Big Data in Predictive Analytics System

Abstract

Big Data is of a form of concepts that is currently essential in themodern day world. The&nbspterm Big Data refers to the assortments ofsets of data that are very enormous and at the same time complex. Bigdata requires advanced modern methods of management as these types ofdata repel efforts to develop the data sets or even manage such typesof data sets using old-fashioned methods. The computing tools withinthe modern day life provides room for the analysis and processing ofcomplex sets of data such as the Big Data. The computing tools havethe ability to swiftly cross-examine the huge sets of data thereafter revealing previously untapped trends, patterns andcorrelations. With such kinds of cross-examinations, new intuitionsand estimations around current and future purchasing can be inferred.Volume, variety, veracity and velocity are the exceptional featuresof Big Data. These are features that the current computing skillsrequire in order to efficiently manage Big Data

Data Science Studio (DSS)is a software platform that is highly compatible with the predictiveanalytics. The DSS provides an environment that aggregates all stepsand data tools that are essential for getting big raw data to anapplication that is product ready. This paper looks to puthistory and context around Big Data through the analysis of previousresearch. This paper will examine the credentials and possibleefficient methods that can be used for analysis of Big Data. Theanalysis will concentrate on the transformative tools in the currentera of reduction limits and ever-more refined and influentialanalytical tools that can be used to address the question of howvaried and efficient methods can be used to solve the complex andcommercially-critical issue of enhancing sales performance and clientsatisfaction. This paperexplores the area of Predictive Analytics in finding correlations andsignificant variables through the use of Big Data with the use of theDSS software. This experimental research surveys an enhanced adoptionin the predictive analytic systems in big data technologies in recentyears. In this study, researchers have taken great interests inadvancing the utilization of Predictive Analytics in improvingBusiness Intelligence and predicting aptitude across a wide range ofvaried applications with the DSS software platforms. Through thisresearch, insight has been given on some of the underpinningsconcepts in enabling predictive capabilities in big data analysis.Furthermore, this research incorporated the varied characteristicsand components of data mining grids in big data as the supplementarybasis of the research. Predictive analytics being the futurefrontline for innovation, this system is essential for the analysisof big data. The efficiency of the Predictive analysis is due to thefact that the system is based on old concepts and techniques such asmathematical analysis and statistical analysis that are efficient forbig data.

Research Methodology

This research has adopted thequalitative approach of meta-analysis in order to evaluate and assesthe current landscape of the predictive analytics with big data. Themethodology that has been adopted in this qualitative study is notentirely experimental or is the methodology observational. This studyrelied on both experimental result of the study as well aspeer-reviewed literatures under the topic of study. The informationretrieved form the peer-reviewed literature were from researches thatwere either observational or experimental on the topic underinvestigation. The research retrieved authentic information formgenuine online sources for this research.

The approach to this researchsubject will be done qualitatively with specific focus being placedon the subject matter. The methodology will utilize the use ofgrounded theory. This approach in this study is as a result bottom-upand inductive in nature due to the concepts and information that willbe retrieved form the literatures reviewed. For that reason, due tothe alignment with the grounded theory principles, this researchmethod will be able to assist the readers as well as the researchersto comprehend multifaceted difficulties through a complete,methodical and inductive approach for the development of the theory.This research therefore, will not provide a hypothesis but insteadwill attempts to generate a theory. The generation of the theory willbe done through the ideas that will be constructed through theanalysis and the evaluation of the existing research resultspublished by other researchers. The research result for this studywill additionally include a set of diagram that will illustrate tothe readers on the concepts being investigated by the research.

The experimental aspect ofthis study will solely concentrate on the new approaches that the BigData processing and the analytics in the modern world concentrate on.The experimental research will ensure that the results from theexperiment is as updated as much a possible to ensure validity andreliability in the research outcomes. Theresearch will take into account the numerous approaches that can beused for the predictive analysis and the processing of Big Data(Gandomi&amp Haider, 2014).The research will however consider the predictive analysis methodsthat share most characteristics to ensure that the results from theresearch are conventional in nature. The experimental research willensure that the predictive analysis method used takes advantage ofthe commodity hardware as this will promote scale-out and parallelprocessing techniques. The results will be able to engagenon-rational data storage capabilities for the process and analysisof the unstructured as well as the semi-structured data while at thesame time apply the advanced analytics and data visualizationtechnologies to the Big Data approaches. This experiment will be ableto transform businesses analytic through comprehensive datamanagement processes.

The experimentalmethodological approach that will be used support this data analyticstudy will be adopted form an advanced and authentic data programmanagement methodological system. The experiment will be able toprovide managers within companies with the most efficient integratedof most preferred practices that they can use to develop data mininggrids with big data. The system is aimed at providing feedback ondata analysis, data integration, data refrainment and feedbackretrievals. This research will retrieve its experimental proceduresfor SPARK-ITS, a data analytic program. The program is acritical component of this research for the provision of qualitymanagement and quality data. The SPARK-ITS method that will be usedin this study will be tuned in a manner that the specific needs andwants of the client will be easily met. The project will howeverspecifically concentrate on large size projects of big data.

Hadoop is an open source framework that is usedfor the storage and analysis of massive amounts of data that isdistributed yet is unstructured. This research has an aim pfutilizing the Hadoop to create more improved and advanced analyticsystems. The Hadoop system will be used to enable customers andclients to access the unstructured as well as the semi-structureddata from sources within the company. Clients will therefore be ableto develop more trust in the company management and system. Thepredictive system will be designed in a systematic way that theunstructured data will be broken apart into different sets afterwhich they will be loaded into easily accessible files that will bemade up of multiple nodes running on the product hardware. The systemwill have a default file that will be stored in the HadoopDistribution File System. The above is due to the fact that theHadoop Distribution File System has the potential of the storage oflarge volumes of unstructured and semi-structured files such as thefiles in the Big Data.

The experiment will replicate the different partsof the data sets in multiple time and afterwards load the filesystems in a special way that provides room for the replacement ofnode in case one fails. The name node will be specially designed forthe research to act as the facilitator of communications within thelarge samples of the unstructured data. The experiment’s designwill be designed in a systematic manner that once the data will beloaded in the cluster of the different data, the data will be readyfor analysis through the framework (Gandomi&amp Haider, 2014).Through the results obtained from the system, the client will submita query that will turn out to be the job tracker. The job trackerwill act as the determinant of the data to be accessed by clientswithin the predictive analytics system. The system will be able tobring the required data back as feedback to the clients. The clientswill hence be able to access the exact required information form alarge sample of data form the results obtained from the experimentalsystem. Hadoop will give room for the systems’ nodes to have ampletime for the processing of data. Afterwards, the retrieved data formthe large unstructured data sample will be efficiently stored withinthe system for easy access. The clients will afterwards will be ableto access the stored results which the system will be able to load into one of the analysis’ environments.

The Hadoop track used in the experiment will becomposed of a variety of components that will promote its efficiencyand suitability for the system. The Hadoop Distributed File Systemwill be used as the default storage layer in a given cluster willable to facilitate the appropriate analysis of unstructured andsemi-structured large data. The name node will specifically be usedin this research as the node will act as the Hadoop cluster that willin turn provide the clients with the information on the specificplace that the data they require will be situated within thepredictive analytic Big Data storage system. The nodes willadditionally be able to replace themselves automatically to ensureefficiently in client operations. The secondary node will furthermorebe used in this experimental research as it will be a backup to theinitial name node. The secondary node will be able to replicateitself and hence store data that are aimed at replacing the datastored by name data in case the name data fails. Job tracker will bespecifically used in this research to initiate and coordinateprocessing of data to enable quick and easy retrieval of informationthat has been gathered form the system. Slave nodes will be essentialfor grunting and storing the retrieved results from the searches madefrom the systems. Due to the fact that the Hadoop ecosystem iscomprised various sub-projects, Hadoop will be proper for thedivision of the data into various parts. The sub-projects willadditionally be essential for the storage of the large unstructuredand the semi-structured data in the system.

The analysis of the resultthat will be retrieved from the research will be conducted insystematic stages. Text categorization will be the first stepinvolved with the data analysis of the retrieved result. The textcategorization in the research will use patented linguistic analysistechnologies and machine learning algorithms. These technologies andalgorithms will involve essential and tightly integrated DSS softwaredata analysis techniques that will ensure proper analysis of thecorrelations and significant variables in big data with the use ofthe software. The research team will search for individuals who haveexpertise in natural languages process analysis. The professionalteam will as a result determine the process of contribution that willlikely result to the success of companies that are likely to use theDSS program in predictive analytic systems in big data. The use ofclustering technologies will be able to classify documents form theretrieved data by splitting the big data into smaller groups thatwill have similar objects. These small groups will be required tohave similar characteristic. Imagecategorization will be used by the analysis method in ensuring theimages that will be gained from the data collection process aredivided into smaller easily analysable groups for efficient analysisof the collected data. The research will accessing the textualcontext of a document is not always possible or practical.

The Big Data user landscape that the experimentwill adopt will be designed in a systematic method for the efficientrequired vendor landscape. The Experiment predictive analytic systemwill be structured in a way that all of the required and recommendedmarket segments will be captured (Gandomi&amp Haider, 2014).The Big Data Vendor Landscape will be designed in a specific way thatall clients need will be efficiently met.

Table1Big Data Vendor Landscape

Big Data Market Segments

Hardware

Server

Storage

Networking

Software

Hadoop

Application

Tools

Services

Cloud Service

Technical Service

Professional Services

For the hardware, the server will consist of chipssuch as for the company Dell, HP and Intel. The storage will consistsof the NetApp amongst many others. Networking will consist of Siscoas well as internet systems amongst many other network channels. Forthe Software aspect of the Big Data Market segmentation, theapplication will consist of google and opera solutions amongst manyothers, the tools will consists of informatics amongst many others.For the services, the cloud services will consist of amazon, thetechnical services will consist of cloud wick while the professionalservices will consist of IBM.

In conclusion, the methodology that has beenadopted by this research is the best required methodology. The largeunstructured and semi-structured data will be divided in to smallerparts of data that will hence efficiently meet the requirements ofthe clients within all markets and segments.

References

Gandomi, A. &amp Haider, M. (2014).Beyond the Hype Big Data Concepts Methods and Analytics.InternationalJournal of Information Management,35(2), 137-144.&nbsp