Cloud and big data pdf
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- Handbook of Research on Cloud Computing and Big Data Applications in IoT
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- The rise of big data on cloud computing
The rise of big data on cloud computing Published on Jan 1, in Information Systems 2. View Paper. Add to Collection.
The rise of big data on cloud computing Published on Jan 1, in Information Systems 2. View Paper. Add to Collection. Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software.
Handbook of Research on Cloud Computing and Big Data Applications in IoT
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields columns offer greater statistical power , while data with higher complexity more attributes or columns may lead to a higher false discovery rate.
Big data was originally associated with three key concepts: volume , variety , and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.
Current usage of the term big data tends to refer to the use of predictive analytics , user behavior analytics , or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set.
Scientists encounter limitations in e-Science work, including meteorology , genomics ,  connectomics , complex physics simulations, biology, and environmental research. The size and number of available data sets has grown rapidly as data is collected by devices such as mobile devices , cheap and numerous information-sensing Internet of things devices, aerial remote sensing , software logs, cameras , microphones, radio-frequency identification RFID readers and wireless sensor networks.
By , IDC predicts there will be zettabytes of data. Relational database management systems and desktop statistical software packages used to visualize data often have difficulty processing and analyzing big data. The processing and analysis of big data may require "massively parallel software running on tens, hundreds, or even thousands of servers". Furthermore, expanding capabilities make big data a moving target.
For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration. The term big data has been in use since the s, with some giving credit to John Mashey for popularizing the term.
They represented the qualities of big data in volume, variety, velocity, veracity, and value. A definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd's relational model.
The growing maturity of the concept more starkly delineates the difference between "big data" and " business intelligence ": . Other possible characteristics of big data are: . Big data repositories have existed in many forms, often built by corporations with a special need.
Commercial vendors historically offered parallel database management systems for big data beginning in the s. For many years, WinterCorp published the largest database report. Teradata Corporation in marketed the parallel processing DBC system.
Teradata systems were the first to store and analyze 1 terabyte of data in Hard disk drives were 2. As of [update] , there are a few dozen petabyte class Teradata relational databases installed, the largest of which exceeds 50 PB.
In , Seisint Inc. This system automatically partitions, distributes, stores and delivers structured, semi-structured, and unstructured data across multiple commodity servers. Users can write data processing pipelines and queries in a declarative dataflow programming language called ECL. Data analysts working in ECL are not required to define data schemas upfront and can rather focus on the particular problem at hand, reshaping data in the best possible manner as they develop the solution.
In , LexisNexis acquired Seisint Inc. CERN and other physics experiments have collected big data sets for many decades, usually analyzed via high-throughput computing rather than the map-reduce architectures usually meant by the current "big data" movement.
In , Google published a paper on a process called MapReduce that uses a similar architecture. The MapReduce concept provides a parallel processing model, and an associated implementation was released 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 ". Studies in showed that a multiple-layer architecture was one option to address the issues that big data presents.
A distributed parallel architecture distributes data across multiple servers; these parallel execution environments can dramatically improve data processing speeds. This type of framework looks to make the processing power transparent to the end-user by using a front-end application server. The 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. A McKinsey Global Institute report characterizes the main components and ecosystem of big data as follows: .
Multidimensional big data can also be represented as OLAP data cubes or, mathematically, tensors. Array database systems have set out to provide storage and high-level query support on this data type.
Additional technologies being applied to big data include efficient tensor-based computation,  such as multilinear subspace learning ,  massively parallel-processing MPP databases, search-based applications , data mining ,  distributed file systems , distributed cache e.
Some 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 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 direct-attached memory or disk is good—data on memory or disk at the other end of an FC SAN connection is not.
The cost of an SAN at the scale needed for analytics applications is 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 [update] did not favor it.
Developed economies increasingly use data-intensive technologies. There are 4. The world's effective capacity to exchange information through telecommunication networks was petabytes in , petabytes in , 2. This also shows the potential of yet unused data i.
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. The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation,  but 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. A common government organization that makes use of big data is the National Security Administration NSA , who monitor the activities of the Internet constantly in search for potential patterns of suspicious or illegal activities their system may pick up.
Civil registration and vital statistics CRVS collects all certificates status from birth to death. CRVS is a source of big data for governments. 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.
A major practical application of big data for development has been "fighting poverty with data". At the same time, working with digital trace data instead of traditional survey data does not eliminate the traditional challenges involved when working in the field of international quantitative analysis. Priorities change, but the basic discussions remain the same.
Among the main challenges are:. Big data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.
The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase. This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data.
There is now an even greater need for such environments to pay greater attention to data and information quality. Big data in health research is particularly promising in terms of exploratory biomedical research, as data-driven analysis can move forward more quickly than hypothesis-driven research. A related application sub-area, that heavily relies on big data, within the healthcare field is that of computer-aided diagnosis in medicine. For this reason, big data has been recognized as one of the seven key challenges that computer-aided diagnosis systems need to overcome in order to reach the next level of performance.
A McKinsey Global Institute study found a shortage of 1. Private boot camps have also developed programs to meet that demand, including free programs like The Data Incubator or paid programs like General Assembly. Because one-size-fits-all analytical solutions are not desirable, business schools should prepare marketing managers to have wide knowledge on all the different techniques used in these subdomains to get a big picture and work effectively with analysts.
To understand how the media uses 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 taps 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 consumer's 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.
Channel 4 , the British public-service television broadcaster, is a leader in the field of big data and data analysis. Health insurance providers are collecting data on social "determinants of health" such as food and TV consumption , marital status, clothing size, and purchasing habits, from which they make predictions on health costs, in order to spot health issues in their clients.
It is controversial whether these predictions are currently being used for pricing. Big data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device inter-connectivity. Such mappings have been used by the media industry, companies, and governments to more accurately target their audience and increase media efficiency.
The IoT is also increasingly adopted as a means of gathering sensory data, and this sensory data has been used in medical,  manufacturing  and transportation  contexts. Kevin Ashton , the digital innovation expert who is credited with coining the term,  defines the Internet of things in this quote: "If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss, and cost.
We would know when things needed replacing, repairing, or recalling, and whether they were fresh or past their best. Especially since , big data has come to prominence within business operations as a tool to help employees work more efficiently and streamline the collection and distribution of information technology IT.
Big data can be used to improve training and understanding competitors, using sport sensors.
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Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields columns offer greater statistical power , while data with higher complexity more attributes or columns may lead to a higher false discovery rate. Big data was originally associated with three key concepts: volume , variety , and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.
PDF | This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four.
The rise of big data on cloud computing
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