Blockchain-based database

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The blockchain-based database is a combination of traditional database and distributed database where data is transacted and recorded via Database Interface [1] (also known as Compute Interface) [2] supported by multiple-layers of blockchains. [3] The database itself is shared in the form of an encrypted/immutable ledger which makes the information open for everyone. [4]

Contents

Concept

In actual case, the blockchain essentially has no querying abilities when compared to traditional database and with a doubling of nodes, network traffic quadruples with no improvement in throughput, latency, or capacity. [5] To overcome these shortcomings, taking a traditional database and adding blockchain features to it sounds more feasible. [6] That's how the concept of blockchain-based database came into existence, which consists of multiple member clouds riding on two primary layers; the first one is Database Interface and the second one is the Blockchain Anchoring. [1] The idea behind the blockchain based database concept is to complement the functionality and features of SQL and NoSQL databases with blockchain properties: data immutability, integrity assurance, decentralized control, Byzantine fault tolerance and transaction traceability. [7]

Iterations

Related Research Articles

In computing, online analytical processing, or OLAP, is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.

A query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. In database systems, query languages rely on strict theory to retrieve information. A well known example is the Structured Query Language (SQL).

<span class="mw-page-title-main">MonetDB</span> Open source column-oriented relational database management system

MonetDB is an open-source column-oriented relational database management system (RDBMS) originally developed at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. It is designed to provide high performance on complex queries against large databases, such as combining tables with hundreds of columns and millions of rows. MonetDB has been applied in high-performance applications for online analytical processing, data mining, geographic information system (GIS), Resource Description Framework (RDF), text retrieval and sequence alignment processing.

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Ontotext is a software company that produces software relating to data management. Its main products are GraphDB, an RDF database; and Ontotext Platform, a general data management platform based on knowledge graphs. It was founded in 2000 in Bulgaria, and now has offices internationally. Together with the BBC, Ontotext developed one of the early large-scale industrial semantic applications, Dynamic Semantic Publishing, starting in 2010.

<span class="mw-page-title-main">Apache Jena</span> Open source semantic web framework for Java

Apache Jena is an open source Semantic Web framework for Java. It provides an API to extract data from and write to RDF graphs. The graphs are represented as an abstract "model". A model can be sourced with data from files, databases, URLs or a combination of these. A model can also be queried through SPARQL 1.1.

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NoSQL is an approach to database design that focuses on providing a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Instead of the typical tabular structure of a relational database, NoSQL databases house data within one data structure. Since this non-relational database design does not require a schema, it offers rapid scalability to manage large and typically unstructured data sets. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.

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Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.

A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions.

NitrosBase is a Russian high-performance multi-model database system. The database system supports relational, graph and document database models.

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Vadalog is a system for performing complex logic reasoning tasks over knowledge graphs. Its language is based on an extension of the rule-based language Datalog, Warded Datalog±.

References

  1. 1 2 Gaetani, Edoardo; Aniello, Leonardo; Baldoni, Roberto; Lombardi, Federico; Margheri, Andrea; Sassone, Vladimiro (2017). "Blockchain-based database to ensure data integrity in cloud computing environments" (PDF). Sun SITE Central Europe operated under the umbrella of RWTH Aachen University . Edoardo Gaetani, Leonardo Aniello, Roberto Baldoni, Federico Lombardi, Andrea Margheri, Vladimiro Sassone.
  2. Casino, Fran; K. Dasaklis, Thomas; Patsakisa, Constantinos (March 2019). "A systematic literature review of blockchain-based applications: Current status, classification and open issues". Telematics and Informatics. 36. Elsevier: 55–81. doi: 10.1016/j.tele.2018.11.006 . ISSN   0736-5853.
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  7. Zheng, Zibin; Xie, Shaoan; Dai, Hongning; Chen, Xiangping; Wang, Huaimin (2017). "An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends". 2017 IEEE International Congress on Big Data (BigData Congress). pp. 557–564. doi:10.1109/BigDataCongress.2017.85. ISBN   978-1-5386-1996-4. S2CID   29591273.
  8. NathanSenthil; GovindarajanChander; SarafAdarsh; SethiManish; JayachandranPraveen (2019-07-01). "Blockchain meets database". Proceedings of the VLDB Endowment. 12 (11): 1539–1552. doi:10.14778/3342263.3342632. S2CID   67877079.
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