The motivation behind this chapter is to highlight the qualities, security issue, advantages, and disadvantages of big data. In the recent researches, the issue and challenges are due to the exponential growth of social media data and other images and videos. Big data security threats are rising, which is affecting the data heterogeneity adaptability and privacy preservation analytics. Big data analytics helps cyber security, but no new application can be envisioned without delivering new types of information, working on data-driven calculations and expending determined measure of information. This chapter demonstrates how innate attributes of big data are protected.

Impact of Big Data on Security

The field of Information Technology has seen a considerable measure of advancement and notoriety in the areas of web application and correspondence. These applications and communications consistently produce a vast and diverse assortment of information, along with some authentically troublesome, multifaceted structure information called colossal information. For instance, E-trade exchanges incorporate exercises: for example, web-based purchasing, offering, or contributing. When information is high in dimension and complex in structure, regular data amassing procedures are not acceptable for examining such a large volume of data. Vast stores of terabytes of data are delivered each day from present-day information systems and electronic headways, for instance, a web of things. Examination of these massive data stores requires undertakings at various levels for a better understanding of the analysis.. The critical objective is to examine the potential impact of immense data challenges, open research issues, and unique instruments identified with the impact. Additionally, it opens another horizon for researchers to develop a game plan and also challenges open research issues. Numerous researchers are examining the lessening of the dimensionality of the extensive data for use and for better examination reporting and data discernment. Subsequently, this section provides an audit of gigantic data examination, issues, challenges, and several developments identified with great information.

Big Data Analytics Challenges and Solutions

Big data is primarily defined by the volume of a data set - generally measuring tens of terabytes and sometimes crossing the threshold of petabytes. The process of extracting useful information by analyzing different types of big data sets is called Big data analytics. Big data analytics helps organizational decision making by discovering hidden patterns, market trends and consumer preferences. This is done by following several steps and technologies involved in big data analytics. The selection of one of these technologies is based on various parameters and this selection varies from project to project as requirements and nature of projects changes. This review paper therefore aims at finding most common strategies of big data such as scalability, Programming Language Support, Latency etc. and their current state in computing world. This paper also discusses public perception on the parameters selected.

Big Data Strategies – A Review and Survey

Hadoop is a data management solution for the analysis of Big Data. In Hadoop, Hive is used to store the metadata. This study compares the scalability of Hadoop MapReduce and Hive for small and medium datasets besides showing how the metadata can be created, loaded, accessed and stored using Hive - a data warehousing solution built on top of Hadoop. To make the comparison of scalabilities Hadoop MapReduce and Hive, a word count program was investigated using two data management solutions- Hadoop MapReduce and Hive. This comparison demonstrates that the Hadoop MapReduce programming model is very low level and it will make the developers write custom programs which are hard to maintain and reuse, where as Hive uses an SQL-like query language called HiveQL to store large amounts of data consuming less time and also plugs in the Map Reduce scripts into queries.

http://ijecs.in/issue/v5-i11/11%20ijecs.pdf

Scalability Study of Hadoop MapReduce and Hive in Big Data Analytics

Screening for Lead: Predictive Modeling of Indoor Dust Lead Concentrations and Possible Effects of Intervention

/pdf/predictive-modeling-of-indoor-dust-lead-concentrations-3clu8lex.pdf

Khadija Jabeen

Papers

Scalability Study of Hadoop MapReduce and Hive in Big Data Analytics

Screening for Lead: Predictive Modeling of Indoor Dust Lead Concentrations and Possible Effects of Intervention

Predictive modeling of indoor dust lead concentrations: Sources, risks, and benefits of intervention.