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2/5/2016 notes

by: loveena Cherukunnathu

2/5/2016 notes INSY 3304 - 002

loveena Cherukunnathu

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2/5/2016 notes
Database Management Systems
Karen Scott
Class Notes
25 ?




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This 4 page Class Notes was uploaded by loveena Cherukunnathu on Sunday February 7, 2016. The Class Notes belongs to INSY 3304 - 002 at University of Texas at Arlington taught by Karen Scott in Spring 2016. Since its upload, it has received 70 views. For similar materials see Database Management Systems in Information technology at University of Texas at Arlington.

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Date Created: 02/07/16
 Emerging Data Models: Big Data and NoSQL  Big Data - Means a lot of data. - Data sets that are too large to manage by traditional relational database management system. - They have access to a lot of external data made possible because of internet.  NoSQL databases - Not based on relational model (not based on SQL) - They support distributed database structures - Highly scalable, a lot of fault tolerance. - Supports large amount of data (sparse data) - They are geared toward performance than consistency. - More for decision support  Key-value data model - 2 data elements (key and value). For every key, there is a corresponding value or set of values.  Sparse data - data with large numbers of attributes. Number of data instances in the attributes is low.  Eventual consistency - as the updates propagate through the system, eventually there will be more consistency among the copies of the data.  Data Models: A Summary  Common characteristics:  Conceptual simplicity with semantic completeness  Represent the real world as closely as possible  Real-world transformations must comply with consistency and integrity characteristics  Each new data model capitalized on the shortcomings of previous models  Some models better suited for some tasks  Degrees of Data Abstraction  Database designer starts with abstracted view, then adds details  ANSI Standards Planning and Requirements Committee (SPARC) o Defined a framework for data modeling based on degrees of data abstraction (1970s)  The External Model - breaks down conceptional into functional components  End users’ view - gives the end users view of the data.  ER diagrams - we use this to represent the external views  External schema - specific representation of each external view, show entity’s, relationship, process, and constrains *** will be in the exam - Highest level of abstraction: end-user/ conceptual. Then internal, then physical model - In networking you need to get physical model  Business unit operations  Provides feedback - Facilitates designer’s job with providing how effective the designers view is.  Security constraints  Simplification - Simplifies application program development  The Conceptual Model  Global view  Conceptual schema - it integrates all external views into a single global view.  ER model - entity relationship model, most widely used  ERD - graphically represent this. - Singular noun – entity names  Easy to understand  Hardware independent? Yes  Software independent? Yes - use any type of software or RDBMS to implement the data model.  The Internal Model - representation of database as seen by DBMS - Map conceptional model to DBMS.  DBMS View - Choose the best DBMS.  Internal schema - specific representation of internal model  Hardware independent? Yes  Software independent? No  Logically independent? Yes, you can change internal model without affecting conceptional model  The Physical Model - Only create for structural dependent models  Level of abstraction? - It will describe how its physically saved on storage media.  Hardware independence? No. But Software Independent. - Does have physical independence. You can change physical model without affecting internal model


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