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Big data – A Review
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1652 Big data – A Review Nanda Kumar Parameshwaran Student, Computer Science Department, Rajiv Gandhi Institute of Technology, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Big data is a data or data sets so large or complex that traditional data processing applications are inadequate and distributed databases are needed. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning. It is a collection of massive and complex data sets that include the huge quantities of data, social media analytics, data management capabilities, real time data etc. Challenges include sensor design, capture, data curation, sharing, storage, analysis, visualization, information privacy etc. Big data refers to datasets high in variety and velocity, so that very difficult to handle using traditional tools andtechniques.Theprocessofresearchinto massive data to reveal secret correlations named as big data analytics. Big Data is a data whose complexity requires new techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. We need a different platform named Hadoop as the core platform for structuring Big Data, and solves the problem of making it useful for analytics purposes. Key Words: Big Data, Parallel programming, MapReduce technique. 1. INTRODUCTION Every digital process and social media exchange produces Big data. The Systems, sensors and mobile devices transmit. The arrival of big data is from multiple sources at a frightening velocity, volume and variety. We need optimal processing power, analytics capabilities and skills to extract meaningful value from big data. More confident decision making can do with accurate big data. Good decisionslead to greater operational efficiency, cost reduction and reduced risk. Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, and combat crime and so on"[1]. Scientists, business executives, practitionersofmedia, and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification(RFID)readers, and wireless sensor networks [2][3][4]. Big data "size" is a constantly moving target, ranging from a few dozen terabytes to many petabytes of data. (1 petabyte is 1000 terabytes) Fig -1: An image of Big data Here are some real-world examples of Big Data in action: • Consumer product companies and retail organizations are monitoring social media likeFace book and Twitter to get an unprecedentedviewinto customer behavior, preferences, and product perception. • Manufacturers are able tomonitorminutevibration data from their equipment, which changes slightly as it wears down, to predict the optimal time to replace or maintain. Replacing it too soon wastes money and replacing it too late triggers an expensive work stoppage. • Manufacturers are also monitoringsocial networks, but with a different goal than marketers: They are using it to detect aftermarket support issues before a warranty failure becomes publicly detrimental. The government is making data public at the national level, state level, and city level for users to develop new applications that can generate public better. • Financial Services organizations are taking data mined from customer interactions to slice and dice their users into finely tuned segments and enables these financial institutions to create increasingly relevant and sophisticated offers. • Advertising and marketing agencies are tracking social media to Insurance companies are using Big Data analysis to see which home insurance applications can be immediately processed, and which ones need a validating in-person visit.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1653 • Retail organizations are engaging brand advocates, changing the perception of brand antagonists, and even enabling enthusiastic customers to sell their products. All these things are doing by embracing social media. • Hospitals predict those patients that are likely to seek readmission within a few months of discharge by analyzing medical data and patient records. The hospital can then be preventing another costly hospital stay. • To offer more appealing recommendations and more successful coupon programs the , Web based businesses are developing information products that combine data gathered from customers • Sports teams are using data for tracking ticket sales and are using bigdata for tracking team strategies also. 1.1 Three Vs of big data: volume, velocity and variety5. (Big Data Parameters) Fig-2: An image of Big data Volume. Volume of data stored in enterprise repositories have grown from gigabytes to petabytes. Many factors contribute to the increase in data volume like transaction based data stored through the years, unstructured data streaming in from social media etc. Huge amounts of sensor and machine-to-machine data being collected. In the past days, excessive data volume was a storage issue. But with decreasing storage costs, otherissues emerge,includinghow to determine relevance within large data volumes and how to use analytics to create value from relevant data. Volume referred as amount of data. 1.2 Velocity. Data isstreaminginatextraordinaryspeed and must be dealt with in a timely manner. RFID sensors and smart metering are driving the need to deal with fastmoving of data in near-real time. It is a challenge for most organizations to reacting quickly enough to deal with data velocity. Velocity referred the speed of data processing. For time-sensitive processes such as catching fraud, big data must be used. It streams into your enterprise in order to maximize its value. 1.3 Variety. Today data comes in different types of formats. Structured and numeric data in traditional databases. Information created from line-of-business applications. Unstructured text documents, email, video, audio and financial transactions. Managing, merging and governing different varieties of data are somethingmanyorganizations still struggle with. Different types and sources of data are there. Data variety exploded fromstructuredandlegacydata stored in enterprise storages to unstructured, semi structured, audio, video etc. We consider two additional dimensionswhenthinkingabout big data: 1.4 Variability. With the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. It is trending in social media. Everyday seasonal and event-triggered peak data loads cannot be able to manage. Even more unstructured data involved. The inconsistency the data can show at times—-which can hamper the process of handling and managing the data properly. The inconsistency the data can show at times can hamper the process of handling and managing the data properly. 