scholarly journals A Paged Prefetching Model for Join Operations of Cross-Databases

2020 ◽  
Vol 38 (6) ◽  
pp. 1225-1234
Author(s):  
Changqing Li ◽  
Jianhua Gu
Keyword(s):  
Big Data ◽  
Query Language ◽  
Research Result ◽  
Management Systems ◽  
Prototype System ◽  
Large Delay ◽  
Basic Composition ◽  
Operation Mechanism ◽  
Nosql Database ◽  
Research Hotspots

As the applications of big data are increasing, the NoSQL (Not only Structured Query Language) database management systems have been developed rapidly. How to integrate NoSQL database and relational database effectively has become one of the research hotspots. In the existing research results, the paged query method used for join operations of these heterogeneous multi-databases can produce a large delay. In view of this deficiency, this paper presents a paged prefetching model, and focuses on its basic composition, prefetching mode and operation mechanism. A prototype system is designed and developed. The effect of this model is verified, and the expected targets are achieved. Compared with the non-prefetched paging query method, the outstanding contribution of this research result is that it can reduce the delay of paging query and thus improve the efficiency of the join operations of cross-databases.

scholarly journals A Comparative Study of NoSQL and Relational Database

2017 ◽  
Vol 1 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Douglas Kunda ◽  
Hazael Phiri
Keyword(s):  
Big Data ◽  
Web 2.0 ◽  
Relational Database ◽  
Relational Databases ◽  
Query Language ◽  
The Other ◽  
Database Model ◽  
Nosql Database ◽  
Database Models ◽  
Year 2000

Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the challenges of NoSQL. Finally, the paper concludes by determining whether Relational Databases would completely be replaced by NoSQL database models. The findings revealed that, Relational Databases are based on ACID model which emphasizes better consistency, security and offers a standard query language. However, Relational Databases have poor scalability, weak performance, cost more, face availability challenges when supporting large number of users and handle limited volume of data. NoSQL, on the other hand is based on the BASE model, which emphasizes greater scalability and provides a flexible schema, offers better performance, mostly open source, cheap but, lacks a standard query language and does not provide adequate security mechanisms. Both databases will continue to exist alongside each other with none being better than the other. The choice of the database to use will depend on the nature of the application being developed. Each database type has its own challenges and strengths, with relational database lacking of support for unstructured data while NoSQL lacks standardization and has poor security. Modern applications in web 2.0, 3.0 and big data are well suited to use NoSQL but, there are still many applications that rely on Relational Databases.

scholarly journals Dynamic load balancing algorithm for large data flow in distributed complex networks

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 706-716
Author(s):  
Zhuo Zhang
Keyword(s):  
Big Data ◽  
Load Balancing ◽  
Information Society ◽  
Relational Databases ◽  
Query Language ◽  
Large Data ◽  
Nosql Database ◽  
Equalization Algorithm ◽  
Load Balancing Algorithm ◽  
The Relationship

Abstract Information society brings convenience to people, but also produces a lot of data. Relational databases are not suitable for processing big data due to architecture defects. The most commonly used system to store and process large amounts of data is the NoSQL (Not only Structured Query Language) database. Obviously, it is very important to cooperate with these independent computers to accomplish processing tasks efficiently, which is the function of load balancing. This paper studies the commonly used NoSQL database and load balancing algorithms, and designs and implements a more efficient load balancing algorithm. By introducing the relationship between nodes and the children of their brother nodes, we reduce the height of the whole sorted binary tree. The time cost of the algorithm is reduced versus the commonly used weighted polling algorithm O(N) to O(log N), while the spatial cost remains unchanged. The equalization algorithm synthetically utilizes the characteristics of big data processing systems and has good performance. At the same time, the algorithm can quickly find the sub-optimal nodes when the optimal nodes have been occupied, so it is very suitable for load balancing in highly concurrent systems. Finally, the effectiveness of the proposed load balancing algorithm is verified by simulation.

Research Hotspots and Trends Visualization Analysis of Chinese E-commerce Big Data

2021 ◽  
Author(s):  
Tingting Lu ◽  
Jiandong Zhao ◽  
Xiongna Deng ◽  
Lirong Dong ◽  
Peng Huang
Keyword(s):  
Big Data ◽  
Visualization Analysis ◽  
Research Hotspots

scholarly journals Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

2020 ◽  
Vol 4 (3) ◽  
pp. 577-577
Author(s):  
Vania V Estrela
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Query Language ◽  
Treatment Decisions ◽  
Cyber Physical Systems ◽  
Detailed Knowledge ◽  
Data Handling ◽  
Search Query ◽  
Physical Systems ◽  
Database As A Service

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse. Methods: BCPSs attributes are presented and discussed.  One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs.  Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways.

