China
Africa
Explore chapters and articles related to this topic
Access Control for Social Network Data Management
Published in Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan, Secure Data Science, 2022
Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan
Big Data Management Tools include the following: Apache Hive: Apache Hive is an open source SQL-like database/data warehouse that is implemented on top of the Hadoop/MapReduce platform. It was initially developed by Facebook to store the information related to Facebook data. However, later it became an open source project and a trademark of Apache. Google Big Query: Big Query is essentially a data warehouse that manages petabyte scale data. It runs on Google's infrastructure and can process SQL queries or carry out analytics extremely fast. NoSQL Database: NoSQL database is a generic term for essentially a nonrelational database design or scalability for the web. It is known as a nonrelational high-performance database. The data models for NoSQL databases may include graphs, document structures, and key-value pairs. Some of the popular NoSQL databases include MongoDB and HBase.
Storage, System Security and Access Control for Big Data IoT
Published in Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, Blockchain, Internet of Things, and Artificial Intelligence, 2021
T. Lucia Agnes Beena, T. Kokilavani, D. I. George Amalarethinam
NoSQL stands for ‘Not Only SQL’ and it has a different performance characteristic from the existing relational database. The NoSQL database provides better results on some read/writes NoSQL systems that have the property of horizontal scaling that is duplicating and segregating data over different data stores. This feature allows them to support a feature called online transaction processing (OLTP), an enormous amount of simple transactions per second. The transactional property ACID is not provided by NoSQL systems. Instead of that NoSQL uses a new feature called BASE, representing availability and consistency. Higher efficiency and elasticity is achieved when NoSQL includes ACID constraints [37]. NoSQL is a freely accessible, non-relational, and distributed data store. It can be used for real-time web application data as well as IoT integrated big data. NoSQL data store also supports different storage representations like column-oriented data store, document-oriented data store, key-value data store, and graph-based data store.
Semantic Technologies as Enabler
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
For the exchange of this semi-structured and unstructured data, a new metadata approach emerged. The meta-information in this approach was represented using standard generalized markup language (SGML), which developed to hypertext markup language (HTML) and further to eXtensible Markup Language (XML). JSON (JavaScript Object Notation) also emerged as an open standard and an alternative to XML for transmitting data objects. XML and JSON documents are self-describing, with the schema elements becoming part of data. Multimodal systems identify these implicit schema elements and are able to query and analyze these data sets. In the mid-2000s, NOSQL DBMSs emerged as an answer to the high cost of RDBMSs for huge data sets and their low functionality. Driven by financial considerations, ACID properties are generally relaxed for NOSQL databases, retrograding them to the data level [Ekren and Erkollar 2020]. Some notable NOSQL database types are wide-column databases (Cassandra), document databases (Couchbase, MongoDB), key-value databases (Redis), and graph databases (AllegroGraph, Neo4j, Virtuoso).
Are NoSQL Databases Affected by Schema?
Published in IETE Journal of Research, 2023
Neha Bansal, Shelly Sachdeva, Lalit K. Awasthi
With the growing complexity of the applications, the performance of the databases working in the backend is becoming increasingly critical. As much as the database should be fast in querying data, it is expected to be concise and small. As a result, the regime of NoSQL databases has grown stronger [1,2]. Based on the data models, NoSQL databases are classified into four types: document, column, key-value, and graph stores. These data models have a similar logical model (key-value pair) but have a different physical model [3]. Despite their differences, NoSQL databases share certain design characteristics. Firstly, NoSQL databases offer more flexible schema-less data models that must be interpreted at the application level. Secondly, to achieve high availability and low Latency while scaling out, this scalability is further facilitated by grouping relevant data items in the same storage node and using extensive data duplication, enabling better query performance without moving related data over the network. NoSQL stores allow weak consistency transaction models by relaxing strict ACID properties. Finally, NoSQL databases allow for easy replication and horizontal data partitioning across local and remote servers.
Experiences with big data: Accounts from a data scientist’s perspective
Published in Quality Engineering, 2020
Murat Kulahci, Flavia Dalia Frumosu, Abdul Rauf Khan, Georg Ørnskov Rønsch, Max Peter Spooner
With increased accumulation of production data, one of the biggest challenges has become the allocation of enough computational resources to process it. Although new technologies, such as parallel computing and quantum computing have revolutionized the whole field, memory capabilities are still limited. Most of the well-known data analytics methods worked on the principal of in-memory processing. Computing frameworks such as Hadoop and Spark (Zaharia et al. 2010) enable in-memory computation of large data streams and provide solutions to the problems prompted by the continuous streams of data (Agneeswaran 2014). In terms of data storage, there is currently a transition towards NoSQL (“non-SQL” or “non-relational”) databases (Leavitt 2010) as opposed to the traditionally structured relational databases. One of the key advantages of NoSQL databases is that they can handle large unstructured data efficiently.
A Review of Spatial Big Data Platforms, Opportunities, and Challenges
Published in IETE Journal of Education, 2020
The Spatial Big Data storage includes SDBMS discussed in the previous section, NoSQL data stores, Hadoop Distributed File System (HDFS), and other geo data file formats. NoSQL is used as an umbrella term for all databases and data stores that are used for problems that cannot be solved using RDBMS. The NoSQL databases are schema less and distribute data among commodity servers to support scalable and efficient processing of large amounts of data. MongoDB, Cassandra, and ElasticSearch are some of the popular NoSQL (No-Structured Query Language) data stores that provide spatial data functionalities. Their spatial data features, in terms of storage and querying of geospatial objects by these NoSQL data stores for massive scale applications, are discussed next.