A Review on Business Intelligence and Big Data <s> Big Data Analytics <s> Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive datasets. Architectures for big data usually range across multiple machines and clusters, and they commonly consist of multiple special purpose sub-systems. Coupled with the knowledge discovery process, big data movement offers many unique opportunities for organizations to benefit (with respect to new insights, business optimizations, etc.). However, due to the difficulty of analyzing such large datasets, big data presents unique systems engineering and architectural challenges. In this paper, we present three system design principles that can inform organizations on effective analytic and data collection processes, system organization, and data dissemination practices. The principles presented derive from our own research and development experiences with big data problems from various federal agencies, and we illustrate each principle with our own experiences and recommendations. <s> BIB001 </s> A Review on Business Intelligence and Big Data <s> Big Data Analytics <s> Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. The Big Data challenge is becoming one of the most exciting opportunities for the years to come. We present in this issue, a broad overview of the topic, its current status, controversy, and a forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining. <s> BIB002
Storing huge data sets is worthless unless they become meaningful. Big data has impacted on data analysis as well as data storage, processing, and management. As big data expands in all domains including science, engineering, business and healthcare, former data analysis techniques and architectures have become insufficient to analyze such an expansion. Therefore new data analyzing approach and technologies are needed to extract useful information from such large datasets BIB001 BIB002  . Analysis of big data is named in different ways such as BDA, advanced analytics, analytics, large-data-set analytics, etc.  . In this paper,BDA will be used. BDA is the process of examining large data sets by using statistical models, datamining techniques, and computing technologies. This process includes discovering hidden business patterns and secret correlations  , market trends, customer preferences and other useful business information  , in data. For example; loyal customers account for the large part of revenue. So, to know whether a customer is loyal or not is very important for an enterprise. It is quite possible to predict that which customer will likely be a loyal one or leave enterprise using machine learning that can use all records no matter how big they are. Although BDA offers many opportunities for organizations to benefit from, yet analyzing big data is a compelling task. X. Wu et al.  have analyzed the challenges at the data, model, and system levels. Data level challenge is developing a safe and sound information sharing protocol. The model level challenge is to design algorithms to analyze model correlations between distributed sites, and fuse decisions from multiple sources to gain the best model out of the Big Data. The system-level challenge is to design for linking unstructured data through their complex relationships to form useful patterns, and the growth of data volumes and item relationships should help form legitimate patterns to predict the trend and future.