Data Storage

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來自 University at Buffalo 的課程

Advanced Manufacturing Process Analysis

99 個評分

University at Buffalo

Advanced Manufacturing Process Analysis

99 個評分

課程 4（共 9 門，Specialization Digital Manufacturing & Design Technology ）

Variability is a fact of life in manufacturing environments, impacting product quality and yield. Through this course, students will learn why performing advanced analysis of manufacturing processes is integral for diagnosing and correcting operational flaws in order to improve yields and reduce costs. Gain insights into the best ways to collect, prepare and analyze data, as well as computational platforms that can be leveraged to collect and process data over sustained periods of time. Become better prepared to participate as a member of an advanced analysis team and share valuable inputs on effective implementation. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the fourth course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA

從本節課中

Data Collection

Storing big data is quite different from handling traditional data. This difference is explained in this module. The purpose of this module is to introduce various steps involved in data analysis. Data Collection, Data Storage, Data Organization and Data Pre-processing concepts are explained.

Data Storage4:44

與講師見面

Rahul Rai
Associate Professor
Mechanical and Aerospace Engineering

After data is collected, It it must be stored.

You might wonder why data can be collected and processed.

Some reasons for storage include the desire to perform

analysis in the future or recheck past analysis to add certainty to conclusions.

Data not stored or stored improperly can be lost.

Retrieving data is a costly proposition.

You store data every day if you are saving pictures on your phone,

or movies on hard drive.

Your smartphone and computer have places to store data,

so that you can retrieve things when you need them.

The storage place is called memory,

and how much you can store is measured in bytes.

The more data you need to store,

the more bytes of memory your camera or computer needs to have.

For example, megabyte is a million bytes.

For higher memory, you will need gigabytes,

terabytes, exabytes and petabytes of storage.

For large data sets,

memory devices that are much larger than

your smartphone and personal computers are needed.

Now imagine the amount of data a manufacturing enterprise can generate.

You are talking about storing massive amounts of data,

and also data that are of different types.

So you need special storage devices to store this type of data.

Also it is a good idea to store data in

a systematic manner so that you can ensure relationship between different data types.

You might have heard the word databases and servers.

A database stores data.

It is desirable to store data in

a systematic way so that the relationship between the data types can be understood.

A server again stores data,

but it stores a really large amount of data.

If you have been in the situation when you have found that

it is very hard to look at a file stored in your computer,

you have witnessed data being not organized that causes this problem.

Organizing data during storage makes it

easy to process compared to leaving it unorganized.

Organized data can be processed quicker,

it leads to reduced number of errors,

it is more efficient to process,

and it also requires less resources.

Additionally, sometimes efficient analytic algorithm

require that data be in a specific structured format.

Structured data is the data that is well-defined and formatted.

For example, data in databases are Excel sheet.

In an Excel sheet, if we need some information,

we can go to that respective column and get that information right away.

Semi-structured data is the data that does not have a proper format associated with it.

For example, data in an email or data are

stored in a Word document is semi structured data.

If you need more information,

you can go to a paragraph or a sentence directly but you need

to read that sentence or paragraph to obtain that required data.

Unstructured data is the least organized form of data that can exist.

Consider an example of unstructured data-- the images gathered by

satellite to predict and forecast weather.

If you see a series of images,

you will require specific softwares or

expert analysis that process this information to forecast the weather.

But if we just look at the individual single file,

it will be hard to process that information and

infer anything about the weather forecast.

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