Traditional Relational Database Systems Research Articles

The main categories of NewSQL databases and their features

In the modern world, the problem of working with big data and workloads is becoming more and more acute. For more than forty years, relational databases have been the main leading systems for storing, searching and managing data. However, despite their great popularity, application experience and universality, traditional relational DBMS, due to the growing needs for scalability and performance, often cannot meet modern requirements. This has led to the emergence of new alternative data management systems, including NewSQL systems. NewSQL is a class of modern relational database management systems that provide performance comparable to NoSQL systems while maintaining the data consistency guarantees inherent in traditional database systems. The growing interest in NewSQL technology in recent times has led to an increase number of evaluations and comparisons among competing NewSQL technologies. However, today there is still a certain lack of work devoted to the study of the features of NewSQL solutions and their capabilities in comparison with other technologies. This paper discusses the main features of the most famous NewSQL products of different categories and the identified problems associated with them. To overcome the certain ambiguity in the names and translations of some terms related to the subject area under consideration, which takes place in numerous relevant sources, additional explanations are given. For comparison, the paper presents the values of important characteristics inherent in NewSQL, traditional relational and NoSQL database systems. This paper can help researchers and people from the industry choose the best storage solutions for their needs.

A workload-driven method for designing aggregate-oriented NoSQL databases

Due to the scalability and availability problems with traditional relational database systems, a variety of NoSQL stores have emerged over the last decade to deal with big data. How data are structured in a NoSQL store has a large impact on the query and update performance and the storage usage. Thus, different from the traditional database design, not only the data structure but also the data access patterns need to be considered in the design of NoSQL database schemas. In this paper, we present a general workload-driven method for designing key-value, wide-column, and document NoSQL database schemas. We first present a generic logical model Query Path Graph (QPG) that can represent the data structures of the UML class diagram. We also define mappings from the SQL-based query patterns to QPG and from QPG to aggregate-oriented NoSQL schemas. We use a cost model to measure the query and update performance and optimize the QPG schemas. We evaluate the proposed method with several typical case studies by simulating workloads on databases with different schema designs. The results demonstrate that our method preserves the generality and the quality of the design.

$$\varvec{\textsc {Orpheus}}$$DB: bolt-on versioning for relational databases (extended version)

Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently store, track, and query across dataset versions. We introduce OrpheusDB, a dataset version control system that “bolts on” versioning capabilities to a traditional relational database system, thereby gaining the analytics capabilities of the database “for free.” We develop and evaluate multiple data models for representing versioned data, as well as a lightweight partitioning scheme, LyreSplit, to further optimize the models for reduced query latencies. With LyreSplit, OrpheusDB is on average $$10^3\times $$ faster in finding effective (and better) partitionings than competing approaches, while also reducing the latency of version retrieval by up to $$20\times $$ relative to schemes without partitioning. LyreSplit can be applied in an online fashion as new versions are added, alongside an intelligent migration scheme that reduces migration time by $$10\times $$ on average.

Regulatory Architecture of Data Processing for Connected and Automated Driving in Europe

The beginning of the 2020s ought to reflect a steady conclusion of the vast majority of the European Union’s projects with regards to the new era of connectivity and mobility within the European Union dimension. We expect Intelligent Connected Vehicles (ICVs) to step into free circulation within the internal market. Since the operation of the ICVs depends on the number of data processing operations, data processing operations should be precisely determined and framed beforehand. ICVs data operations consist of extraordinarily large volumes and velocity of a data flow which previously existed in traditional relational database systems and could not have been processed within the desired timeframe. Even though the currently adopted database systems are ready to face the new level of data processing, a huge data stream is also faced with complex obstacles and new risks which have never been experienced beforehand.While seeking to ensure safe and secure introduction of a new level of data processing for connectivity and automation at the European Union market, the author precisely examines all potential risks and possibilities of integration into a uniform legal regulation to ensure secured ICVs data processing at all levels. The regulatory framework should document adequate security requirements and defences against ICVs attacks e.g. interference and remote-control interception.

