big_data
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big_data [2014/07/23 15:54] – hkimscil | big_data [2014/07/23 16:21] (current) – hkimscil | ||
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Organizations | Organizations | ||
* Relational Data Model: RDBMS (Relational Database Management System), mainly implemently by SQL (Structured Query Language). | * Relational Data Model: RDBMS (Relational Database Management System), mainly implemently by SQL (Structured Query Language). | ||
- | * Entity-Relationship Data Model (ER): . . . It added additional abstraction to increase the usability of the data. In the model, each item was defined independently of its use. Therefore, developers could create new relationships between data sources without complex programming | + | * Entity-Relationship Data Model (ER): . . . It added additional abstraction to increase the usability of the data. In the model, each item was defined independently of its use. Therefore, developers could create new relationships between data sources without complex programming |
* Data warehouse in 90s | * Data warehouse in 90s | ||
* Beginning of unstructured data use -- BLOBs (Binary Large Objects) | * Beginning of unstructured data use -- BLOBs (Binary Large Objects) | ||
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* Setting architectural foundation | * Setting architectural foundation | ||
- | {{: | + | {{: |
+ | Sources of big structured data | ||
+ | * Computer-generated or Machine-generated Data | ||
+ | * Human-generated Data | ||
* Computer-generated or Machine-generated Data | * Computer-generated or Machine-generated Data | ||
+ | * Sensor data . . . . RFID tags, Smart meters, medical devices, GPS data, etc. | ||
+ | * Web log data . . . Google analytics, | ||
+ | * Point-of-sale data . . . Cashiers' | ||
+ | * Financial data | ||
* Human-generated Data | * Human-generated Data | ||
- | | + | |
+ | * Click stream data . . . . | ||
+ | * Gaming-related data . . . . | ||
+ | |||
+ | Sources of unstructured data | ||
+ | Exploring sources of unstructured data | ||
+ | |||
+ | Unstructured data is everywhere. In fact, most individuals and organizations conduct their lives around unstructured data. Just as with structured data, unstructured data is either machine generated or human generated. | ||
- | | + | Here are some examples of machine-generated unstructured data: |
- | * Web log data . . . Google analytics, | + | |
- | * Point-of-sale data . . . Cashiers' | + | * Scientific |
- | * Financial | + | * Photographs and video: This includes security, surveillance, |
+ | * Radar or sonar data: This includes vehicular, meteorological, | ||
+ | The following list shows a few examples of human-generated unstructured data: | ||
+ | * Text internal to your company: Think of all the text within documents, logs, survey results, and e-mails. Enterprise information actually represents a large percent of the text information in the world today. | ||
+ | * Social media data: This data is generated from the social media platforms such as YouTube, Facebook, Twitter, LinkedIn, and Flickr. | ||
+ | * Mobile data: This includes data such as text messages and location information. | ||
+ | * Website content: This comes from any site delivering unstructured content, like YouTube, Flickr, or Instagram. |
big_data.1406098493.txt.gz · Last modified: 2014/07/23 15:54 by hkimscil