has the promise to radically impact the speed and direction in tackling these problems. Health care field grows tremendously in last few decades. The health care field has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big Data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information. This paper basically studies the impact of implementing the big data solutions on the healthcare field, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care field.

Uma Ontologia CIDOC-CRM para o Museu Virtual da Emigração

In this article we describe the use of the international standard for museum ontologies, CIDOC-CRM, to build a concrete ontology for ME, a virtual museum for the Emigration phenomena. Although the digital document repository is a conventional relational database, more abstract data models should be used to enable conceptual navigation over the information to help end-users (the museum visitors) to extract knowledge from the navigation (the visit). Thus, we will emphasize the design of a Reduced CRM-compatible form to describe the objects of our assets (people that emigrate in some moment to some other country), and the relations holding among them that better express the emigration movements. We also discuss the choice of RDF to expose the ontology triples via Apache Jena Fuseki server, a SPARQL end-point accessible over HTTP, and an engine (Jena ARQ) to query the RDF data. Finally, it was possible to transform these data into RDF and apply SPARQL queries, returning the desired results.

BASE analysis of NoSQL database

NoSQL databases are designed to address performance and scalability requirements of web based application which cannot be addressed by traditional relational databases. Due to their contrast in priorities and architecture to conventional relational databases using SQL, these databases are referred as “NoSQL” databases since they ​incorporate lots of additional features in addition to the features of conventional databases. The relational databases strongly follow the ACID (Atomicity, Consistency, Isolation, and Durability) properties while the NoSQL databases follow BASE (Basically Available, Soft State, Eventual consistency) principles. This survey paper is an analytical study on BASE features of some of NoSQL databases.

A new approach for image databases design

This paper focuses on the methodologies to organize and structure image databases. Conventional relational database techniques are optimized to deal with textual and numeric data; however, they are not effective to handle image data. Some progresses have been made in developing new approaches to establish and use image databases, but the applications of these approaches are very labor-intensive, error-prone, and impractical to large-scale databases. In this paper, we propose a new approach to develop the structure of a large-scale image automatically. It is an integrated approach from existing technologies for the new application where the management of image data is focused. In addition, we present a solution to data indexing for the image database with different image types.

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Cloud computing enables a conventional relational database system's hardware to be adjusted dynamically according to query workload, performance and deadline constraints. One can rent a large amount of resources for a short duration in order to run complex queries efficiently on large-scale data with virtual machine clusters. Complex queries usually contain common subexpressions, either in a single query or among multiple queries that are submitted as a batch. The common subexpressions scan the same relations, compute the same tasks (join, sort, etc.), and/or ship the same data among virtual computers. The total time spent for the queries can be reduced by executing these common tasks only once. In this study, we build and use efficient sets of query execution plans to reduce the total execution time. This is an NP-Hard problem therefore, a set of robust heuristic algorithms, Branch-and-Bound, Genetic, Hill Climbing, and Hybrid Genetic-Hill Climbing, are proposed to find (near-) optimal query execution plans and maximize the benefits. The optimization time of each algorithm for identifying the query execution plans and the quality of these plans are analyzed by extensive experiments.