Performance Evaluation of Cloud Based Query Processing with MongoDB and Cassandra on Assorted Parameters

With the transmission of enormous data from multiple channels, the issues are rising related to big data and associated performance of storage engines on cloud environment. These datasets and signals are getting transmitted on assorted domains including social media, live streaming of multimedia, satellites, Internet of Things (IoT) based monitoring and many others. To process and execute such huge data, there is need to incorporate specialized and high performance database engines because of concurrency, integrity and security issues. The traditional relational database systems are not effective in handling such heterogeneous as well as big data flowing on cloud. From last few years, the advent of NoSQL databases attempts to provide a secured and high performance system using which millions of database queries per second can be processed. Even, these database systems can handle unstructured data in multiple formats without any compatibility issues. There are more than hundred NoSQL databases which are prominently used in different applications. In this research manuscript, the evaluation of two database systems MongoDB and Cassandra is done on query execution time to evaluate the relative performance with the pragmatic analysis. From the results, it is found that Cassandra NoSQL is quite effectual in terms of minimum execution time as compared to MongoDB with the excellent powers towards throughput with high write and no downtime.

A Survey on NoSQL Stores

Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.

Semi-structured data analysis and visualisation using NoSQL

In the field of computing, every day huge amounts of data are created by scientific experiments, companies and users' activities. These large datasets are labelled as 'big data', presenting new challenges for computer science researchers and professionals in terms of storage, processing and analysis. Traditional relational database systems (RDBMS) supported with conventional searches cannot be effectively used to handle such multi-structured data. NoSQL databases complement to the challenges of managing RDBMS with big data and facilitate in further analysis of data. In this paper, we introduce a framework that aims at analysing semi-structured data applications using NoSQL database MongoDB. The proposed framework focuses on the key aspects needed for semi-structured data analytics in terms of data collection, data parsing and data prediction. The layers involved in the framework are request layer facilitating the queries from user, input layer that interfaces the data sources and the analytics layer; and the output layer facilitating the visualisation of the analytics performed. A performance analysis for select+fetch operations needed for analytics, of MySQL and MongoDB is carried out where NoSQL database MongoDB outperforms MySQL database. The proposed framework is applied on predicting the performance and monitoring of cluster of servers.

O rpheus DB

Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently store, track, and query across dataset versions. We introduce O rpheus DB, a dataset version control system that " bolts on " versioning capabilities to a traditional relational database system, thereby gaining the analytics capabilities of the database "for free". We develop and evaluate multiple data models for representing versioned data, as well as a light-weight partitioning scheme, L yre S plit , to further optimize the models for reduced query latencies. With L yre S plit , O rpheus DB is on average 10 3 × faster in finding effective (and better) partitionings than competing approaches, while also reducing the latency of version retrieval by up to 20× relative to schemes without partitioning. L yre S plit can be applied in an online fashion as new versions are added, alongside an intelligent migration scheme that reduces migration time by 10× on average.

An OAIS-Based Hospital Information System on the Cloud: Analysis of a NoSQL Column-Oriented Approach.

The Open Archive Information System (OAIS) is a reference model for organizing people and resources in a system, and it is already adopted in care centers and medical systems to efficiently manage clinical data, medical personnel, and patients. Archival storage systems are typically implemented using traditional relational database systems, but the relation-oriented technology strongly limits the efficiency in the management of huge amount of patients' clinical data, especially in emerging cloud-based, that are distributed. In this paper, we present an OAIS healthcare architecture useful to manage a huge amount of HL7 clinical documents in a scalable way. Specifically, it is based on a NoSQL column-oriented Data Base Management System deployed in the cloud, thus to benefit from a big tables and wide rows available over a virtual distributed infrastructure. We developed a prototype of the proposed architecture at the IRCCS, and we evaluated its efficiency in a real case of study.

