Data warehouse data mining

By | 13.08.2018
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Learn how data mining and data warehousing are related in this article. Visit HowStuffWorks.com to learn more about how data mining and data warehousing . A.A. 04-05 Datawarehousing & Datamining 2 Outline 1. Introduction and Terminology 2. Data Warehousing 3. Data Mining • Association rules • Sequential patterns. Data Warehouse Overview analytical processing, and data mining are the three types of data warehouse applications that are discussed below.
Amazon.com: Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management) (9780070062726): Alex Berson, Stephen J. Smith: Books3.8/5(10). Enterprise data is the lifeblood of a corporation, but it's useless if it's left to languish in data silos. Data warehousing and mining provide the tools to bring. Learn how data mining and data warehousing are related in this article. Visit HowStuffWorks.com to learn more about how data mining and data warehousing . A.A. 04-05 Datawarehousing & Datamining 2 Outline 1. Introduction and Terminology 2. Data Warehousing 3. Data Mining • Association rules • Sequential patterns. Data Warehouse Overview analytical processing, and data mining are the three types of data warehouse applications that are discussed below.

Data Warehousing and Data Mining in Business Definition

Data warehousing is a subject-oriented, integrated, non-volatile, and time variant collection of data that supports management’s decision making processes (Inmon, 1996). Data mining is the process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequently gain knowledge from large databases or data warehouses (Turban et al., 2007). Both data warehousing and data mining are being applied in a wide range of businesses including retail, finance, manufacturing, transportation and aerospace.

Data Warehousing and Data Mining in Business Description *

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DATA WAREHOUSING AND DATA MINING Notes (DWDM)

Data Warehouse Overview analytical processing, and data mining are the three types of data warehouse applications that are discussed below. In other words, data warehousing is the process of compiling and organizing data into one common database, and data mining is the process of extracting meaningful. Warehousing data: A discussion of the implementation of data warehouses and analysis techniques, consisting of data mining, OLAP, and data visualization. An Introduction to Data Mining. Discovering hidden value in your data warehouse. Overview. Data mining, the extraction of hidden predictive information from large. Data mining is the process of discovering patterns in large data sets involving methods at the intersection Data analysis; Data warehouse; Decision. Collections of databases that work together are called data warehouses. This makes it possible to integrate data from multiple databases. Data.

Data Warehouse concept and Data Mining Data Warehousing and Data Mining 1. Data Warehousing and OLAP Technology Oleh: Nama: Sunaryo Tandi N I M. Data mining and warehousing - Free download as PDF File .pdf), Text File .txt) or view presentation slides online. In other words, data warehousing is the process of compiling and organizing data into one common database, and data mining is the process of extracting meaningful.


