Nikita rajbhoj(a 50)
- 1. G.S. Moze College of Engineering, BALEWADI, PUNE A Seminar Report On Seminar Title SUBMITTED BY NAME: Nikita Sanjay Rajbhoj ROLL NO: A 50 CLASS: TE (A) GUIDED BY PROF. Ashwini jadhav COMPUTER ENGINEERING DEPARTMENT Academic Year: 2021-22
- 2. G.S. Moze College of Engineering, BALEWADI, PUNE CERTIFICATE This is to certify that Miss. Nikita Sanjay Rajbhoj, Roll No. A40 a student of T.E. (Computer Engineering Department) Batch 2021-2022, has satisfactorily completed a seminar report on “Big Data” under the guidance of prof. Ashwini jadhav towards the partial fulfilment of the third year Computer Engineering Semester I of Pune University. Prof. Ashwini jadhav Prof. Bharti Kudale Seminar Guide Head of Department, Computer Engineering Date: Place:
- 3. Acknowledgement I would like to thank respected Prof. Ashwini jadhav mam for giving me such a wonderful Opportunity to expand my knowledge for my own branch and giving me guidelines to present a seminar report. It helped me a lot to realize of what we study for. Secondly, I would like to thank my parents who patiently helped me as i went through my work and helped to modify and eliminate some of the irrelevant or un-necessary stuffs. Thirdly, I would like to thank my friends who helped me to make my work more organized and well-stacked till the end. Next, I would thank Microsoft for developing such a wonderful tool like MS Word. It helped my work a lot to remain error-free. Last but clearly not the least, I would thank The Almighty for giving me strength to complete my report on time.
- 4. Content Abstract Introduction Definition Characteristics Architecture Technologies Applications Conclusion Reference
- 5. Abstract Big data analytics is an area of rapidly growing diversity. It requires massive performances, scalability and fault tolerance. Existing platforms cannot scale to big data volumes, load data too slowly, respond to queries too slowly, lack processing capacity for analytics and cannot handle mixed workloads. Traditional data warehousing is a large but relatively slow producer of information to analytics users and mostly idea for analyzing structured data from various system. Hadoop-based platform is well suited deal with not only structured data but also semi structured and unstructured data, and provides scalability and fault tolerance. Scale-out storage system meets the needs of big data challenges and can scale to hundreds of petabytes without difficulty.
- 6. 1) Introduction Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reductions and reduced risk. Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on." Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. 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. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 Exabyte (2.5×1018) of data were created; The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization. Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook that can handle the available data set. Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers". What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
- 7. 2) Definition Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data. Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale. In a 2001 research report and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data. In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." Additionally, a new V "Veracity" is added by some organizations to describe it. If Gartner’s definition (the 3Vs) is still widely used, the growing maturity of the concept fosters a more sound difference between big data and Business Intelligence, regarding data and their use: Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends etc.; Big data uses inductive statistics and concepts from nonlinear system identification to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density to reveal relationships, dependencies and perform predictions of outcomes and behaviors. A more recent, consensual definition states that "Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value".
- 8. 3) Characteristics Big data can be described by the following characteristics: Volume – The quantity of data that is generated is very important in this context. It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered Big Data or not. The name ‘Big Data’ itself contains a term which is related to size and hence the characteristic. Variety - The next aspect of Big Data is its variety. This means that the category to which Big Data belongs to is also a very essential fact that needs to be known by the data analysts. This helps the people, who are closely analyzing the data and are associated with it, to effectively use the data to their advantage and thus upholding the importance of the Big Data. Velocity - The term ‘velocity’ in the context refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development. Variability - This is a factor which can be a problem for those who analyse the data. This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively. Veracity - The quality of the data being captured can vary greatly. Accuracy of analysis depends on the veracity of the source data. Complexity - Data management can become a very complex process, especially when large volumes of data come from multiple sources. These data need to be linked, connected and correlated in order to be able to grasp the information that is supposed to be conveyed by these data. This situation, is therefore, termed as the ‘complexity’ of Big Data. Factory work and Cyber-physical systems may have a 6C system: Connection (sensor and networks), Cloud (computing and data on demand), Cyber (model and memory), content/context (meaning and correlation), community (sharing and collaboration), and customization (personalization and value). In this scenario and in order to provide useful insight to the factory management and gain correct content, data has to be processed with advanced tools (analytics and algorithms) to generate meaningful information. Considering the presence of visible and invisible issues in an industrial factory, the information generation algorithm has to be capable of detecting and addressing invisible issues such as machine degradation, component wear, etc. in the factory floor.
- 9. 4) Architecture In 2000, Seisint Inc. developed C++ based distributed file sharing framework for data storage and querying. Structured, semi-structured and/or unstructured data is stored and distributed across multiple servers. Querying of data is done by modified C++ called ECL which uses apply scheme on read method to create structure of stored data during time of query. In 2004 LexisNexis acquired Seisint Inc. and 2008 acquired ChoicePoint, Inc. and their high speed parallel processing platform. The two platforms were merged into HPCC Systems and in 2011 was open sourced under Apache v2.0 License. Currently HPCC and Quantcast File Systemare the only publicly available platforms capable of analyzing multiple exabytes of data. In 2004, Google published a paper on a process called MapReduce that used such an architecture. The MapReduce framework provides a parallel processing model and associated implementation to process huge amounts of data. With MapReduce, queries are split and distributed across parallel nodes and processed in parallel (the Map step). The results are then gathered and delivered (the Reduce step). The framework was very successful, so others wanted to replicate the algorithm. Therefore, an implementation of the MapReduce framework was adopted by an Apache open source project named Hadoop. MIKE2.0 is an open approach to information management that acknowledges the need for revisions due to big data implications in an article titled "Big Data Solution Offering". The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records. Recent studies show that the use of a multiple layer architecture is an option for dealing with big data. The Distributed Parallel architecture distributes data across multiple processing units and parallel processing units provide data much faster, by improving processing speeds. This type of architecture inserts data into a parallel DBMS, which implements the use of MapReduce and Hadoop frameworks. This type of framework looks to make the processing power transparent to the end user by using a front end application server. Big Data Analytics for Manufacturing Applications can be based on a 5C architecture (connection, conversion, cyber, cognition, and configuration). Big Data Lake - With the changing face of business and IT sector, capturing and storage of data has emerged into a sophisticated system. The big data lake allows an organization to shift its focus from centralized control to a shared model to respond to the changing dynamics of information management. This enables quick segregation of data into the data lake thereby reducing the overhead time.
- 10. 5) Technologies Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. A 2011 McKinsey report suggests suitable technologies include A/B testing, crowdsourcing, data fusion and integration, genetic algorithms, machine learning, natural language processing, signal processing, simulation, time series analysis and visualisation. Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and computing resources) and the Internet. Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS. DARPA’s Topological Data Analysis program seeks the fundamental structure of massive data sets and in 2008 the technology went public with the launch of a company called Ayasdi. The practitioners of big data analytics processes are generally hostile to slower shared storage, preferring direct-attached storage (DAS) in its various forms from solid state drive (SSD) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—Storage area network (SAN) and Network-attached storage (NAS) —is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost. Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques. There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of 2011 did not favour it.[
- 11. 6) Applications Big data has increased the demand of information management specialists in that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have spent more than $15 billion on software firms specializing in data management and analytics. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year: about twice as fast as the software business as a whole. Developed economies make increasing use of data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide and between 1 billion and 2 billion people accessing the internet Between 1990 and 2005, more than 1 billion people worldwide entered the middle class which means more and more people who gain money will become more literate which in turn leads to information growth. The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007and it is predicted that the amount of traffic flowing over the internet will reach 667 exabytes annually by 2014. It is estimated that one third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications. This also shows the potential of yet unused data (i.e. in the form of video and audio content). While many vendors offer off-the-shelf solutions for Big Data, experts recommend the development of in-house solutions custom-tailored to solve the company's problem at hand if the company has sufficient technical capabilities. 6.1) Government The use and adoption of Big Data within governmental processes is beneficial and allows efficiencies in terms of cost, productivity, and innovation. That said, this process does not come without its flaws. Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome. Below are the thought leading examples within the Governmental Big Data space. 6.2)United States of America In 2012, the Obama administration announced the Big Data Research and Development Initiative, to explore how big data could be used to address important problems faced by the government. The initiative is composed of 84 different big data programs spread across six departments. Big data analysis played a large role in Barack Obama's successful 2012 re-election campaign. The United States Federal Government owns six of the ten most powerful supercomputers in the world. The Utah Data Center is a data center currently being constructed by the United States National Security Agency. When finished, the facility will be able to handle a large amount of information collected by the NSA over the Internet. The exact amount of
