Introduction to Big Data and OLAP , Data Mining

Introduction to Big Data 

& Basic Data Analysis

Big Data EveryWhere!

•       Lots of data is being collected 

and warehoused

–   Web data, e-commerce

–   purchases at department/

grocery stores

–   Bank/Credit Card 

transactions

–   Social Network

How much data?

•        Google processes 20 PB a day (2008)

•        Wayback Machine has 3 PB + 100 TB/month (3/2009)

•        Facebook has 2.5 PB of user data + 15 TB/day (4/2009)

•        eBay has 6.5 PB of user data + 50 TB/day (5/2009)

•        CERN’s Large Hydron Collider (LHC) generates 15 PB a year

 

Introduction to Big Data
& Basic Data Analysis

Big Data EveryWhere!

•       Lots of data is being collected
and warehoused

–   Web data, e-commerce

–   purchases at department/
grocery stores

–   Bank/Credit Card
transactions

–   Social Network

How much data?

•        Google processes 20 PB a day (2008)

•        Wayback Machine has 3 PB + 100 TB/month (3/2009)

•        Facebook has 2.5 PB of user data + 15 TB/day (4/2009)

•        eBay has 6.5 PB of user data + 50 TB/day (5/2009)

•        CERN’s Large Hydron Collider (LHC) generates 15 PB a year

 

 

 

 

 

The Earthscope

•  The Earthscope is the world's largest science project. Designed to track North America's geological evolution, this observatory records data over 3.8 million square miles, amassing 67 terabytes of data. It analyzes seismic slips in the San Andreas fault, sure, but also the plume of magma underneath Yellowstone and much, much more. (http://www.msnbc.msn.com/id/44363598/ns/technology_and_science-future_of_technology/#.TmetOdQ--uI)

 

Type of Data

•       Relational Data (Tables/Transaction/Legacy Data)

•       Text Data (Web)

•       Semi-structured Data (XML)

•       Graph Data

–   Social Network, Semantic Web (RDF), …

 

•       Streaming Data

–   You can only scan the data once

 

 

 

What to do with these data?

•       Aggregation and Statistics

–   Data warehouse and OLAP

•       Indexing, Searching, and Querying

–   Keyword based search

–   Pattern matching (XML/RDF)

•       Knowledge discovery

–   Data Mining

–   Statistical Modeling

 

 

Statistics 101

Random Sample and Statistics

•         Population: is used to refer to the set or universe of all entities under study.

•         However, looking at the entire population may not be feasible, or may be too expensive.

•         Instead, we draw a random sample from the population, and compute appropriate statistics from the sample, that give estimates of the corresponding population parameters of interest.

Statistic

•        Let Si denote the random variable corresponding to data point xi , then a statistic ˆθ is a function ˆθ : (S1, S2, · · · , Sn) → R.

 

•        If we use the value of a statistic to estimate a population parameter, this value is called a point estimate of the parameter, and the statistic is called as an estimator of the parameter.

Empirical Cumulative Distribution Function

Where

Example

Measures of Central Tendency (Mean)

Population Mean:

Measures of Central Tendency (Median)

Population Median:

Example

Measures of Dispersion (Range)

Range:

Measures of Dispersion (Inter-Quartile Range)

Inter-Quartile Range (IQR):

Measures of Dispersion
(Variance and Standard Deviation)

Measures of Dispersion
(Variance and Standard Deviation)

Univariate Normal Distribution

Multivariate Normal Distribution

OLAP and Data Mining

Warehouse Architecture

Star Schemas

•                 A star schema  is a common organization for data at a warehouse.  It consists of:

•   Fact table : a very large accumulation of facts such as sales.

w    Often “insert-only.”

•   Dimension tables : smaller, generally static information about the entities involved in the facts.

Terms

•       Fact table

•       Dimension tables

•       Measures

Star

Cube

3-D Cube

ROLAP vs. MOLAP

•       ROLAP:
Relational On-Line Analytical Processing

•       MOLAP:
Multi-Dimensional On-Line Analytical Processing

Aggregates

Aggregates

Another Example

Aggregates

•       Operators: sum, count, max, min,                   median, ave

•       “Having” clause

•       Using dimension hierarchy

–   average by region (within store)

–   maximum by month (within date)

What is Data Mining?

•       Discovery of useful, possibly unexpected, patterns in data

•       Non-trivial extraction of implicit, previously unknown and potentially useful information from data

•       Exploration & analysis, by automatic or
semi-automatic means, of  large quantities of data in order to discover meaningful patterns

Data Mining Tasks

•       Classification [Predictive]

•       Clustering [Descriptive]

•       Association Rule Discovery [Descriptive]

•       Sequential Pattern Discovery [Descriptive]

•       Regression [Predictive]

•       Deviation Detection [Predictive]

•       Collaborative Filter [Predictive]

 

Classification: Definition

•       Given a collection of records (training set )

–   Each record contains a set of attributes, one of the attributes is the class.

•       Find a model  for class attribute as a function of the values of other attributes.

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

–   A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

Decision Trees

Clustering

K-Means Clustering

Association Rule Mining

Association Rule Discovery

•       Marketing and Sales Promotion:

–   Let the rule discovered be

             {Bagels, … } --> {Potato Chips}

–   Potato Chips as consequent => Can be used to determine what should be done to boost its sales.

–   Bagels in the antecedent => can be used to see which products would be affected if the store discontinues selling bagels.

–   Bagels in antecedent and Potato chips in consequent => Can be used to see what products should be sold with Bagels to promote sale of Potato chips!

•       Supermarket shelf management.

•       Inventory Managemnt

Collaborative Filtering

•         Goal: predict what movies/books/… a person may be interested in, on the basis of

–     Past preferences of the person

–     Other people with similar past preferences

–     The preferences of such people for a new movie/book/…

•         One approach based on repeated clustering

–     Cluster people on the basis of preferences for movies

–     Then cluster movies on the basis of being liked by the same clusters of people

–     Again cluster people based on their preferences for (the newly created clusters of) movies

–     Repeat above till equilibrium

•         Above problem is an instance of collaborative filtering, where users collaborate in the task of filtering information to find information of interest

Other Types of Mining

•        Text mining: application of data mining to textual documents

–    cluster Web pages to find related pages

–    cluster pages a user has visited to organize their visit history

–    classify Web pages automatically into a Web directory

•        Graph Mining:

–    Deal with graph data

Data Streams

•         What are Data Streams?

–     Continuous streams

–     Huge, Fast, and Changing

•         Why Data Streams?

–     The arriving speed of streams and the huge amount of data are beyond our capability to store them.

–     “Real-time” processing

•         Window Models

–     Landscape window (Entire Data Stream)

–     Sliding Window

–     Damped Window

•         Mining Data Stream

 

 

 

A Simple Problem

•         Finding frequent items

–    Given a sequence (x₁,…x_N) where x_i ∈[1,m], and a real number θ between zero and one.

–    Looking for x_i whose frequency > θ

–    Naïve Algorithm (m counters)

•         The number of frequent items ≤ 1/θ

•         Problem: N>>m>>1/θ

KRP algorithm
─ Karp, et. al (TODS’ 03)

Streaming Sample Problem

•       Scan the dataset once

•       Sample K records

–   Each one has equally probability to be sampled

–   Total N record: K/N