Data Warehousing & Data Mining

MIS 430 - Data Warehousing / Data Mining George Mason University Fairfax, VA Professor Kumar Mehta
Structured Query Language (SQL)
A language for defining the structure and processing of a relational database. It can be used as a stand-alone query language, or it can be embedded in application programs. SQL was developed by IBM and is accepted as a national standard by the American National Standards Institute.
(1) A group of actions that is performed on the database atomically; either all actions are committed to the database, or
none of them are.
(2) In the business world, the record of an event.
A candidate key selected to be the key of a relation.
Uniquely identifies the records (rows) in a table (relation).
An attribute that is a key of one or more relations other than the one in which it appears.
Any table that fits the definition of a relation.
A relation in first normal form in which all non-key attributes are dependent on all the keys.
A relation in second normal form that has no transitive dependencies.
A relation in BCNF in which every multivalued dependency is a functional dependency.
A normal form necessary to eliminate an anomaly where a table can be split apart but not correctly joined back together.
Also known as Project-Join Normal Form (PJ/NF).
Business intelligence (BI) systems
Information systems that assist managers and other professionals in analyzing current and past activities and in predicting future events.
Two major categories of BI systems are reporting systems and data mining systems.
An attribute or a group of attributes that identifies a unique row in a relation.
One of the candidate keys is chosen to be the primary key.
In a binary relationship, the maximum or minimum number of elements allowed on each side of the relationship.
The maximum cardinality can be 1:1, 1:N, N:1, or N:M.
The minimum cardinality can be optional/optional, optional/mandatory, mandatory/optional, or mandatory/mandatory.
A statement of a policy in a business that restricts the ways in which data can be inserted, updated, or deleted in the database.
A property of a relationship that indicates that when one row is deleted, related rows should be deleted as well.
A referential integrity action specifying that when the key of a parent row is updated, the foreign keys of matching child rows should be updated as well.
A row, record, or node on the many side of a one-to-many
relationship. See also parent.
A logical group of bytes in a row of a relation or a table.
The meaning of a column is the same for every row of the relation.
A command issued to a DBMS to make database modifications permanent. After the command has been processed, the changes are written to the database and to a log in such a way that they will survive system crashes and other failures. A commit is usually used at the end of an atomic transaction. Contrast this with rollback.
An identifier of an entity that consists of two or more attributes.
A key of a relation that consists of two or more columns.
A condition in which two or more transactions are processed against a database at the same time.
In a single CPU system, the changes are interleaved; in a multi-CPU system, the transactions can be processed simultaneously, and the changes on the database server are interleaved.
Concurrent update problem
An error condition in which one user's data changes are overwritten by another user's data changes. Also called lost update problem.
Data definition language (DDL)
A language used to describe the structure of a database.
Data manipulation language (DML)
A language used to describe the processing of a database.
A facility similar to a data warehouse, but with a restricted domain. Often, the data are restricted to particular types, business functions, or business units.
The use of statistical and mathematical techniques to find patterns in database data.
(1) A model of users' data requirements, usually expressed in terms of the entity-relationship model. It is sometimes called a users' data model.
(2) A language for describing the structure and processing of a database.
A store of enterprise data that is designed to facilitate management decision making.
A data warehouse includes not only data, but also metadata, tools, procedures, training, personnel information, and other resources that make access to the data easier and more relevant to decision makers.
A self-describing collection of related records, or, for relational databases, of related tables.
Database management system (DBMS)
A set of programs used to define, administer, and process a database and its applications.
A condition that can occur during concurrent processing in which each of two (or more) transactions is waiting to access data that the other transaction has locked. It also is called the deadly embrace.
In the entity-relationship model, the number of entities participating in a relationship.
The process of intentionally designing a relation that is not normalized. Denormalization is done to improve performance or security.
One or more attributes that functionally determine another attribute or attributes. In the functional dependency (A, B) → D, C, the attributes (A, B) are the determinant.
In the entity-relationship model, an attribute of a supertype entity that determines which subtype pertains to the supertype.
User-directed disaggregation of data used to break higher-level totals into components.
Something of importance to a user that needs to be represented in a database. In the entity-relationship model, entities are restricted to things that can be represented by a single table. See also strong entity and weak entity.
A set of entities of the same type; for example, several EMPLOYEE entity instances make up the EMPLOYEE class.
A particular occurrence of an entity; for example, Employee 100 (an EMPLOYEE) and Accounting Department (a DEPARTMENT).
(1) A logical group of bytes in a record used with file processing. (2) In the context of the relational model, a synonym for attribute.