하둡 기반 빅 데이터 기법을 이용한 웹 서비스 데이터 처리 설계 및 구현

데이터를 구조화하여 사용하는 관계형 데이터베이스가 현재까지 데이터 관리에 가장 많이 사용되고 있다. 그러나 관계형 데이터베이스는 데이터가 증가되면 데이터를 저장하거나 조회할 때 읽기, 쓰기 연산 수행에 제약 조건이 발생되어 서비스가 느려지는 현상이 나타난다. 또 새로운 업무가 추가되면 데이터베이스 내 데이터는 증가되고 결국 이를 해결하기 위해 하드웨어의 병렬 구성, CPU, 메모리, 네트워크 등 추가적인 인프라 구성을 필요로 하게 된다. 본 논문에서는 관계형 데이터베이스의 데이터 증가로 느려지는 웹 정보서비스 개선을 위해 기존 관계형 데이터베이스의 데이터를 하둡 HDFS로 전송하고 이를 일원화하여 데이터를 재구성한 후 사용자에게 하둡 데이터 처리로 대량의 데이터를 빠르고 안전하게 추출하는 모델을 구현한다. 본 시스템 적용을 위해 웹 기반 민원시스템과 비정형 데이터 처리인 이미지 파일 저장에 본 제안시스템을 적용하였다. 적용결과 관계형 데이터베이스 시스템보다 제안시스템 데이터 처리가 0.4초 더 빠른 결과를 얻을 수 있었고 기존 관계형 데이터베이스와 같은 대량의 데이터를 처리를 빅 데이터 기법인 하둡 데이터 처리로도 웹 정보서비스를 지원이 가능하였다. 또한 하둡은 오픈소스로 제공되어 소프트웨어 구매 비용을 줄여주는 장점이 있으며 기존 관계형 데이터베이스의 데이터 증가로 효율적인 대용량 데이터 처리를 요구하는 조직에게 도움을 줄 수 있을 것이다. Relational databases used by structuralizing data are the most widely used in data management at present. However, in relational databases, service becomes slower as the amount of data increases because of constraints in the reading and writing operations to save or query data. Furthermore, when a new task is added, the database grows and, consequently, requires additional infrastructure, such as parallel configuration of hardware, CPU, memory, and network, to support smooth operation. In this paper, in order to improve the web information services that are slowing down due to increase of data in the relational databases, we implemented a model to extract a large amount of data quickly and safely for users by processing Hadoop Distributed File System (HDFS) files after sending data to HDFSs and unifying and reconstructing the data. We implemented our model in a Web-based civil affairs system that stores image files, which is irregular data processing. Our proposed system's data processing was found to be 0.4 sec faster than that of a relational database system. Thus, we found that it is possible to support Web information services with a Hadoop-based big data processing technique in order to process a large amount of data, as in conventional relational databases. Furthermore, since Hadoop is open source, our model has the advantage of reducing software costs. The proposed system is expected to be used as a model for Web services that provide fast information processing for organizations that require efficient processing of big data because of the increase in the size of conventional relational databases.

Research and Implementation of Relational Database Encryption Technology Based on Main Memory Database

As the relational database is applied widely, how to protect the sensitive data and state secrets has become a problem which needs to be solved urgently. The conventional relational database encryption technology not only reduces the system’s performance, but also results that we can not quickly inquire the encryption data through the original index mechanism due to the loss of the original partial order relation among data. This paper proposes a kind of relational database encryption technology based on the main memory database. It has not only realized the transparent encryption, decryption and ciphertext storage of the relational database, but also still retained the original response and retrieval performance. This technical proposal has very strong adaptability as well as strong practice guidance meaning for protecting the internet business data and the war-industry informative state secrets.

Alternatives to relational database: Comparison of NoSQL and XML approaches for clinical data storage

Clinical data are dynamic in nature, often arranged hierarchically and stored as free text and numbers. Effective management of clinical data and the transformation of the data into structured format for data analysis are therefore challenging issues in electronic health records development. Despite the popularity of relational databases, the scalability of the NoSQL database model and the document-centric data structure of XML databases appear to be promising features for effective clinical data management. In this paper, three database approaches – NoSQL, XML-enabled and native XML – are investigated to evaluate their suitability for structured clinical data. The database query performance is reported, together with our experience in the databases development. The results show that NoSQL database is the best choice for query speed, whereas XML databases are advantageous in terms of scalability, flexibility and extensibility, which are essential to cope with the characteristics of clinical data. While NoSQL and XML technologies are relatively new compared to the conventional relational database, both of them demonstrate potential to become a key database technology for clinical data management as the technology further advances.

Answering biological questions by querying k‐mer databases

SUMMARYThis paper describes a k‐mer approach to analysing DNA data and quickly answering certain types of ad hoc biological questions. These k‐mers (short DNA strings) are stored in a conventional relational database and indexed to support efficient exact match operations. We show that k‐mers around 20–25 bases long have interesting and useful uniqueness properties that can be used to compute a ‘relatedness’ metric and also allow k‐mers to be used as ‘unique enough’ tags to identify organisms and genes. This relatedness metric is used in SQL queries that can directly answer questions such as how two related species differ, and what genes are unique to an organism. The k‐mer tags have proven useful in applications, largely metagenomic ones that can quickly process large volumes of sequencing data to say something about what organisms and genes might be present in an environmental sample. All of this work is based on simple and fast exact matches of k‐mer strings using a database, rather than conventional alignment based on inexact matches of much longer strings. These k‐mer tools provide ways of rapidly exploring large genome spaces and handling large volumes of sequence data, and complement rather than replace existing alignment and assembly tools. Copyright © 2012 John Wiley & Sons, Ltd.