The Use of Big Data Analytics in Medical Applications

The field of Big Data Analytics does not have a linear capacity for growth. It is based on a specified structure. Big data is now most useful for data backup purposes, rather than for anything else. Big Data is a collection of data sets that are both numerous and complicated in nature, and it is becoming increasingly popular. They consist of both organized and unstructured data that is constantly changing at a rate that is inconvenient for traditional relational database systems and existing analytical tools to keep pace with. There is constantly some new information being introduced. It also contributes to the resolution of India's major concerns. It also contributes to closing the data gap. Healthcare is the preservation or advancement of health by the prevention, interpretation, and medical treatment of the disorder, ill health, abuse, and other significant physical, mental, and spiritual degeneration in the mortal body. Health care is conveyed by health professionals in the form of unified health experts, specialists, physician associates, midwives, nurses, antibiotics, pharmacy, psychology, and other health-related fields of expertise. Additionally, it has an introduction, challenging elements and concerns, Big Data Analytics in use, technical specifications, research applications, industrial applications, and future applications. This article aims to provide knowledge in the field of big data analytics and its use in the medical arena.

Search Ranking for Heterogeneous Data over Dataspace

Traditional relational database systems queries works over structured data, whereas information retrieval systems are designed for additional versatile and flexible ranked keyword queries, works over unstructured data, Semi-structured, Streamed data, Social networking data and data without any format, known as heterogeneous data. However, several new and emerging applications need data management capabilities that mix the advantages both approaches. In this paper, we have proposed and initiate steps to combine heterogeneous statistics and information retrieval systems over Dataspace, which are the collection heterogeneous data, data from various sources and in different format. In several enterprise, the heterogeneity among information at different levels has becomes a difficult job. In an organization, data exist in structured, semi-structured or unstructured format or combination of all these. The existing heterogeneous data management systems are unsuccessful to deal with such information in efficient manner. Dataspace approach gives the solution of the problem of presence of heterogeneity in information and a variety of drawbacks of the existing systems. The main motive of this paper is to explain searching ranking mechanism in Dataspace. We also investigate how structured, semi structured or unstructured data can be take advantages for ranking of search on Web and Dataspace with their research challenges.

Operational NoSQL Systems: What's New and What's Next?

Operational NoSQL systems are relatively new in the data-management ecosystem, and there is much confusion about their capabilities and how they differ from traditional relational database systems. This summary of characteristics clearly distinguishes the two system classes and provides a glimpse into directions for future work.

Database-as-a-Service for Big Data: An Overview

The last two decades were marked by an exponential growth in the volume of data originating from various data sources, from mobile phones to social media contents, all through the multitude devices of the Internet of Things. This flow of data can’t be managed using a classical approach and has led to the emergence of a new buzz word: Big Data. Among the research challenges related to Big Data there is the issue of data storage. Traditional relational database systems proved to be unable to efficiently manage Big Data datasets. In this context, Cloud Computing plays a relevant role, as it offers interesting models to deal with Big Data storage, especially the model known as Database as a Service (DBaaS). We propose, in this article, a review of database solutions that are offered as DBaaS and discuss their adaptability to Big Data applications.

A Transformation from Relational Databases to Big Data

We are living in an information age and there is enormous amount of data that is flowing between systems, internet, telephones, and other media. The data is being collected and stored at unprecedented rates. There is a great challenge not only to store and manage the large volume of data, but also to analyze and extract meaningful information from it. There are several approaches to collecting, storing, processing, and analyzing big data. The main focus of the paper is to draw an analogy for data management between the traditional relational database systems and the Big Data.

Profiling and analyzing the I/O performance of NoSQL DBs

The advent of the so-called NoSQL databases has brought about a new model of using storage systems. While traditional relational database systems took advantage of features offered by centrally-managed, enterprise-class storage arrays, the new generation of database systems with weaker data consistency models is content with using and manag- ing locally attached individual storage devices and providing data reliability and availability through high-level software features and protocols. This tutorial aims to review the architecture of selected NoSQL DBs to lay the foundations for understanding how these new DB systems behave. In particular, it focuses on how (in)efficiently these new systems use I/O and other resources to accomplish their work. The tutorial examines the behavior of several NoSQL DBs with an emphasis on Cassandra - a popular NoSQL DB system. It uses I/O traces and resource utilization profiles captured in private cloud deployments that use both dedicated directly attached storage as well as shared networked storage.