1.5 Complexity. Today's data comes from different types of sources. it is still an undertaking to link, match and transform data across systems. Anyway, it is necessary to connect and correlate relationships,hierarchiesand multiple data linkages. Otherwise your data can quickly spiral out of control. When large volumes of data come from multiple sources, the data management is very complex. Especially data must be linked, connected, and correlated theuserscan grasp the information or messages the data is supposed to convey. 1.6 Veracity The quality of captured data, which vary so high. The Accurate analysis of data depends on the veracity of source data. 2. PARALLEL PROGRAMMING & Map Reduce Data analysis software parallelizes fairly naturally. Many programmers are interested to building programs on the parallel model. The parallel research hadthemostsuccessin the field of parallel databases. Rather than requiring the programmer to unknot an algorithmintoseparatethreadsto be run on separate cores, parallel databases let them break up the input data tables into pieces, and pump each piece through the same single-machine program on each processor. This “parallel dataflow” model makes parallel programming as easy as programminga singlemachine. And it works on “shared-nothing” clusters of computers in a data center: The machines involved can communicate via simple streams of data messages, without a need for an expensive shared RAM or disk infrastructure. [6] Famous big data analysis tool is Hadoop. Apache Hadoop is an open-source software framework. it is written in Java for distributed storage and distributed processingofbigdata on
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1654 computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines or racks of machines) are commonplace and thus should be automatically handled in software by the framework.[7] The heart of Hadoop is MapReduce. It is this programming paradigm that allows for massive scalability across thousands of servers in a Hadoop cluster. It is useful for batch processing on petabytes or zeta bytes of data storedin Apache Hadoop. If we are familiar with clustered scale-out data processing solutions. Then the MapReduce concept is simple to understand. MapReduce programming model has twisted a new page in the parallelism story. The MapReduce framework is a parallel dataflow system that works by dividing data across machines. Each of which runs the same single-node logic. MapReduce asks programmers to write traditional code, in languages like C, Java, PythonandPerl.In addition to its familiar syntax, MapReduce allows programs to be written to and read from traditional files in a file system, rather than requiring database schema definitions. MapReduce refers to two separate and distinct tasks. The first is the job of map, which takes a set of data and converts it into another set of data. Individual elements are broken down into value pairs. The reduce job takes the output from a map as input and combines those data valuesintoa smaller set of values. The reduce job is always performed after the map job. So the sequence of the name MapReduce. Fig -2: MapReduce 3. BEST BIG DATA ANALYTICS USE CASES 3.1Sentiment Analysis Sentiment analysis offers powerful business intelligence to enhance the customer experience, revitalize a brand, and gain competitive advantage. The key to successful sentiment analysis lies in the ability to dig for multi structured data pulled from different sources into a single database. 3.2 360-Degree View of Customer A 360-degree customer view offers a deeper understanding of customer behavior and motivations. Obtaining a 360- degree customer review requires analysis of data from different sources like social media, data collecting sensors, mobile devices etc. From there, more effective micro- segmentation and real-time marketing are getting as result. 3.3 Ad Hoc Data Analysis Ad-hoc analysis only looks at the data requested or needed, providing another layer of analysis for data sets that are becoming larger and more varied. Big data ad-hoc analytics can help in the effort to gain greater insight into customers by analyzing the relevant data from unstructured sources, both external and internal. 3.4 Real-Time Analytics Systems that offer real-time analytics quickly decipher and analyze data sets, providing results even as data is being generated and collected. This high-velocity method of analytics can lead to immediate reaction and changes. It allows for better sentiment analysis, split testing, and improved targeted marketing. 3.5 Multi-Channel Marketing Multi-channel marketingcreatesa seamless acrossdifferent types of media like company websites, social media, and physical stores. During all stagesofthe buyingprocessmulti- channel marketing requires an integratedbigdata approach. 3.6 Customer Micro-Segmentation Customer micro-segmentation provides more tailored and targeted messaging for smaller groups. This personalized approach requires analysis of big data collected through sources like customers’ online interactions, social media etc. 3.7 Ad Fraud detection Ad fraud detection requires data analysis of fraud strategies by recognizing patterns and behaviors. Data that shows irregularity of group behavior make it so ad fraud is find out and blocked before it is spread. 3.8Click stream analysis Click stream analysis helps to grow the user experience by optimizing company websites, and offering better insight into customer segments. click stream analysis helps to personalize the buying experience, getting an improved return on customer visits with big data. 3.9 Data Warehouse Modernization Integrate big data and data warehouse capabilities to boost operational efficiency. Optimize your data warehouse to enable fresh types of analysis. Use big data technologies to set up a staging area or landing zone for your new data before formative what data should be moved to the data warehouse. divest infrequently accessed or aged data from warehouse and application databases using in sequence integration software and tools.
