Structured data relates to data that has a defined length and format. Some common examples of structured data include numbers, dates, and groups of words and numbers, which are called strings. In general, structured data follows a pattern like that in Figure 3.1. Generally, the data has predefined columns and rows. Some of the data columns could be missing, but they are still classified as structured data:
As shown in Figure 3.2, most of the structured dataset can be categorized into either computer-generated or human-generated data. Computer-generated structured data includes sensors data, web log data, financial data, and point-of-sales data. Human-generated data includes social media activities and events:
Sensor datasets are generated by sensors. Some of them include radio-frequency ID tags, smart meters, medical devices, smartwatch sensors, and Global Positioning System (GPS) data. The following snippet shows a Comma-Separated Value (CSV) example of sensor data, which includes position identifier, trip identifier, latitude, longitude, and timestamps:
"pos_id","trip_id","latitude","longitude","altitude","timestamp"
"1","3","4703.7815","1527.4713","359.9","2017-01-19 16:19:04.742113"
"2","3","4703.7815","1527.4714","359.9","2017-01-19 16:19:05.741890"
"3","3","4703.7816","1527.4716","360.3","2017-01-19 16:19:06.738842"
"4","3","4703.7814","1527.4718","360.5","2017-01-19 16:19:07.744001"
"5","3","4703.7814","1527.4720","360.8","2017-01-19 16:19:08.746266"
"6","3","4703.7813","1527.4723","361.3","2017-01-19 16:19:09.742153"
"7","3","4703.7812","1527.4724","361.8","2017-01-19 16:19:10.751257"
"8","3","4703.7812","1527.4726","362.2","2017-01-19 16:19:11.753595"
"9","3","4703.7816","1527.4732","362.9","2017-01-19 16:19:12.751208"
"10","3","4703.7818","1527.4736","363.9","2017-01-19 16:19:13.741670"
"11","3","4703.7817","1527.4737","364.6","2017-01-19 16:19:14.740717"
"12","3","4703.7817","1527.4738","365.2","2017-01-19 16:19:15.739440"
"13","3","4703.7817","1527.4739","365.4","2017-01-19 16:19:16.743568"
"14","3","4703.7818","1527.4741","365.7","2017-01-19 16:19:17.743619"
"15","3","4703.7819","1527.4741","365.9","2017-01-19 16:19:18.744670"
"16","3","4703.7819","1527.4741","366.2","2017-01-19 16:19:19.745262"
"17","3","4703.7819","1527.4740","366.3","2017-01-19 16:19:20.747088"
"18","3","4703.7819","1527.4739","366.3","2017-01-19 16:19:21.745070"
"19","3","4703.7819","1527.4740","366.3","2017-01-19 16:19:22.752267"
"20","3","4703.7820","1527.4740","366.3","2017-01-19 16:19:23.752970"
"21","3","4703.7820","1527.4741","366.3","2017-01-19 16:19:24.753518"
"22","3","4703.7820","1527.4739","366.2","2017-01-19 16:19:25.745795"
"23","3","4703.7819","1527.4737","366.1","2017-01-19 16:19:26.746165"
"24","3","4703.7818","1527.4735","366.0","2017-01-19 16:19:27.744291"
"25","3","4703.7818","1527.4734","365.8","2017-01-19 16:19:28.745326"
"26","3","4703.7818","1527.4733","365.7","2017-01-19 16:19:29.744088"
"27","3","4703.7817","1527.4732","365.6","2017-01-19 16:19:30.743613"
"28","3","4703.7819","1527.4735","365.6","2017-01-19 16:19:31.750983"
"29","3","4703.7822","1527.4739","365.6","2017-01-19 16:19:32.750368"
"30","3","4703.7824","1527.4741","365.6","2017-01-19 16:19:33.762958"
"31","3","4703.7825","1527.4743","365.7","2017-01-19 16:19:34.756349"
"32","3","4703.7826","1527.4746","365.8","2017-01-19 16:19:35.754711"
....................................................................
Structured human-generated data can be input data, click-stream data, or gaming-related data. Input data can be any piece of data that a human might feed into a computer, such as a name, phone, age, email address, income, physical addresses, or non-free-form survey responses. This data can be helpful for understanding basic customer behavior.
Click-stream data can generate a lot of data when users navigate through websites. This data can be recorded and then used to analyze and determine customer behavior and buying patterns. This data can be utilized to learn how a user behaves on the website, discover the process followed by the user on the website, and find out a flaw in the process.
Gaming-related data includes activities of the users while they are playing games. Every year, more and more people are using the internet to play games on a variety of platforms, including computers and consoles. Users browse on the web, send emails, chat, and stream high-definition video.
This data can be helpful in learning how end users go through a gaming portfolio. Figure 3.3 shows an example of how gaming-related data looks:
Structured is a regulated format for presenting information about a page and classifying the page's content. These datasets are of finite length and in a regular format. One of the real usages of a structured dataset is how Google uses structured data that it finds on the web to understand the content of the page, as well as to gather information about the web and the world in general. For example, here is a JSON-LD structured snippet that might appear on the contact page of the Unlimited Ball Bearings corporation, describing their contact information:
<script type="application/ld+json">
{
"@context": "http://schema.org",
"@type": "Organization",
"url": "http://www.example.com",
"name": "Unlimited Ball Bearings Corp.",
"contactPoint": {
"@type": "ContactPoint",
"telephone": "+1-401-555-1212",
"contactType": "Customer service"
}
}
</script>