Advantages Of Building Blocks Of Machine Learning: Mahout And Spark

The big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. It represents a database that can’t be processed and managed by current data mining techniques due to large size and complexity of data. Big data analytic includes the representation of data in a suitable form and make use of data mining to extract useful information from these large dataset or stream of data. As stated above the big data analytics has recently emerged as a very popular research and practical-oriented framework that implements i) data mining, ii) predictive analysis forecasting, iii) text mining, iv) virtualization, v) optimization, vi) data security, vii) virtualization tools for processing very large data sets. In the implementation of big data applications, new data mining techniques and virtualization are required to be implemented due to the volume, variability, forms and velocity of the data to be processed. A set of machine learning techniques based on statistical analysis and neural networking technology for big data is still evolving but it shows a great potential for solving a big data business problems. Further, a new concept of in-memory database for enhancing the speed for analytic processing is further helping

Hadoop \cite{white2012hadoop} is an open-source framework for distributed storage and data-intensive processing, first developed by Yahoo!. It has two core projects: Hadoop Distributed File System (HDFS) and MapReduce programming model \cite{dean2008mapreduce}. HDFS is a distributed file system that splits and stores data on nodes throughout a cluster, with a number of replicas. It provides an extremely reliable, fault-tolerant, consistent, efficient and cost-effective way to store a large amount of data. The MapReduce model consists of two key functions: Mapper and Reducer. The Mapper processes input data splits in parallel through different map tasks and sends sorted, shuffled outputs to the Reducers that in turn groups and processes them using a reduce task for each group.

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a Parallel and distributed computing environment. It makes Use of the commodity hardware Hadoop is Highly Scalable and Fault Tolerant. Hadoop runs in cluster and eliminates the use of a Super computer. Hadoop is the widely used big data processing engine with a simple master slave setup. Big Data in most companies are processed by Hadoop by submitting the jobs to Master. The Master distributes the job to its cluster and process map and reduce tasks sequencially.But nowdays the growing data need and the and competition between Service Providers leads to the increased submission of jobs to the Master. This Concurrent job submission on Hadoop forces us to do Scheduling on Hadoop Cluster so that the response time will be acceptable for each job.

During the period of my curriculum practical training I have learned the HADOOP technology, initial three weeks I was taught the concepts that I should be aware of in order to understand the whole concepts of HADOOP technology. In this regard I was taught collection framework in java at first.

She does a good job confirming the appraisal is available on MyQL and provided the instructions to assist the client to locate it.

The paper “A Comparison to Approaches to Large-Scale Data Analysis” by Pavlo, compares and analyze the MapReduce framework with the parallel DBMSs, for large scale data analysis. It benchmarks the open source Hadoop, build over MapReduce, with two parallel SQL databases, Vertica and a second system form a major relational vendor (DBMS-X), to conclude that parallel databases clearly outperform Hadoop on the same hardware over 100 nodes. Averaged across 5 tasks on 100 nodes, Vertica was 2.3 faster than DBMS-X which in turn was 3.2 times faster than MapReduce. In general, the parallel SQL DBMSs were significantly faster and required less code to implement each task, but took longer to tune and load the data. Finally, the paper talk about

In previous waves of automation, workers had the option of moving from routine jobs in one industry to routine jobs in another; but now the same “big data” techniques that allow companies to improve their marketing and customer-service operations also give them the raw material to train machine-learning systems to perform the jobs of more and more people. “E-discovery” software can search mountains of legal documents much more quickly than human clerks or paralegals can. Some forms of journalism, such as writing market reports and sports summaries, are also being automated.

Using big data analysis, the capability of predictive analytics through machine learning to recognize patterns in open-source data, supports

Today, most financial services organizations try to solve all their big data challenges using either grid or cluster technologies. These data analytic technologies have solved several problems around the world.

Some machines are capable to acquire their own knowledge by extracting patterns from raw data, a phenomenon known as machine learning (ML) (Bengio, Ian and Aaron 2016). Without question, many aspects of modern society have been deeply impacted by these machine learning systems. Furthermore, ML claims to accomplish simple results that can be effortlessly understood by humans (Michie, et al. 1994). Outputs from these systems that are used in service systems include, but are not limited to offering customers new items and narrowing down their search based on their interests; language understanding, object recognition, speech perception, and identifying and favoring significant results of online searches (Yann , Yoshua and Geoffrey 2015). It is important to emphasize that even though human intervention is necessary for background knowledge, the operational phase is expected to be without human interaction (Michie, et al. 1994). Consequently, these systems must be able to learn through time. According to Alpaydin (2004), they must be able to evolve and optimize a performance criterion in order to adapt to the environmental changes to which they are exposed over time. These systems do that through the use of past experience or example data.

David Whitenack discusses how Go, a new programming language invented by Google can be used to overcome common struggles data scientists face such as: building ‘production ready’ applications, applications or services with inconsistent behavior and difficulties in integrating data science development in an engineering company. Go alleviates these problems while still being productive in performing data science. And then he discusses that Go has a data science ecosystem which enables users to perform basics like data gathering, cleaning, organizing as well as machine learning. Nicolas Seyvet and Ignacio Mulas Viela explain the how the telecom industry can handle the “explosion of data” by using data analytics. They apply two data analytics models: Kappa and a self- training Bayesian model, on a use case using a data stream originating from a telco cloud-monitoring system. These models help the user understand the principles behind the two models, how an end-to-end analytics project is carried out in the telecom industry and finally main challenges in these two analytical implementations.

Abstract— The Data which is structured and unstructured and is so large with massive volume that it is not possible by traditional database system to process this data is termed as Big Data. The governance, organization and administration of the big data is known as Big Data Management. For reporting and analysis purposes we use data warehouse techniques to process data. These are the central repositories from disparate data sources. Now Big Data Management also requires the data warehousing techniques for future predictions and reporting. So in this paper we touched certain issues of data warehousing usage in Big Data management, its applications as well as limitations also and tried to give the ways data warehousing is useful in Big Data Management.

Machine-learning fascinated me ever since I discovered the field because, throughout my mathematical education, I often thought about the idea of fitting functions on pre-existing data to create generalized solutions. There is a variety of potential applications within machine-learning that interest me; such as the automation of various tasks performed typically by doctors or the ML applications to computer software systems. Extraction of information from human-written text sources (NLP) also interests me because it enables a researcher to quantify and qualify(?)information

Spark is cluster framework with an open source software. It was 1st invented by Berkely in