Why not share! A big-data architecture for real-ti Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Published in: Technology , Business. Full Name Comment goes here. Are you sure you want to Yes No. Mohan K. Show More. No Downloads. Views Total views. Actions Shares. Embeds 0 No embeds. No notes for slide. All rights reserved. Printed in the United States of America. While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.
Table of Contents 1. How Fast Is Fast?. How Real Is Real Time?. The Five Phases of Real Time. How Big Is Big?.
Table of Contents
Part of a Larger Trend. Steve is a great showman … how can we predict if the iPhone is a fad or the next big thing? The badnewsisthatyouhavenowayofqueryingthatdataanddiscovering the answer to a critical question: How many people are accessing my sites from their iPhones? Back then, you had to know the kinds of questions you planned to ask before you stored your data.
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Today, you are much less likely to face a scenario in which you cannot query data and get a response back in a brief period of time. Analytical processesthatusedtorequiremonth,days,orhourshavebeenreduced to minutes, seconds, and fractions of seconds. But shorter processing times have led to higher expectations.
Two years ago, many data analysts thought that generating a result from a query in less than 40 minutes was nothing short of miraculous. Today, theyexpecttoseeresultsinunderaminute. A rapidly emerging universe of newer technologies has dramatically reduced data processing cycle time, making it possible to explore and experiment with data in ways that would not have been practical or even possible a few years ago. Despite the availability of new tools and systems for handling massive amounts of data at incredible speeds, however, the real promise of advanced data analytics lies beyond the realm of pure technology.
To others, it signals the dawn of a new era in which machines begin to think and respond more like humans. Introduction 3 8.
About the Interviewee
Businesses and governments have been storing huge amounts of data for decades. What we are witnessing now, however, is an explosion of new techniques for analyzing those large data sets. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory NVM based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.
In order to leverage effectively the abundant parallelism provided by large many-core enterprise servers, Java applications, libraries, and the virtual machine need to be architected carefully to avoid single-thread bottlenecks. Among the solutions for challenges tackled in scaling a single Java Virtual Machine JVM , we discuss the most rewarding ones. They include: converting shared data objects to per-thread independent objects, applying scalable memory allocators, utilizing appropriate concurrency frameworks, parallelizing garbage collection phases, and enhancing data affinity to CPUs through a NUMA aware garbage collector.
Heterogeneous computing platforms combining general-purpose processing elements with different accelerators such as GPU or FPGAs are ideally suited for efficient processing of compute-intensive data analytics kernels. In this chapter, we focus on the acceleration of data analytics kernels on heterogenous computing systems with FPGAs. The introduction of FPGAs in the context of data analytics is negatively impacted by the difficulty in programming such systems given the increasing complexity of FPGA-based accelerators.
This makes high-level synthesis HLS an attractive solution to improve designer productivity by abstracting the programming effort above register-transfer level RTL. HLS offers various architectural design options with different trade-offs via pragmas loop unrolling, loop pipelining, array partitioning. However, non-negligible HLS runtime renders manual or automated HLS-based exhaustive architectural exploration for implementation of the kernels practically infeasible.
To address this challenge, we have developed Lin-Analyzer, a high-level accurate performance analysis tool that enables rapid design space exploration with various pragmas for FPGA-based accelerators without requiring RTL implementations. We show how Lin-Analyzer can enable easy but performance efficient implementation of computational kernels from a variety of data analytics applications onto FPGA-based heterogeneous systems. Real-time data analytics based on machine learning algorithms for smart building energy management system is challenging.
This chapter presents a fast machine-learning accelerator for real-time data analytics in smart micro-grid of buildings. A compact yet fast incremental least-squares-solver based learning algorithm is developed on computational resource limited IoT hardware. The compact accelerator mapped on FPGA can perform real-time data analytics with consideration of occupant behavior and continuously update prediction model with newly collected data.
Experimental results have shown that our proposed accelerator has a comparable forecasting accuracy with an average speed-up of 4. Energy efficiency has emerged as a major barrier to system performance and scalability, especially when dealing with applications which require processing large datasets. These data-intensive kernels differentiate themselves from compute-intensive kernels in that increased processor performance through parallel execution and technology scaling are unlikely to sufficiently improve energy-efficiency.
This chapter describes two embodiments of a novel and reconfigurable memory-based computing architecture which is designed to handle data-intensive kernels in a scalable and energy-efficient manner, suitable for next-generation systems. The rise of the Internet of Things—billions of internet connected sensors constantly monitoring the physical environment has coincided with the rise of big data and advanced data analytics that can effectively gather, analyze, generate insights about the data, and perform decision making. Data analytics allows analysis and optimization of massive datasets: deep analysis has led to advancements in business operations optimization, natural language processing, computer vision applications such as object classification, etc.
Furthermore, data-processing platforms such as Apache Hadoop White, Hadoop: the definitive guide. Due to the natural parallelism and the speed enhancement, Residue Number System RNS has been introduced to perform the modular multiplications in public-key cryptography. In this work, we examine the secure performance of RNS under side channel attacks, expose the vulnerabilities, and propose countermeasures accordingly. The proposed methods improve the resistance against side channel attacks without great area overhead or loss of speed performance, and are compatible to other countermeasures on both the logic level and the algorithm level.
We prototype the proposed design on FPGA, and presented the implementation results confirm the efficiency of the proposed countermeasures. In this chapter, we describe a radically new framework for ultra-low-power biomedical circuit design and optimization. The proposed framework seamlessly integrates data processing algorithms and their customized circuit implementations for co-optimization.