- 12. storage space is unknown, but more recent sources claim it will be on the order of a few exabytes. 6.3) India Big data analysis was, in parts, responsible for the BJP and its allies to win a highly successful Indian General Election 2014. The Indian Government utilises numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation 6.4)United Kingdom Examples of uses of big data in public services: Data on prescription drugs: by connecting origin, location and the time of each prescription, a research unit was able to exemplify the considerable delay between the release of any given drug, and a UK-wide adaptation of the National Institute for Health and Care Excellence guidelines. This suggests that new/most up-to-date drugs take some time to filter through to the general patient. Joining up data: a local authority blended data about services, such as road gritting rotas, with services for people at risk, such as 'meals on wheels'. The connection of data allowed the local authority to avoid any weather related delay. 6.5) International development Research on the effective usage of information and communication technologies for development (also known as ICT4D) suggests that big data technology can make important contributions but also present unique challenges to International development. Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, and natural disaster and resource management. However, longstanding challenges for developing regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns with big data such as privacy, imperfect methodology, and interoperability issues. 6.6) Manufacturing Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing. Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information. A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data. Vast amount of sensory data in addition to historical data construct the big data
- 13. in manufacturing. The generated big data acts as the input into predictive tools and preventive strategies such as Prognostics and Health Management (PHM). 6.7) Cyber-Physical Models Current PHM implementations mostly utilize data during the actual usage while analytical algorithms can perform more accurately when more information throughout the machine’s lifecycle, such as system configuration, physical knowledge and working principles, are ncluded. There is a need to systematically integrate, manage and analyze machinery or process data during different stages of machine life cycle to handle data/information more efficiently and further achieve better transparency of machine health condition for manufacturing industry. With such motivation a cyber-physical (coupled) model scheme has been developed. Please see http://www.imscenter.net/cyber-physical-platform The coupled model is a digital twin of the real machine that operates in the cloud platform and simulates the health condition with an integrated knowledge from both data driven analytical algorithms as well as other available physical knowledge. It can also be described as a 5S systematic approach consisting of Sensing, Storage, Synchronization, Synthesis and Service. The coupled model first constructs a digital image from the early design stage. System information and physical knowledge are logged during product design, based on which a simulation model is built as a reference for future analysis. Initial parameters may be statistically generalized and they can be tuned using data from testing or the manufacturing process using parameter estimation. After which, the simulation model can be considered as a mirrored image of the real machine, which is able to continuously record and track machine condition during the later utilization stage. Finally, with ubiquitous connectivity offered by cloud computing technology, the coupled model also provides better accessibility of machine condition for factory managers in cases where physical access to actual equipment or machine data is limited. 6.8) Media 6.8.1) Internet of Things (IoT) To understand how the media utilises Big Data, it is first necessary to provide some context into the mechanism used for media process. It has been suggested by Nick Couldry and Joseph Turow that practitioners in Media and Advertising approach big data as many actionable points of information about millions of individuals. The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead tap into consumers with technologies that reach targeted people at optimal times in optimal locations. The ultimate aim is to serve, or convey, a message or content that is (statistically speaking) in line with the consumers mindset. For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities. Targeting of consumers (for advertising by marketers) Data-capture
- 14. Big Data and the IoT work in conjunction. From a media perspective, data is the key derivative of device inter connectivity and allows accurate targeting. The Internet of Things, with the help of big data, therefore transforms the media industry, companies and even governments, opening up a new era of economic growth and competitiveness. The intersection of people, data and intelligent algorithms have far-reaching impacts on media efficiency. The wealth of data generated allows an elaborate layer on the present targeting mechanisms of the industry. 6.8.2) Technology eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising. Inside eBay’s 90PB data warehouse Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux- based and as of 2005 they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB. Facebook handles 50 billion photos from its user base. As of August 2012, Google was handling roughly 100 billion searches per month. 6.9) Private sector 6.9.1) Retail Walmart handles more than 1 million customer transactions every hour, which are imported into databases estimated to contain more than 2.5 petabytes (2560 terabytes) of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress. 6.9.2) Retail Banking FICO Card Detection System protects accounts world-wide. The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates. 6.9.3) Real Estate Windermere Real Estate uses anonymous GPS signals from nearly 100 million drivers to help new home buyers determine their typical drive times to and from work throughout various times of the day. 6.9.4) Science The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.99995% of these streams, there are 100 collisions of interest per second.
- 15. As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication. If all sensor data were to be recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times more than all the other sources combined in the world. The Square Kilometre Array is a telescope which consists of millions of antennas and is expected to be operational by 2024. Collectively, these antennas are expected to gather 14 exabytes and store one petabyte per day. It is considered to be one of the most ambitious scientific projects ever undertaken. 6.9.5) Science and Research When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2016 it is anticipated to acquire that amount of data every five days. Decoding the human genome originally took 10 years to process, now it can be achieved in less than a day: the DNA sequencers have divided the sequencing cost by 10,000 in the last ten years, which is 100 times cheaper than the reduction in cost predicted by Moore's Law. The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster.
- 16. 7) Conclusion The availability of Big Data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. 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 if both business and technology professionals continue to work together and deliver on the promise. 8) Reference www.google.com www.wikipedia.com www.studymafia.org