Structured And Unstructured Data Pdf

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structured and unstructured data pdf

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Unorganized information is costly…and for many organizations, a majority of their data is just that — unstructured and unorganized.

Email: solutions altexsoft. According to IBM, the global volume of data was predicted to reach 35 zettabytes in Since it increases daily, data scientists expect that the number will hit zettabytes in It will take million years to watch all those movies. The prevailing part of data, which is 80 percent or so, is unstructured.

Comparison of Structured vs. Unstructured Data for Industrial Quality Analysis

In my previous blog post I talk about what data is. In this article, we will see what different types of data there are. The distinction between different types of data is important because it impacts how data can be stored, how it should be organized and how easy it is to process and analyze it. This applies to all data, regardless of what sector we are looking at. In this article we will look at. Recall from this blog post that put very simply, data is nothing else than information stored in digital format.

It should be clear then, that data can take many forms. Consequently, there are many different criteria by means of which we can classify and categorize different data forms i. You might recall one data classification type from your college times. In an academic context we often distinguish between quantitative consisting of numbers and qualitative consisting of non-numbers data.

If a sociologist conducts an interview, this is qualitative data. If an economist is comparing the GDP and other economic indicators of various countries, they are dealing with qualitative data. Or, if you are working in a company a distinction of data is often made depending on what entity or business process data refers to.

For instance, in a business setting we will often speak of customer, employee and sales data. Another data classification type that is often used in a business setting is also the distinction between master and transactional data.

Master data is usually static data that rarely changes and reflects business objects that are shared across a company such as customer data the name, address and contact details of customers change relatively rarely.

Transactional data is usually non-static data with a temporal dimensions which describes and event and transactions such as product orders or website logs. There are many more data classification types and all of them can be helpful depending on the context we are in. However, arguably the most important data classification type is along the criterion of the degree of organisation.

In that we distinguish between structured, semi structured and unstructured data. Structured data is data with a high degree of organization, typically stored in a spreadsheet-like manner. Semi-structured data is data with some degree of organization. And unstructured data is data with no predefined organizational form and no specific format, so essentially everything which is not structured or semi-structured data.

As you can see, the distinction of structured, semi structured and unstructured data breaks down to how organized your data is.

But why is the degree of organization so important? There are many reasons, but the two reasons that stand out are:. If data follows a rigorous structured like in a spreadsheet from which there is no deviation, then this make the data highly machine-readable. As a result, we can analyze even large datasets very easily by harnessing computer power. In contrast, if data does not follow a rigorous structure, it might still be easy for us consume as humans but is usually not very machine-readable.

So to harness computer power to analyze it will be much more difficult. On the one hand, I could have every participants enter their name and age into an Excel sheet upon arrival. Or I could have everyone write down their name and age on a name tag i. In the first case I can directly use my computer and perform operation on the data. For example, I could use a simple tool like Excel to display all participants older than 40 years, or I could filter for a participant name to look up their age.

I could of course do it manually, but there is no software tool where I can tell my computer to give me the age of participant X we are getting there with image classification and object detection, but I hope you get the point.

As we will see, the distinction is also important because it has implications on how data can be stored. Structured data is data with a high degree of organization, usually stored in some sort of spreadsheet. Simply think about a well organized Excel sheet, which is a prime example of structured data. Even though we are currently making major progress in processing and thus also in gaining valuable insights from semi-structured and unstructured data, structured data is often considered more valuable.

The reason is that it can directly be leveraged with computer power without major the need of major pre-processing steps. We can easily use structured data for data visualisation, data analytics and machine learning.

Unfortunately, there is no data on what the distribution of data between structured, semi-structured and unstructured data looks like. This seems reasonable if you consider that a major data source of today are our smartphones, with which we listen to music, take pictures and create videos all of which is unstructured data.

Figure 1 shows customer data of Your Model Car, using a spreadsheet as an example of structured data. The tabular form and inherent structure make this type of data analysis-ready, e. Typically, structured data is stored in spreadsheets e. Excel files or in relational databases. These formats also happen to be pretty human-readable as figure 1 shows. However, this is not always necessarily the case. Another common storage format of structured data are comma separated value files CSV.

Figure 2 shows structured data in csv format. While it might look messy at first, if you look closely it follows a rigorous structure that can easily be converted into a spreadsheet-like view. Each row has a value for a product code, order number, etc. For example, every first value in a row indicates a product code. Structured data is typically stored in relational database systems. Semi-structured is data which has some degree of organization in it. It is not as rigorously structured as structured data, but also not as messy as unstructured data.

This degree of organization is typically achieved with some sort of tags or other elements with defined properties which introduce a hierarchy and system into a file. However, the order and amount of such structuring tags and elements may vary.

Therefore, the structure imposed on a dataset it not as rigorous as in structured datasets where all data has to conform to the structure of the data table spreadsheet. If wanted to see an example of semi-structured data, you have been looking at one the entire time!

You are currently reading a hypertext markup language HTML file. HTML is one example of semi-structured data, in which a text and other data is organized with tags. These tags somewhat organise this file and help your browser rendering it and making sense of it. However, on a different webpage the number and type of tags used might be completely different.

Figure 3: Example of semi-structured data. Another widely used type of semi-structured are JSON files. This figure below a JSON file containing employee data. As you can see, JSON files have an inherent tree-like structure that gives some degree of organization, but it is less strong than in a table.

Unstructured data is data with no pre-defined organizational form or specific format. Or in other words, unstructured data is any data which is not structured or semi-structured. This can literally be data of any file format which is not nicely put into a spreadsheet or some semi-structured data format.