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1655 3.10 Big Data and Predictive Modeling The most common uses of big data by companies are for tracking business processes andoutcomes,andfor buildinga wide array of predictive models. Amazon and Netflix recommendations rely on predictive modelsofwhatbook or movie an individual might want to purchase.Google’ssearch results and news feed rely on algorithms that predict the significance of particular web pages or articles.Apple’sauto- complete function tries to forecast the rest of one’s text or email based on past convention patterns. Online advertising and marketing rely greatly on automated predictive models that aim individuals who might be particularly likely to answer to offers. The application of predictive algorithms extends well ahead of the online world. In health care, it is now common for insurers to adjust payments and quality measures based on “risk scores,” which are resulting from predictive models of human being health expenses and outcomes. An individual’s risk score is naturally a weighted sum of health indicators that recognize whether an individual hasdifferent persistent conditions, with the weights chosen based on a statistical analysis. Credit card companies use predictive models of default and repayment to guide their underwriting, pricing, and marketing actions. 4. BIGDATA CHALLENGES Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquirement, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to maintain and what to reject,and how to store what we keep unfailingly with the right metadata. a great deal data today is not natively in structured format; for example, tweets and blogsareweakly ordered pieces of text, whileimagesandvideoarestructured for storage and display. But not for semantic content and look for. Transforming such content intoa structuredformat for later analysis is a main test. The value of data explodes when it can be associated with other data. Thus, data integration is a major creator of value. The majority data is directly generated in digital format today; we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link before created data. Data analysis, organization, recovery, and modeling are other foundational challenges. Data analysisis a clear bottleneck in a lot of applications, both due to be small of scalability of the original algorithms and due to the complexity of the data that needs to be analyzed. Lastly, presentation of the results and its clarification by non- technical domain experts is vital to extracting actionable knowledge. 4.1 Volume of data The volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with new sources of data that are emerging. For instance, in the year 2000, 800,000 petabytes (PB)ofdata werestoredin the world. According to IBM it is anticipated to reach 35 zettabytes (ZB) by 2020. Social media plays a key role ie Twitter generates 7+ terabytes (TB) of data every day. Face book, 10 TB. Mobile devices also play an important role. 4.2 Big data skills are in short supply There’s already a shortage of data scientists in the market. This includes a shortage of people who know how to labor well with large volumes of data and big data sets.Companies need the right merge of people to help make sense of the data streams that are coming into their organizations. This includes skills for applying prophetic analytics to big data, a skill set that even most data scientists be short of. 5. CONCLUSIONS It is now a phase for Big Data development. This article focuses on the concept of Big Data with 3V to present the boundaries and challenges in big-data processing. To get out of Big Data limitations these challenges have to be met. The document describes various advantages and disadvantages of Hadoop as a tool for large data management. Although Hadoop with its ecosystem is a powerful solution to deliver big data but does not yet sound good for frequent data changes. In other words, we can currently say that there is no transactional support in Hadoop. Hadoop is onlyused forOLAP.Themachinelearning algorithm for Big Data needs to be more robust and easier to use. Therefore, a further improvement is required in the Big Data solution. References [1] "Data, data everywhere". The Economist. 25 February 2010. Retrieved 9 December 2012. [2] "Data, data everywhere". The Economist. 25 February 2010. Retrieved 9 December 2012. [6]Hellerstein, Joe (9 November 2008). "Parallel Programming in the Age of Big Data". Gigaom Blog. [7]"Welcome to Apache™ Hadoop®!". hadoop.apache.org. Retrieved 2015-09-20. [3] "Community cleverness required". Nature455(7209):1. 4 September 2008. doi:10.1038/455001a. [5] META Group. "3D Data Management: Controlling Data Volume, Velocity, and Variety." February 2001. [4] "Sandia sees data management challenges spiral". HPC Projects. 4 August 2009.
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