The efficacy of the proposed framework is demonstrated by a case study of brain—computer interface BCI. Processing large amounts of data has been done in the past, but what is new this time around is that it's being done on commodity hardware and open-source software tools. We have chosen a selection of the most useful books for data analysis in this bibliography. These start from high level concepts of business intelligence, data analysis and data mining.
Emerging Big Data Trends for
The list then works its way down to the tools needed for number crunching mathematical toolkits, machine learning, and natural language processing. In order to implement these concepts beyond the "toy" stage, infrastructure tools are covered in Cloud Services and Infrastructure and Amazon Web Services. Finally, big data tools that can be deployed in the cloud or locally are covered in the Hadoop, and NoSqL Data Stores section.
Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history.
These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world Risk, Marketing, Healthcare, Financial Services, etc.
Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more. Investors and technology gurus have called big data one of the most important trends to come along in decades. Big Data Bootcamp explains what big data is and how you can use it in your company to become one of tomorrow's market leaders.
Along the way, it explains the very latest technologies, companies, and advancements. Big data holds the keys to delivering better customer service, offering more attractive products, and unlocking innovation. That's why, to remain competitive, every organization should become a big data company. It's also why every manager and technology professional should become knowledgeable about big data and how it is transforming not just their own industries but the global economy.
And that knowledge is just what this book delivers. It explains components of big data like Hadoop and NoSQL databases; how big data is compiled, queried, and analyzed; how to create a big data application; and the business sectors ripe for big data-inspired products and services like retail, healthcare, finance, and education. D3 B Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis.
Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches.
H Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools.
If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals. W37 To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools.
Descriptions are based on first-hand experience with these tools in a production environment.
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This handy glossary also includes a chapter of key terms that help define many of these tool categories. D32 M64 Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?
Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. Call Number: QA D B54 The Big Data Now anthology is relevant to anyone who creates, collects or relies upon data. It's not just a technical book or just a business guide. Data is ubiquitous and it doesn't pay much attention to borders, so we've calibrated our coverage to follow it wherever it goes.
In the first edition of Big Data Now, the O'Reilly team tracked the birth and early development of data tools and data science. Now, with this second edition, we're seeing what happens when big data grows up: how it's being applied, where it's playing a role, and the consequences -- good and bad alike -- of data's ascendance. B Big Data in Healthcare can be characterized by volume, velocity and variety. Enterprises have an ever-growing amount of data of all types equivalent to petabytes of information. Consider the amount of data created from the Genomic research on a daily basis.
The velocity refers to how fast the input is and how quickly we can analyze data to yield measurable results. One example is how big data must stream in the instance of emergency care. There is a wide variety of data, structured and unstructured, such as text, sensor data, audio, video, click streams, log files and more. This bibliography will connect you with books and videos in the vast Safari Books Online library, covering all of the Big Data technologies and applications for the healthcare industry to help treatment effectiveness and efficiency.
D S35 Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. C76 The phenomenon of volunteered geographic information is part of a profound transformation in how geographic data, information, and knowledge are produced and circulated.
By situating volunteered geographic information VGI in the context of big-data deluge and the data-intensive inquiry, the 20 chapters in this book explore both the theories and applications of crowdsourcing for geographic knowledge production with three sections focusing on 1 VGI, Public Participation, and Citizen Science; 2 Geographic Knowledge Production and Place Inference; and 3 Emerging Applications and New Challenges. This book argues that future progress in VGI research depends in large part on building strong linkages with diverse geographic scholarship.
Contributors of this volume situate VGI research in geography's core concerns with space and place, and offer several ways of addressing persistent challenges of quality assurance in VGI. This book positions VGI as part of a shift toward hybrid epistemologies, and potentially a fourth paradigm of data-intensive inquiry across the sciences. It also considers the implications of VGI and the exaflood for further time-space compression and new forms, degrees of digital inequality, the renewed importance of geography, and the role of crowdsourcing for geographic knowledge production.
B37 Technology does not exist in a vacuum. In the same way that a plant needs water and nourishment to grow, technology needs people and process to thrive and succeed. Culture i. Big data is not just a technology phenomenon. It has a cultural dimension. It's vitally important to remember that most people have not considered the immense difference between a world seen through the lens of a traditional relational database system and a world seen through the lens of a Hadoop Distributed File System. This paper broadly describes the cultural challenges that accompany efforts to create and sustain big data initiatives in an evolving world whose data management processes are rooted firmly in traditional data warehouse architectures.
S87 The Internet used to be a tool for telling your customers about your business. Now its real value lies in what it tells you about them. Every move your customers make online can be tracked, catalogued, and analyzed to better understand their preferences and predict their future behavior.
And with mobile technology like smartphones, customers are online almost every second of every day. The companies that succeed going forward will be those that learn to leverage this torrent of information - without being drowned by it. D38 The world is awash with digital data from social networks, blogs, business, science and engineering.
Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements.
This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.
D32 M Large-scale data analysis is now vitally important to virtually every business. Mobile and social technologies are generating massive datasets; distributed cloud computing offers the resources to store and analyze them; and professionals have radically new technologies at their command, including NoSQL databases.
Until now, however, most books on "Big Data" have been little more than business polemics or product catalogs. Data Just Right is different: It's a completely practical and indispensable guide for every Big Data decision-maker, implementer, and strategist. C88 M37 It is now possible to predict the future when it comes to crime. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions.
In this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity.
Knowledge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis. The book is a starting point for those thinking about using data mining in a law enforcement setting.
Real-Time Big Data Analytics: Emerging Architecture
It provides terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis. D P Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, "Data Science for Business" provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects.