Aborts all uncommited changes

Databased is rolled to it's previos state

The size of the locked resource:

Database-level..Table-level..Page-level..Row-level

Assumes that no transaction conflit(s) will occur.

DBMS proccesses a transaction to a temporary filel checks weather conflict occured.

Assumes conflict(s) with occur:

Lcoks are issued before a transaction is processed, and then the locks are released.

Guarantees serializability

one of the most common techniques used to achieve this

Transactions are allowed to obtain as many locks as necessary (growing phase)

Once the first lock is released (shrinking phase), no additional locks can be obtained

Two-phase locking doesn't prevent deadlocks

Abort a transaction if possibility of deadlock

Reschedule transaction for later execution

DBMS periodically tests database for deadlocks

If found, one transaction ("victim") is rolled back

Standard languages (Java, C#)

Scripting Languages (Javascript,VBScript,PHP)

SQL Command prompt (SQL*Plus)

Performance

Compiled Once

Server Side computation

executable code is cached and shared

grouping SQL statements allow for single call exection

SQL itself does not support control statements such as looping operations

SQL-99 Standard defines the use.

A procedure that is automatically executed by the RDBMS when a given data manipulation event occurs.

Often used to enforce referential integrity.

Data extraction, transformation, and loading tools

Data store (data warehouse or Data mart)

Data Query and analysis tool (OLAP)

Data Presentation and visualtion tools (dashboard)

(Transactional databases)

Stored in highly normalized tables in a relational database

Dynamically updated

Focus on traditional information systems

(Data Warehouses)

Stored in formats that facilitate data extraction, data analysis, and decision making

Often aggregated

Often with redundancies.

1.Collect and storing ops data

2.Aggregating the ops data into decision support data

3.Analyzing the decision support data to generate info

4.Presentating the info to the end user to support decision-making

5.Making business decision (and generating more data)

6.Monitoring results to evaluate outcomes of the business decisions

Provides for a comprehensive and consistent definition of all data in an organization

Ensures uniform and consistent views of all data

Supports proper Governance

For controlling and monitoring business health

Creates accountability

Integrated - Consistent format and meaning.

Subject-oriented - Organized to answer questions.

Time-variant - captures and represents the flow of data over time.

Nonvolatile - Once the data enters the warehouse, it's never removed.

~1 to 3 years to implement

Less organizational commitment

Lower Cost

Shorter implementation time

~6 months - 1 year

Graphical User Interface

Analytic processing logic

Data-processing logic

Capacity for multi-deminsional analysis

Used with both transactional and Data warehouses

Data is stored in multi-dimensional arrays

Typically visualized as being stored as a Data Cube

Data Retrieval is much quicker than with standard  relational databases

Provides opportunity to "Slice and dice" data

Foundation for multi-dimensional OLAP

Associated with a particular type of (aggregated) data.

Ex. Sales Table

Attributes provide descriptive information about the facts within a given dimension.

Ex. Product, time, and location tables.

Learning by example

Use of training data which has correct answers

Create a model by running the algorithm on the training data

Does not use training data

Classes may not be known in advance

Given a collection of records

Each record contains a set of attributes, one of the attributes is the dependent variable/class

Find a model  to predict the class attribute as a function of the values of the other attributes

Goal: previously unseen records should be assigned to a class as accurately as possible

Basic idea:

Look at characteristics / attributes

“If it walks like a duck and quacks like a duck, then it’s probably a duck”

Requires three things

-The set of stored records

-Distance Metric to compute the distance between records

-The value of k, the number of nearest neighbors to retrieve

To classify an unknown record:

-Compute distance to other training records

-Identify k nearest neighbors 

-Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote, weighted distance)

If k is too small, the model is sensitive to noise

If k is too large, neighborhood may include too many points from other classes

An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks.

They are usually used to model complex relationships between inputs and outputs or to find patterns in data.

Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that

Data points in one cluster are more similar to one another

Data points in separate clusters are less similar to one another

Similarity Measures:

Euclidean Distance (if attributes are continuous)

Other Problem-specific Measures

Clustering Points:  Twitter feeds / blog comments

Similarity Measure:  How many words are common in these “documents” (after some word filtering)

Applications:  

Identify issues with a product more quickly and with greater detail

Identify the occurrence of / details about a disaster event as it is in the process of occurring (used for flooding in Oklahoma and North Dakota)