Big Data Variety
Big Data Variety refers to the diverse types of data that can be collected and analyzed, including structured, unstructured, and semi-structured data. This encompasses a wide range of data sources such as text, images, videos, sensor data, and more. Managing and analyzing this variety of data is a key challenge in the field of big data analytics.
6 Key excerpts on "Big Data Variety"
eBook - ePubRecent Trends in Communication and Electronics
Proceedings of the International Conference on Recent Trends in Communication and Electronics (ICCE-2020), Ghaziabad, India, 28-29 November, 2020
- Sanjay Sharma, Astik Biswas, Brajesh Kumar Kaushik, Vibhav Sachan, Sanjay Sharma, Astik Biswas, Brajesh Kumar Kaushik, Vibhav Sachan(Authors)
- 2021(Publication Date)
- CRC Press(Publisher)
...A database stores the data in the form of tables with rows and columns, for example: relational data. Unstructured data is unorganized data or the data which does not have a predefined data model, for example: Word, Text, PDF,Media logs. Semi-structured data does not follow any formal data model, but does contain some markers or tags that can separate the elements into various hierarchies. An example of such data is JSON (thestructure that DataAccess uses by default),.csv files, XML, tab delimited files etc. Various studies and research in the field of Big Data have shown that if we can find a way to manage and process the data in an effective way then Big Data has the capacity to save time and money,boost efficiency and improve decision making in the fields of fraud control, weather forecasting, health and medicines, national security, business areas, education and traffic control. 2 The 4 V'S Of Big Data The whole theory of Big Data revolves around the 3 V’s, namely, volume, variety and velocity. A fourth V has also been now introduced which expands to veracity. Figure 1. The four V's of big data. Below is the detailed description of these V’s which form the building block for Big Data: 2.1 Volume It is the huge amount of data that exists today which is the reason for the discovery of the term “Big Data”. Therefore, volume of the data is the core feature of “Big Data”. Today, data generation is increasing exponentially which can be quantified not in terms of terabytes but zettabytes and brontobytes. Today, the data generated every minute is the same amount which was generated between the oldest date and 2008. Hence, the available conventional means are of no use in the management of such a massive amount of data...
eBook - ePubApplication of Big Data for National Security
A Practitioner's Guide to Emerging Technologies
- Babak Akhgar, Gregory B. Saathoff, Hamid R Arabnia, Richard Hill, Andrew Staniforth, Petra Saskia Bayerl(Authors)
- 2015(Publication Date)
- Butterworth-Heinemann(Publisher)
...These data are more complex to explore, and their analytical complexity is high in terms of capture, storage, processing, and resolving meaningful queries from them. More than 80% of data generated today are unstructured as a result of recording event data from daily activities. Unstructured data are also generated by both machine and human sources. Some machine-generated data include image and video files generated from satellite and traffic sensors, geographical data from radars and sonar, and surveillance and security data from closed-circuit television (CCTV) sources. Human-generated data include social media data (e.g., Facebook and Twitter updates) (Murtagh, 2013 ; Wigan and Clarke, 2012), data from mobile communications, Web sources such as YouTube and Flickr, e-mails, documents, and spreadsheets. Semi-structured Data Semi-structured data are a combination of both structured and unstructured data. They still have the data organized in chunks, with similar chunks grouped together. However, the description of the chunks in the same group may not necessarily be the same. Some of the attributes of the data may be defined, and there is often a self-describing data model, but it is not as rigid as structured data. In this sense, semi-structured data can be viewed as a kind of structured data with no rigid relational integration among datasets. The data generated by electronic data interchange sources, e-mail, and XML data can be categorized as semi-structured data. The Five V’s of Big Data As discussed before, the conversation of Big Data often starts with its volume, velocity, and variety. The characteristics of Big Data—too big, too fast, and too hard—increase the complexity for existing tools and techniques to process them (Courtney, 2012a ; Dong and Srivatsava, 2013). The core concept of Big Data theory is to extract the significant value out of the raw datasets to drive meaningful decision making...
eBook - ePubCreating Smart Enterprises
Leveraging Cloud, Big Data, Web, Social Media, Mobile and IoT Technologies
- Vivek Kale(Author)
- 2017(Publication Date)
- Auerbach Publications(Publisher)
...The answer to these challenges is a scalable, integrated computer systems hardware and software architecture designed for parallel processing of Big Data computing applications. This chapter explores the challenges of Big Data computing. 7.1.1 What Is Big Data? Big Data can be defined as volumes of data available in varying degrees of complexity, generated at different velocities and varying degrees of ambiguity that cannot be processed using traditional technologies, processing methods, algorithms, or any commercial off-the-shelf solutions. Data defined as Big Data includes weather, geospatial, and geographic information system (GIS) data; consumer-driven data from social media; enterprise-generated data from legal, sales, marketing, procurement, finance and human-resources departments; and device-generated data from sensor networks, nuclear plants, X-ray and scanning devices, and airplane engines (Figures 7.1 and 7.2). Figure 7.1 4V characteristics of Big Data. Figure 7.2 Use cases for Big Data computing. 7.1.1.1 Data Volume The most interesting data for any organization to tap into today is social media data. The amount of data generated by consumers every minute provides extremely important insights into choices, opinions, influences, connections, brand loyalty, brand management, and much more. Social media sites not only provide consumer perspectives but also competitive positioning, trends, and access to communities formed by common interest. Organizations today leverage the social media pages to personalize marketing of products and services to each customer. Many additional applications are being developed and are slowly becoming a reality...