A relationship between attributes in which one attribute or group of attributes determines the value of another.
The expressions X → Y, "X determines Y," and "Y is functionally
dependent on X" mean that given a value of X, we can determine the value of Y.
An entity that cannot logically exist without the existence of another entity. APPOINTMENT, for example, cannot exist without CLIENT to make the appointment.
To be an ID-dependent entity, the identifier of the entity must
contain the identifier of the entity on which it depends. Such
entities are a subset of a weak entity. See also strong entity and
weak entity.
In an entity, a group of one or more attributes that determine entity instances. See also nonunique identifier and unique
identifier.
A relationship that is used when the child entity is ID-dependent upon the parent entity.
A table (also called a relation) used to represent a many-to-many relationship. It contains the keys of the relations in the relationship. If it contains nonkey columns, it is called an association table. See association entity.
An entity that represents the combination of at least two other objects and that contains data about that combination. It is often used in contracting and assignment applications.
A relational algebra operation on two relations, A and B, that produces a third relation, C. A row of A is concatenated with a row of B to form a new row in C if the rows in A and B meet restrictions concerning their values. For example, A1 is an attribute in A, and B1 is an attribute in B. The join of A with B in which (A1 = B1) will result in a relation, C, having the concatenation of rows in A and B in which the value of A1 is equal to the value of B1. In theory, restrictions other than equality are allowed—a join could be made in which A1 < B1. However, such non-equal joins are not used in practice. Also known as inner join. See also natural join,compare to outer join.
(1) A group of one or more attributes that identify a unique row in a relation. Because relations cannot have duplicate rows, every relation must have at least one key that is the composite of all the attributes in the relation. A key is sometimes called a logical key. (2) With some relational DBMS products, an index on a column used to improve access and sorting speed. It is sometimes called a physical key. See also nonunique key, unique key, and physical key.
(1) The maximum number of values that an attribute can have within a semantic object. (2) In a relationship between tables, the maximum number of rows to which a row of one table can relate in the other table.
In a relationship between tables, the minimum number of rows to which a row of one table can relate in the other table.
Data concerning the structure of data in a database stored in the data dictionary.
Metadata are used to describe tables, columns, constraints, indexes, and so forth.
In a relation, a condition that exists when, to add a complete row to a table, one must add facts about two or more logically different themes.
A situation that exists when the storing of one row in a table records facts about two themes or the deletion of a row removes facts about two themes, or when a data change must be made in multiple rows for consistency.
CREATE TABLE EMPLOYEE (
EmpID char(10) PRIMARY KEY,
LastName varchar(20) NOT NULL,
FirstName varchar(20) NOT NULL,
MiddleName varchar(20) NULL,
SSN char(9) UNIQUE NOT NULL
);
SQL: CREATE TABLE (PK Constraint)
CREATE TABLE TABLE_X(
colA char(10) NOT NULL,
colB varchar(10) NOT NULL,
CONSTRAINT TblX_PK PRIMARY KEY (colA, colB)
);
Adding Column
ALTER TABLE Employee
ADD EmpID Char(10) NOT NULL;
Changing Column (data type, null constraint, unique)
ALTER TABLE Employee
ALTER EmpID Char(10) NOT NULL UNIQUE;
Deleting Column
ALTER TABLE Employee
DROP EmpID;
SQL: ALTER TABLE (Constraints)
Example: adding primary key constraints to an existing table
ALTER TABLE Employee
ADD CONSTRAINT Emp_PK PRIMARY KEY(EmpID);
Example: adding foreign key constraint to an existing table
ALTER TABLE Employee
ADD CONSTRAINT Emp_Dept_FK FOREIGN KEY(DeptID)
REFERENCES DEPT(DeptID);
Example: Dropping constraint in an existing table
ALTER TABLE Employee
DROP CONSTRAINT Emp_Dept_FK;
Syntax:
SELECT <list of column expressions>
FROM <list of tables and join operations>
WHERE <list of logical expressions for rows>
GROUP BY <list of grouping columns>
HAVING <list of logical expressions for groups>
ORDER BY <list of sorting specifications>
The WHERE/HAVING clause match criteria may include
Equals "="
Not Equals "<>"
Greater than ">"
Less than "<"
Greater than or Equal to ">="
Less than or Equal to "<="
BETWEEN value1 and value2 /
NOT BETWEEN value1 and value2
WHERE/HAVING:
AND - an intersection of the data sets
OR - a union of the data sets
WHERE only:
LIKE 'text pattern' / NOT LIKE 'text pattern'
IN (list of values) / NOT IN (list of values)
IS NULL / IS NOT NULL
SQL: Aggregation Functions
COUNT - Counts the number of rows that match the specified criteria
MIN - Finds the minimum value for a specific column for those rows matching the criteria
MAX - Finds the maximum value for a specific column for those rows matching the criteria
SUM - Calculates the sum for a specific column for those rows matching the criteria
AVG - Calculates the numerical average of a specific column for those rows matching the criteria
INSERT INTO TableName
VALUES(value1, value2, value3, ....);

INSERT INTO TableName (colName1, colName2, colName3, ...)
VALUES(value1, value2, value3, ....);
UPDATE TableName
SET column1 = value1, column2 = value2, ....
WHERE <match criteria>;
DELETE FROM TableName
WHERE <match criteria>;
SELECT <columns>
FROM <Table1> <JOIN TYPE> <Table2>
ON <Table1.columnA = Table2.columnB>
[AND Table1.columnY = Table2.columnZ]
[AND Table1.columnX = Table2.columnY]
WHERE <record match conditions>
GROUP BY <columns>
HAVING <group match conditions>;
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