The vast majority of all data created today is unstructured. Just think of all the text, chat, video and audio content that is generated every day around the world! Unstructured data is typically easy to consume for us humans e. But due to the lack of organization in the data, it is very cumbersome — or even impossible — for a computer to make sense of it. That is why we say that it is less machine-readable.

However, with the advent of AI and more sophisticated machine learning methods, we are currently making a lot of progress in processing and essentially teaching a machine how to make sense of unstructured data. For example, the fields of natural language processing NLP and computer vision are witnessing significant breakthroughs at the moment.

There is a plethora of examples of unstructured data. Just think of any image e. PDFs or docx or any other file type. The image below shows just one concrete example of unstructured data: a product image and description text. Even though this type of data might be easy to consume for us humans, it has no degree of organization and is therefore difficult for machines to analyse and interpret.

Figure 4: Example of unstructured data. For decades, before the dawn of unstructured data, most of the was stored in so called relational databases. The idea of such relational database is to store data in interrelated tables. Relational database are still the most prevalent type of database today, which is quite remarkable given their age.

But there is a reason for that: they are extremely powerful and versatile. However, they are also not perfect and ideal to use in any situation.

One of their shortcomings is that they cannot store unstructured data how would you store images in interrelated spreadsheets? Because the majority of today that is crated today is not structured, in the past years we have seen new storage technologies and methods mushrooming in the industry that are able to efficiently store unstructured data.

To clarify the difference between structured and unstructured data and its implications consider this example: Image you have employee data of your company, which has employees, in two formats. Second, as an image of that Excel sheet unstructured data. Now, in the image, i. To us, this comes effortlessly. However, for a machine to make sense of an image is extremely difficult. Because unlike you, what the computer sees are millions of numeric RGB codes and not an image at all. Because we are making advancements in the field of computer vision, this is not impossible for a computer anymore.

Unstructured data

Explaining what structured data is and what it means quickly leads to its counterpart, which is unstructured data. Examples of unstructured data include analogue or digital text documents, audio files, videos, and images. The challenge with such data is that it is hard to organise or manage into further forms. Only structured data can be managed and used efficiently. This is especially true for electronic data processing solutions and Internet applications. Online shops, news portals, weather services and sports sites process tremendous amounts of information. The applications can only handle data that is presented in tabular form, i.

In my previous blog post I talk about what data is. In this article, we will see what different types of data there are. The distinction between different types of data is important because it impacts how data can be stored, how it should be organized and how easy it is to process and analyze it. This applies to all data, regardless of what sector we are looking at. In this article we will look at. Recall from this blog post that put very simply, data is nothing else than information stored in digital format. It should be clear then, that data can take many forms.

Unstructured data or unstructured information is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text -heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated semantically tagged in documents. As of [update] , IDC and Dell EMC project that data will grow to 40 zettabytes by , resulting in a fold growth from the beginning of The earliest research into business intelligence focused in on unstructured textual data, rather than numerical data.

Think about pictures, videos or PDF documents. The ability to extract value from unstructured data is one of main drivers behind the quick.

What is structured, semi structured and unstructured data?

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Unstructured Data Unstructured data encompasses everything that isn't structured or semi-structured data. Text documents and the different kinds of multimedia files audio, video, photo are all types of unstructured data file formats. The reason all of this matters is because a cloud data lake allows you to quickly throw structured, semi-structured, and unstructured datasets into it and to analyze them using the specific technologies that make sense for each particular workload or use case. Table compares the three data types.

Structured vs Unstructured Data: Compared and Explained

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Metadata – Data about Data

 Я д-думал, - заикаясь выговорил Бринкерхофф.  - Я думал, что вы в Южной Америке. Лиланд Фонтейн окинул своего помощника убийственным взглядом. - Я был. Но сейчас я. ГЛАВА 69 - Эй, мистер.

Вздохнув, она просунула руку в углубление с цифровым замком и ввела свой личный код из пяти цифр. Через несколько секунд двенадцатитонная стальная махина начала поворачиваться. Она попыталась собраться с мыслями, но они упрямо возвращали ее к. Дэвид Беккер.

Сьюзан быстро проскочила мимо него и вышла из комнаты. Проходя вдоль стеклянной стены, она ощутила на себе сверлящий взгляд Хейла. Сьюзан пришлось сделать крюк, притворившись, что она направляется в туалет. Нельзя, чтобы Хейл что-то заподозрил.

Один раз Грег Хейл уже разрушил планы АНБ. Что мешает ему сделать это еще. Но Танкадо… - размышляла.  - С какой стати такой параноик, как Танкадо, доверился столь ненадежному типу, как Хейл.

Data Types: Structured vs. Unstructured Data

Теперь рука была закинута за голову, следовательно, Хейл лежал на спине. Неужели высвободился. Однако тот не подавал никаких признаков жизни. Сьюзан перевела взгляд на помост перед кабинетом Стратмора и ведущую к нему лестницу.

Жемчугами из Майорки. - Неужели из Майорки. Вы, должно быть, много путешествуете. Голос болезненно кашлянул.

 Извините, сэр… Бринкерхофф уже шел к двери, но Мидж точно прилипла к месту. - Я с вами попрощался, мисс Милкен, - холодно сказал Фонтейн.  - Я вас ни в чем не виню. - Но, сэр… - заикаясь выдавила .


  1. Alin M. 14.04.2021 at 17:23

    Unstructured Data with Structured Legacy Systems. In Aerospace Conference, IEEE (pp. ). IEEE.

  2. Rafel B. 20.04.2021 at 11:06

    as data, that cannot be stored in rows and columns in a relational database. Storing data in an unstructured form without any defined data schema is. a common.

  3. Segundino G. 21.04.2021 at 20:53

    In computer science, a data structure is a particular way of organising and storing data in a computer such that it can be accessed and modified efficiently.