eBook - ePubBig Data Analytics
Turning Big Data into Big Money
- Frank J. Ohlhorst(Author)
- 2012(Publication Date)
- Wiley(Publisher)
...Pharmaceutical companies and energy companies have leveraged Big Data for more tangible results, such as drug testing and geophysical analysis. The New York Times has used Big Data tools for text analysis and Web mining, while the Walt Disney Company uses them to correlate and understand customer behavior in all of its stores, theme parks, and Web properties. Big Data plays another role in today’s businesses: Large organizations increasingly face the need to maintain massive amounts of structured and unstructured data—from transaction information in data warehouses to employee tweets, from supplier records to regulatory filings—to comply with government regulations. That need has been driven even more by recent court cases that have encouraged companies to keep large quantities of documents, e-mail messages, and other electronic communications, such as instant messaging and Internet provider telephony, that may be required for e-discovery if they face litigation. WHERE IS THE VALUE? Extracting value is much more easily said than done. Big Data is full of challenges, ranging from the technical to the conceptual to the operational, any of which can derail the ability to discover value and leverage what Big Data is all about. Perhaps it is best to think of Big Data in multidimensional terms, in which four dimensions relate to the primary aspects of Big Data. These dimensions can be defined as follows: 1. Volume. Big Data comes in one size: large. Enterprises are awash with data, easily amassing terabytes and even petabytes of information. 2. Variety. Big Data extends beyond structured data to include unstructured data of all varieties: text, audio, video, click streams, log files, and more. 3. Veracity. The massive amounts of data collected for Big Data purposes can lead to statistical errors and misinterpretation of the collected information...
eBook - ePubIt's All Analytics!
The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government
- Scott Burk, Gary D. Miner(Authors)
- 2020(Publication Date)
- Productivity Press(Publisher)
...The most common form of unstructured data is text. Other examples include video, audio, images and generally analog data in any form. It is estimated that text alone (structured and semi-structured) accounts for 75–80% of the entire world’s data (see Miner et al., 2012). This number may well continue to rise with the prominence of the Internet of Things (IoT). Semi-structured data is a form of structured data that does not obey the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data (see Buneman, 1997). Semi-structured data does not have the same level of organization and predictability of structured data. The data does not reside in fixed fields or records but does contain elements that can separate the data into various hierarchies. Most people are familiar with CSV files that can be imported into databases and spreadsheets. They provide a minimal structure to the data. XML is a format that has been around for twenty years, but its use has really taken off in the last five to ten years. JSON (JavaScript Object Notation) is one of the most popular forms of semi-structured data today. Last, in this section, we discuss qualitative data and quantitative data (also see Chapter 4, where we discussed the four scales of measurement and data formats). Qualitative data are observed, but generally cannot be measured with a numerical result. Examples might be color, breed of dog, state of residence and phone brand. Quantitative data can be measured on numeric scales such as the number of readmissions per year, per member per month (PMPM) insurance rates, Gross Domestic Product (GDP) and revenue per year. What Is Big Data? There are various reports of who officially coined the term “Big Data” and of where it actually started...
eBook - ePubInnovating Analytics
How the Next Generation of Net Promoter Can Increase Sales and Drive Business Results
- Larry Freed(Author)
- 2013(Publication Date)
- Wiley(Publisher)
...Remember microfiche? Remember stacks of old, yellowing newspapers and magazines in libraries? No more. I bet my sons have never even used microfiche. With the amount of digital data doubling every three years, as of 2013 less than 2 percent of all stored information is nondigital. An extraordinary change. So what is a workable definition of big data? For me, it is the explosion of structured and unstructured data about people caused by the digitization and networking of everything: computers, smartphones, GPS devices, embedded microprocessors, and sensors, all connected by the mobile Internet that is generating data about people at an exponential rate. Big data is driven by the three Vs: an increasing Volume of data with a wide range of Variety and gathered and processed at a higher Velocity. Big Data Volume The increase in volume provides us a bigger set of data to manipulate. This provides higher accuracy, a lower margin of error, and the ability to analyze the data into many more discrete segments. As entrepreneur and former director of the MIT Media Lab Frank Moss explains in an interview on MSN 1 : Every time we perform a search, tweet, send an e-mail, post a blog, comment on one, use a cell phone, shop online, update our profile on a social networking site, use a credit card, or even go to the gym, we leave behind a mountain of data, a digital footprint, that provides a treasure trove of information about our lifestyles, financial activities, health habits, social interactions, and much more. He adds that this trend has been “accelerated by the spectacular success of social networks like Facebook, Twitter, Foursquare, and LinkedIn and video- or picture-sharing services like YouTube and Flickr. When acting together, these services generate exponential rates of growth of data about people in astonishingly short periods of time.” More statistics show the scope of big data...
