Why Big Data and Where Did it Come From?

Why Big Data and Where Did it Come From? >> Introduction to Big Data

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The Internet

Wi-Fi Networks

Individual, Unconnected Hospital Databases

Social Media

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Larger storage means easier accessibility to big data for every user because it allows users to download inbulk.

Lower prices mean larger storage becomes easier to access for everyone, creating bigger amounts of data for client-facing services to work with.

It isn’t, it was just an arbitrary example of big data usage.

Companies can’t afford to own, maintain, and spend the energy to support large data storage unless the cost is sufficiently low.

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Being able to use personalized data from every single customer for personalized marketing needs.

Being able to obtain and use customer information for groups of consumers and utilize them for marketing needs.

Marketing to each customer on an individual level and suiting to their needs.

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A survey that asks your age and markets to you a specific brand.

Google ordering ads to show items based on recent and past search results.

News outlets gathering information from the internet in order to report them to the public.

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Extrapolation -> Understanding -> Reproducing

Big Data -> Better Models -> Higher Precision

Theory -> Models -> Precise Advice

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Mobile advertising benefits from data integration with location which requires big data.

Mobile advertising in and of itself is always associated with big data.

Mobile advertising allows massive cellular/mobile texting to a wide audience, thus providing large amounts of data.

Since almost everyone owns a cell/mobile phone, the mobile advertising market is large and thus requires big data to contain all the information.

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Sensor Data, Organizational Data, and Social Media

Machine Data, Organizational Data, and People

Information Networks, Map Data, and People

Machine Data, Map Data, and Social Media

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Sorted data from Amazon regarding customer info.

Weather station sensor output.

Social Media

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Social Media

Satellite Data

Disease data from Center for Disease Control.

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Organizational Data

Machine Data

People

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Integration -> Personalization -> Precision

Big Data -> Better Models -> Higher Precision

Theory -> Models -> Precise Advice

Extrapolation -> Understanding -> Reproducing

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Size of the data.

Combining streams of data and analyzing them for new insights.

Using the three major data sources: Machines, People, and Organizations.

Having data-enabled decisions and actions from the insights of new data.

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Collect data and services autonomously.

Having a specific processing speed in order to keep up with the demands of data processing.

Must have a way to interact with the user.

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The sensors used in airplanes to measure altitude.

Bringing the computation to the location of the data.

Accelerometers.

In the situation

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Very unstructured data.

The velocity of the data is very high.

Skilled people to analyze the data are hard to come by.

They cannot be modeled and stored.

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To enable ETL methods.

Since the multi-layered process is built into the Neo4j database connection.

Designed to work like the ETL process.

To allow scalable analytical solutions to big data.

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A giant centralized database to house all the data produces within an organization. Bad because it is hard to maintain as highly structured data.

Data produced from an organization that is spread out. Bad because it creates unsynchronized and invisible data.

Highly unstructured data. Bad because it does not provide meaningful results for organizations.

A giant centralized database to house all the data production within an organization. Bad because it hinders opportunity for data generation.

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Why Big Data and Where Did it Come From? >> Introduction to Big Data

Why Big Data and Where Did it Come From?

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