Big Data encompasses large volume of complex structured, semi-structured, and unstructured data, which is beyond the processing capabilities of conventional databases. The processing and analysis of Big Data now play a central role in decision making, forecasting, business analysis, product development, customer experience, and loyalty, to name but a few. In this paper, we examine the distinguishing characteristics of Big Data along the lines of the 3Vs: variety, volume, and velocity. Accordingly, the paper provides an insight into the main processing paradigms in relation to the 3Vs. It defines a lifecycle for Big Data processing and classifies various available tools and technologies in terms of the lifecycle phases of Big Data, which include data acquisition, data storage, data analysis, and data exploitation of the results. This paper is first of its kind that reviews and analyzes current trends and technologies in relation to the characteristics, evolution, and processing of Big Data. Copyright © 2014 John Wiley & Sons, Ltd.

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Emerging trends and technologies in big data processing

The huge growth in the Internet market and the emerging of the new web technologies and the trend toward what is called web 2.0 and recently web 3.0 come with a new challenges, new applications and new concepts such as NoSQL databases which is recently becomes a very popular as an alternative to the relational databases specially in dealing with large data which is one of the most common features of web today, providing high availability and scalability to the distributed systems which need fast access time and can't tolerate any down time during failures and have been used heavily by the big enterprises and web companies such as Facebook, amazon and google. Every new technology faced many challenges like Security vulnerabilities. This paper addresses the concepts of NoSQL, the movement, motivations and needs behind it, and reviews the types of NoSQL databases and the issues concerning to these databases mainly areas of application and the security issues compared with traditional relational databases.

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Relational vs. NoSQL Databases: A Survey

The Internet of Things (IoT) is posing new challenges and opportunities for data management and analysis techniques. One of the major problems is how to handle an increasing amount of data, with a variety of data types and data sources, in order to meet application-specific performance requirements. In this article, we address the suitability of different types of databases for storing and accessing IoT data in the cloud. Specifically, we compare the performance of SQL (i.e., MySQL) and NoSQL databases (i.e., MongoDB, CouchDB, and Redis) in a cloud environment by using different types of IoT data, namely, sensor readings and multimedia. We conducted a performance evaluation of the considered database systems through extensive experiments in the cloud. Our results show that SQL and NoSQL databases are both viable for IoT data, even though there is a high variability in the performance of the different NoSQL databases considered.

Cloud Databases for Internet-of-Things Data

This study presents a comprehensive method for rapidly processing, storing, retrieving, and analyzing big healthcare data. Based on NoSQL (not only SQL), a patient-driven data architecture is suggested to enable the rapid storing and flexible expansion of data. Thus, the schema differences of various hospitals can be overcome, and the flexibility for field alterations and addition is ensured. The timeline mode can easily be used to generate a visual representation of patient records, providing physicians with a reference for patient consultation. The sharding-key is used for data partitioning to generate data on patients of various populations. Subsequently, data reformulation is conducted as a first step, producing additional temporal and spatial data, providing cloud computing methods based on query-MapReduce-shard, and enhancing the search performance of data mining. Target data can be rapidly searched and filtered, particularly when analyzing temporal events and interactive effects.

Temporal Event Tracing on Big Healthcare Data Analytics

The purpose of this paper is to present a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and on non-relational databases thus highlighting the results obtained during performance comparison tests. The study was based on the implementation of a web-based application for population records. For the non-relational database, we used MongoDB and for the relational database, we used MSSQL 2014. We will also present the advantages of using a non-relational database compared to a relational database integrated in a web-based application, which needs to manipulate a big amount of data.

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A Comparative Study of Relational and Non-Relational Database Models in a Web- Based Application

We report the comparison between the two leading type of Database storage components prevailing in the industry. The Database is largely concerned with managing massive amount of data in a consistent, stable, repeatable and quick manner. The prominent features of both relational as well as non relational databases have been specified which form the basis of the comparison between the two types of database. The relational model is based on mathematical theory(set theory, relational theory) whereas the nonrelational databases may or may not have a single groundwork mathematical theory. Relational model is beneficial when it comes to reliability, flexibility, robustness, scalability requirements but in order to cater to the needs of modern applications where the data is huge and generally unstructured; Non-relational databases show true signs of usability here. Based on the characteristics, commonly used tools of relational and non relational databases are mentioned along with brief introduction of the tools. Comparison has been done between the tools to notify the distinctive features of tools of relational and non relational database. Conclusive remarks about the two categories of database have been mentioned.

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A Survey and Comparison of Relational and Non-Relational Database

Efficiency Measurement of Organic Solar Cells: Step-by-Step Protocol to be Followed

Swarm intelligence is the collective and organized behavior of decentralized systems, natural or artificial. ant colony. ACO methods find its applications in problems that need to find paths to goals. In this paper, we propose an algorithm that can improve performance of ACO. The proposed algorithm consists of two agents: the data agent and the routing agent where the routing agent is responsible to prepare the routing table from the given source to destination and the data agent transfers the data along that path. The earlier algorithms do this task with a common agent which also carries the data. This increases the frame size of the packet and hence the time to process the packet at each node for extracting routing information.

"two processes based routing algorithm" for ant colony optimization using swarm intellligence

The current work describes the Michael addition (MA) reaction between amine group appended NDI derivatives and ,-unsaturated ketones (chalcones) with an aim to tune the optical and electrochemical properties of...

Mehak Ahuja

Papers

A Survey and Comparison of Relational and Non-Relational Database

Efficiency Measurement of Organic Solar Cells: Step-by-Step Protocol to be Followed

Efficiency Measurement of Organic Solar Cells: Step-by-Step Protocol to be Followed

"two processes based routing algorithm" for ant colony optimization using swarm intellligence

Tuning of energy levels, transport properties and device performance of naphthalenediimide derivatives by introduction of Michael addition reaction