Computer Science: Chapter 6 - Database

Bit - represents the smallest unit of data a computer can handle Byte - is a group of bits representing a single character, which can be a letter, a number or another symbol Field - a group of character into a word Record - a group of related fields, such a name File - a group of records of the same type Entity - person, place, or event on which we store and maintain information Attribute - Each characteristic or quality describing a particular entity

1. Data Redundancy and Inconsistency ---Data Redundancy, is the presence of duplicate data in multiple data files so that the same data are stored in more than one place or location. ---Data Inconsistency, where the same attribute may have different values. 2. Program-Data Dependence, refers to the coupling of data stored in files and the specific programs required to update and maintain those files such that changes in programs require changes to the data 3. Lack of Flexibility, traditional file system can deliver routine scheduled reports after extensive programming efforts, but it cannot deliver ad hoc reports in a timely fashion. 4. Poor Security, due to lack of control, access to and dissemination of information may be out of control. 5. Lack of Data Sharing and Availability, because pieces of information in different files and different parts of the organization cannot be related to one another, it is virtually impossible for information to be shared or accessed in a timely manner.

Database is a collection of data organized to serve many applications efficiently by centralizing the data and controlling redundant data.

DBMS - is a software that enables an organization to centralize data, manage them efficiently, and provide access to the stored data by application programs 1. How a DBMS Solves the Problems of the Traditional File Environment ---DBMS reduces data redundancy and inconsistency by minimizing isolated file in which the same data are repeated. DBMS uncouples programs and data, enabling data to stand on their own. 2. Relational DBMS - most popular type of DBMS for today's PCs and larger computers and mainframes. Microsoft SQL Server are relational DBMS for large mainframes and mid-range computers. ---Tuples, rows are commonly referred to as records, or in very technical terms, as tuples --- Key Field, uniquely identifies each record so that record can be retrieved, updated and sorted. ---Primary key, each table in a relational database has one field that is designated as its primary key ---Foreign Key, essentially a lookup field 3. Operations of Relational DBMS - The Project operation creates a subset consisting of columns in a table, permitting the user to create new tables that contain only the information required.

Capabilities of Database Management Systems ---Data definition, DBMS have a data definition capability to specify the structure of the content of the database ---Data dictionary, is an automated or manual file that stored definitions of data elements and their characteristics. Query and Reporting, DBMS includes tools for accessing and manipulation information in databases. ---Data manipulation language, is used to add, change, delete, and retrieve the data in the database. ----Most prominent data manipulation language today is SQL - Structured Query Language

Designing Database-must understand relationship Normalization and Entity-Relationship Diagrams ---Normalization, to use a relational database model effectively, complex grouping of data must be streamlined to minimize redundant data elements and awkward many-to-many relationships. ---Referential integrity, rules to ensure that relationships between coupled tables remain consistent ---Entity-relationship diagram, database designers document their data model with an entity-relationship diagram.

Non-relations Databases, Cloud Databases, and Blockchain ---Non-relational database management systems, use a more flexible data model land are designed for managing large data sets across many distributed machines and for easily scaling up or down. 1. Cloud Databases and Distributed Databases ---Distributed database, is one that is stored in multiple physical locations. Parts or copies of the database are physically stored in one location and other parts or copies are maintained in other locations in hundred of data center around the globe 2. Blockchain ---Blockchain is a distributed database technology that enables firms and organizations to create and verify transactions on a network nearly instantaneously without a central authority. What makes a blockchain system possible and attractive to business firms is encryption and authentication of the actors and participants firms, which ensures that only legitimate actors can enter information, and only validated transactions are accepted.

The Challenge of Big Data ---Big data, to describe these data sets with volumes so huge that they are beyond the ability of typical DBMS to capture, store, and analyze Big Data is characterize by "3V" 1. Volume data 2. Variety data 3. Velocity data Big data doesn't designate any specific quantity but usually refers to data in the petabyte and exabyte range

Business Intelligence Infrastructure 1. Data Warehouses and Data Marts ---Data warehouse, is a database that stores current and historical data of potential interest to decision makers throughout the company ---Data mart, is a subset of data warehouse in which a summarized or highly focused portion of the organization's data is placed in separate database for specific population of users. 2. Hadoop-For handling unstructured and semi-structured data in vast quantities, as well as structured data, organizations are using Hadoop 3. In-Memory Computing, relies primarily on a computer's main memory (RAM) for data storage. 4. Analytic Platforms ---Analytic Platforms-Commercial database vendors have developed specialized high-speed analytic platforms using both relational and non-relational technology that are optimized for analyzing large data sets ---Data lake, is a repository for raw unstructured data or structured data that for the most part has not yet been analyzed, and the data can be accessed in many ways.

Analytical Tools: Relationships, Patterns, Trends 1. OLAP- Online Analytical Processing-supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions. 2. Data Mining, is more discovery-driven. Provides insight into corporate data that cannot be obtain with OLAP by finding hidden patterns and relationships in large databases and inferring rules from them to predict future behavior. Types of information obtainable from data mining include: a. associations are occurrences linked to a single event. b. sequences, events are linked over time c. classification recognizes patterns that describe the group to which an item belongs d. Clustering works in a manner similar to classification when no groups have been defined yet e. forecasting uses predictions in different ways. 3. Text Mining & Web Mining ---Text mining tools are or available to help business analyze these data ---Sentiment analysis software is able to mine text comments in an email message to detect favorable and unfavorable opinions about specific subjects.

Establishing an Information Policy ---Information Policy, specifies the organization's rules fir sharing disseminating, acquiring, standardizing, classifying, and inventory information. ---Data administration, is responsible for the specific policies and procedures through which data can be managed as an organizational resource ---Data governance, used to describe many of these activities ---Database administration- In close cooperation with users, the design group establishes the physical database, the logical relations among elements, and the access rules and security procedures. The functions it performs are called database administration

Ensuring Data Quality, if a database is properly designed and enterprise-wide data standards are established, duplicate or inconsistent data elements should be minimal ---Data quality audit- Analysis of data quality often begins with a data quality audit, which is structured survey of the accuracy and level of completeness of the data in information system. ---Data cleansing, also known as data scrubbing, consist of activities for detecting and correcting data in a database that are incorrect, incomplete, improperly formatted, or redundant.