No-Sql Databases

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better suited to Oracle RDBMS.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better suited to Oracle RDBMS.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better s...

Relational databases are a technology used universally that enables storage, management and retrieval of varied data schemas. However, execution of requests can become a lengthy and inefficient process for some large databases. Moreover, storing large amounts of data requires servers with larger capacities and scalability capabilities. Relational databases have limitations to deal with scalability for large volumes of data. On the other hand, non-relational database technologies, also known as NoSQL, were developed to better meet the needs of key-value storage of large amounts of records. But there is a large amount of NoSQL candidates, and most have not been compared thoroughly yet. The purpose of this paper is to compare different NoSQL databases, to evaluate their performance according to the typical use for storing and retrieving data. We tested 10 NoSQL databases with Yahoo! Cloud Serving Benchmark using a mix of operations to better understand the capability of non-relational databases for handling different requests, and to understand how performance is affected by each database type and their internal mechanisms.

— in today's information scenario, processing of data for exact knowledge has become a very important but critical task for the professionals and organizations. Involvement of Big Data and real time streaming data makes the data processing much more challenging in order to extract and to visualize the exact data. In the most popular social media portals which includes streaming data like Twitter, Facebook and LinkedIn the rapidly growing information is updated several thousand time within one second. In this research, the extraction of streaming data from most popular social media portal (Facebook) is done using different SDKs, Graph API Explorer and other APIs and data processing is done using Cypher Query Language (CQL) and Neo4jCLient (C# API for Neo4j) in Microsoft C#. Then Neo4j Graph Database along with Microsoft Visual Studio 2013 is used for the Visualization and knowledge extraction. CQL facilitate the extraction of streaming data in an efficient manner as its development intended the processing of data in a linked manner. The processing and visualization of rapidly growing streaming data is done as linked data which makes the data and knowledge extraction very easy as the relationship between data is present along with the link between them.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better s...

In this study, we evaluate the performance of SQL and NoSQL database management systems namely; Cassandra, CouchDB, MongoDB, PostgreSQL, and RethinkDB. We use a cluster of four nodes to run the database systems, with external load generators.The evaluation is conducted using data from Telenor Sverige, a telecommunication company that operates in Sweden. The experiments are conducted using three datasets of different sizes.The write throughput and latency as well as the read throughput and latency are evaluated for four queries; namely distance query, k-nearest neighbour query, range query, and region query. For write operations Cassandra has the highest throughput when multiple nodes are used, whereas PostgreSQL has the lowest latency and the highest throughput for a single node. For read operations MongoDB has the lowest latency for all queries. However, Cassandra has the highest throughput for reads. The throughput decreasesas the dataset size increases for both write and read, for both sequential as well as random order access. However, this decrease is more significant for random read and write. In this study, we present the experience we had with these different database management systems including setup and configuration complexity.

Relational databases are a technology used universally that enables storage, management and retrieval of varied data schemas. However, execution of requests can become a lengthy and inefficient process for some large databases. Moreover, storing large amounts of data requires servers with larger capacities and scalability capabilities. Relational databases have limitations to deal with scalability for large volumes of data. On the other hand, non-relational database technologies, also known as NoSQL, were developed to better meet the needs of key-value storage of large amounts of records. But there is a large amount of NoSQL candidates, and most have not been compared thoroughly yet. The purpose of this paper is to compare different NoSQL databases, to evaluate their performance according to the typical use for storing and retrieving data. We tested 10 NoSQL databases with Yahoo! Cloud Serving Benchmark using a mix of operations to better understand the capability of non-relational databases for handling different requests, and to understand how performance is affected by each database type and their internal mechanisms.

En los últimos años, con la evolución de la web y el crecimiento exponencial de usuarios en las redes sociales, ha surgido una nueva generación de usuarios, conocidos como coleccionistas digitales, los cuales, al no contar con mecanismos de organización efectivos, se convierten en acaparadores digitales. Sin embargo, esta situación es moderada a través de herramientas de software para el manejo de información personal (PIM) enfocadas al coleccionismo. En este trabajo, se analizaron algunas de ellas con el objetivo de proponer e implementar una propia, que cuente con características similares y que principalmente, mejore los procesos de personalización y organización de una colección digital. Al final del artículo, se sugiere explorar algunas líneas de investigación que involucran técnicas de inteligencia artificial, y disciplinas emergentes como la lingüística computacional y la gestión del conocimiento con el propósito de optimizar actividades de gestión en colecciones digitales.

This paper proposes a framework, namely, HSMF (Hadoop Secure Messaging Framework). Now a day, it requires efficiency in processing billion bytes of data (binary) particularly through business processes. It also has become expensive to ensure reliability in each and every application that processes large amount of datasets (complex or structured). Hadoop platform was designed to address these
issues. It was designed mainly for enterprise solution. Our goal is to establish secure communication between hadoop and smart devices or applications, so that applications of smart devices can send queries to hadoop and display respective results. HSMF is based on XML(Extensible Markup Language) message over TCP/IP which gives hadoop more flexibility in order to communicate not only with java based applications but also with applications written in other languages such as C/C++, C#, Python, Dot Net framework etc. Since XML is generated dynamically, client application may choose DML (Data Manipulation Language) of hadoop using HSMF. Client application does not require knowing the design of dataset, it only performs query on particular dataset based on user requirement. During communication via TCP/IP, every XML message header is checked and matched by a messaging header checking mechanism. This is why HSMF is secure. Our results show a successful communication between hadoop and smart devices or applications where smart devices and applications send query to hadoop and display the result received from hadoop.