Comparison of query performance in relational a non-relation databases

Čerešňák, Roman; Kvet, Michal

doi:10.1016/j.trpro.2019.07.027

Cited by 39 publications

(16 citation statements)

References 6 publications

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“…Finally, Table A2 shows a benchmark of operation performance for different database technologies [45]. After this study, the database technology selected to store the data extracted from documents was MongoDB [46], as shown in the definition and implementation of the system.…”

Section: Discussionmentioning

confidence: 99%

“…The main operations that are performed in the database are the insertion and selection of items. As these operations are being carried out thousands of times, the execution time of these actions needs to be as short as possible [45]. For this reason, MongoDB (v4.0.8, open source) [46] is a very efficient database technology oriented to store documents and perform text mining operations.…”

Section: Stage 2: Creation Of An Input Datasetmentioning

confidence: 99%

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Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

et al. 2020

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Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.

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Section: Discussionmentioning

confidence: 99%

Section: Stage 2: Creation Of An Input Datasetmentioning

confidence: 99%

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

et al. 2020

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“…The advantages of column-oriented systems are as follows [1,2,5,9]:  A high degree of scalabilityin essence, columnoriented systems have the property of scalability, which means that when there is a high intensity of data addition, more general-purpose nodes have to be added to the cluster. With a good design of the cluster can be achieved a linear relation between the way the data is expanding and the number of the needed nodes.…”

Section: Column-oriented Data Model Propertiesmentioning

confidence: 99%

“…In the column store, each attribute is stored in a separate column, so that the sequential values of this attribute are stored sequentially on disk [4,9]. This data model provides more opportunities for improved performance as a result of applying algorithms to compress data elements compared to roworiented architectures.…”

Section: Introductionmentioning

confidence: 99%

Impact of Data Compression on the Performance of Column-oriented Data Stores

Mladenova¹,

Kalmukov²,

Marinov³

et al. 2021

IJACSA

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Compression of data in traditional relational database management systems significantly improves the system performance by decreasing the size of the data that results in less data transfer time within the communication environment and higher efficiency in I/O operations. The column-oriented database management systems should perform even better since each attribute is stored in a separate column, so that its sequential values are stored and accessed sequentially on the disk. That further increases the compression efficiency as the entire column is compressed/decompressed at once. The aim of this research is to determine if data compression could improve the performance of HBase, running on a small-sized Hadoop cluster, consisted of one name node and nine data nodes. Test scenario includes performing Insert and Select queries on multiple records with and without data compression. Four data compression algorithms are tested since they are natively supported by HBase -SNAPPY, LZO, LZ4 and GZ. Results show that data compression in HBase highly improves system performance in terms of storage saving. It shrinks data 5 to 10 times (depending on the algorithm) without any noticeable additional CPU load. That allows smaller but significantly faster SSD disks to be used as cluster's primary data storage. Furthermore, the substantial decrease in the network traffic is an additional benefit with major impact on big data processing.

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“…Jadi dalam hal ini basis data NoSQL akan lebih baik untuk diterapkan pada ERP Retail. Penelitian yang membandingkan SQL dan noSQL yang lain dilakukan [5], [6], [7] Cara kerja basis data noSQL dengan menggunakan berbagai model basis data untuk mengelola dan mengakses data, seperti dokumen, key-value, grafik, inmemory dan search-engine. Jenis basis data NoSQL diantaranya monggoDb, , CouchDB, Cassandra, Neo4j.…”

Section: Pendahuluanunclassified

Pemodelan Basis data Graph dengan Neo4j (Studi Kasus : Basis Data Sistem Informasi Penjualan pada UMKM)

Sholeh

Rachmawati

Susanti

2020

J-TIT

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Penelitian ini mengimplementasikan penyimpanan data dengan menggunakan basis data graph. Basis data graph merupakan salah satu kategori dari basis data noSQL. Dalam basis data model SQL data dibentuk dalam tabel –tabel yang terdiri dari baris dan kolom, sedangkan pada basis data model NoSQL data tidak memiliki skema standar yang harus didefinisikan. NoSql merupakan sistem manajemen basis data yang tidak mempunyai atau mematuhi aturan tertentu seperti pada model sistem manajemen basis data relasional. NoSQL memiliki skema yang dinamis sedangkan pada database SQL mengikuti skema yang telah ditetapkan pada awal perancangan. Pengembangan basis data graph sistem penjualan UMKM ini dilakukan dengan mengembangkan terlebih dahulu dalam konseptual dan membandingkan dengan membuat terlebih dahulu model database relasional dan model database graph. Hasil penelitian ini menghasilkan basis data graph yang mengelola data-data penjualan serta menghubungkan berbagai simpul yang

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Comparison of query performance in relational a non-relation databases

Cited by 39 publications

References 6 publications

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

Impact of Data Compression on the Performance of Column-oriented Data Stores

Pemodelan Basis data Graph dengan Neo4j (Studi Kasus : Basis Data Sistem Informasi Penjualan pada UMKM)

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