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

scholarly journals Devising Enabling Spaces and Affordances for Personal Knowledge Management System Design

10.28945/3744 ◽  
2017 ◽  
Author(s):  
Ulrich Schmitt
Keyword(s):  
Knowledge Management ◽  
Personal Growth ◽  
Design Science ◽  
Rapid Development ◽  
Science Research ◽  
Personal Knowledge ◽  
Prototype System ◽  
Development Platform ◽  
Personal Knowledge Management ◽  
Nosql Database

[This Proceedings paper was revised and published in Informing Science: the International Journal of an Emerging Transdiscipline (InfoSci)] Aim/Purpose: Personal Knowledge Management (PKM) has been envisaged as a crucial tool for the growing creative class of knowledge workers, but adequate technological solutions have not been forthcoming. Background: Based on former affordance-related publications (primarily concerned with communication, community-building, collaboration, and social knowledge sharing), the common and differing narratives in relation to PKM are investigated in order to suggest further PKM capabilities and affordances in need to be conferred. Methodology: The paper follows up on a series of the author’s PKM-related publications, firmly rooted in design science research (DSR) methods and aimed at creating an innovative PKM concept and prototype system. Contribution: The affordances presented offer PKM system users the means to retain and build upon knowledge acquired in order to sustain personal growth and facilitate productive collaborations between fellow learners and/or professional acquaintances. Findings: The results call for an extension of Nonaka’s SECI model and ‘ba’ concept and provide arguments for and evidence supporting the claims that the PKM concept and system is able to facilitate better knowledge traceability and KM practices. Recommendations and Impact on Society: Together with the prior publications, the paper points to current KM shortcomings and presents a novel trans-disciplinary approach offering appealing opportunities for stakeholders engaged in the context of curation, education, research, development, business, and entrepreneurship. Its potential to tackle opportunity divides has been addressed via a PKM for Development (PKM4D) Framework. Future: DSR Activities After completing the test phase of the prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

DocBase

2013 ◽  
pp. 365-393
Author(s):  
Arijit Sengupta ◽  
Ramesh Venkataraman
Keyword(s):  
Query Processing ◽  
Query Language ◽  
Formal Model ◽  
Query Languages ◽  
Prototype System ◽  
Storage And Retrieval ◽  
Visual Query Formulation ◽  
Visual Query ◽  
Nested Relations ◽  
Entity Relationship

This chapter introduces a complete storage and retrieval architecture for a database environment for XML documents. DocBase, a prototype system based on this architecture, uses a flexible storage and indexing technique to allow highly expressive queries without the necessity of mapping documents to other database formats. DocBase is an integration of several techniques that include (i) a formal model called Heterogeneous Nested Relations (HNR), (ii) a conceptual model XER (Extensible Entity Relationship), (ii) formal query languages (Document Algebra and Calculus), (iii) a practical query language (Document SQL or DSQL), (iv) a visual query formulation method with QBT (Query By Templates), and (v) the DocBase query processing architecture. This paper focuses on the overall architecture of DocBase including implementation details, describes the details of the query-processing framework, and presents results from various performance tests. The paper summarizes experimental and usability analyses to demonstrate its feasibility as a general architecture for native as well as embedded document manipulation methods.

Building OLAP Cubes From Columnar NoSQL Data Warehouses

2019 ◽  
pp. 129-157
Author(s):  
Khaled Dehdouh
Keyword(s):  
Big Data ◽  
Database System ◽  
Massive Data ◽  
Data Warehouses ◽  
Online Analysis ◽  
Storage Model ◽  
Data Cubes ◽  
Nosql Database ◽  
Oriented Approach

In the big data warehouses context, a column-oriented NoSQL database system is considered as the storage model which is highly adapted to data warehouses and online analysis. Indeed, the use of NoSQL models allows data scalability easily and the columnar store is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). To build OLAP cubes corresponding to the analysis contexts, the most common way is to integrate other software such as HIVE or Kylin which has a CUBE operator to build data cubes. By using that, the cube is built according to the row-oriented approach and does not allow to fully obtain the benefits of a column-oriented approach. In this chapter, the main contribution is to define a cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes according to the columnar approach by taking into account the non-relational and distributed aspects when data warehouses are stored.

Architecture for Big Data Storage in Different Cloud Deployment Models

2021 ◽  
pp. 178-208
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Data Services ◽  
Big Data Applications ◽  
Nosql Database ◽  
Amazon Web Services ◽  
Product Domains ◽  
Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

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