I/O characteristics of NoSQL databases

The advent of the so-called NoSQL databases has brought about a new model of using storage systems. While traditional relational database systems took advantage of features offered by centrally-managed, enterprise-class storage arrays, the new generation of database systems with weaker data consistency models is content with using and managing locally attached individual storage devices and providing data reliability and availability through high-level software features and protocols. This work aims to review the architecture of several existing NoSQL DBs with an emphasis on how they organize and access data in the shared-nothing locally-attached storage model. It shows how these systems operate under typical workloads (new inserts and point and range queries), what access characteristics they exhibit to storage systems. Finally, it examines how several recently developed key/value stores, schema-free document storage systems, and extensible column stores organize data on local filesystems on top of directly-attached disks and what system features they must (re)implement in order to provide the expected data reliability.

PIQL

Newly-released web applications often succumb to a "Success Disaster," where overloaded database machines and resulting high response times destroy a previously good user experience. Unfortunately, the data independence provided by a traditional relational database system, while useful for agile development, only exacerbates the problem by hiding potentially expensive queries under simple declarative expressions. As a result, developers of these applications are increasingly abandoning relational databases in favor of imperative code written against distributed key/value stores, losing the many benefits of data independence in the process. Instead, we propose PIQL, a declarative language that also provides scale independence by calculating an upper bound on the number of key/value store operations that will be performed for any query. Coupled with a service level objective (SLO) compliance prediction model and PIQL's scalable database architecture, these bounds make it easy for developers to write success-tolerant applications that support an arbitrarily large number of users while still providing acceptable performance. In this paper, we present the PIQL query processing system and evaluate its scale independence on hundreds of machines using two benchmarks, TPC-W and SCADr.

Tree-Structured Data Processing Platform for Large-Scale Data Mining

We propose a data processing platform that can analyze a large amount of tree-structured data. The proposed platform stores tree-structured data in separated files corresponding to each attribute, and uses MapReduce framework for distributed computing. These methods enable to reduce disk I/O load, and to avoid computationally-intensive processing, such as grouping or combining of records. An early stage of data mining needs try-and-error processes to find out how to analyze and utilize the data. Our platform speeds up computations of the try-and-error processes, such as appending new attributes and calculating statistics of attributes. Experimental results show that the proposed methods are efficient to process large-scale tree-structure data, and our platform is comparable or superior to a traditional relational database system. With the proposed platform, it became possible to process 90 GB data within 5 minutes on 6 benchmark tasks. We also describe system architecture for the try-and-error phase, which integrates the proposed platform and a few Web applications. The main contributions of this paper are: (1) formulation of vertical partitioning for tree-structured data, (2) effective utilization of MapReduce, and (3) construction of large-scale data mining system for a try-and-error phase.

NeuroSys: a semistructured laboratory database.

The inherent complexity of traditional relational database systems is a key obstacle to more widespread use of database technology in the neuroscience community. As an alternative to relational technology, we propose a simpler semistructured data model for documenting laboratory procedures and results. The semistructured data model allows researchers to document their data in an organized, regularly formatted, machine-readable, and network accessible manner, without requiring the services of database professionals. We present proof-of-concept software, consisting of an HTML interface that communicates with a remotely located, semistructured database. We also discuss the importance of standardized terminology and the importance of building flexible data description systems that are more easily adapted and reconfigured to conform with standardized terminologies as they evolve.

A meta modeling approach to workflow management systems supporting exception handling

Workflow Management Systems (WFMSs) facilitate the definition of structure and decomposition of business processes and assists in management of coordinating, scheduling, executing and monitoring of such activities. Most of the current WFMSs are built on traditional relational database systems and/or using an object-oriented database system for storing the definition and run time data about the workflows. However, a WFMS requires advanced modeling functionalities to support adaptive features, such as on-line exception handling. This article describes our advanced meta-modeling approach using various enabling technologies (such as object orientation, roles, rules, active capabilities) supported by an integrated environment, the ADOME, as a solid basis for a flexible WFMS involving dynamic match making, migrating workflows and exception handling.