Data Mining and Data Warehousing of Many-to-Many Relationships and

some Applications

William PerrizoDept of Computer Science North Dakota State Univ.

Why Mining Data?

Parkinson’s Law of Data

Data expands to fill available storage (and then some)

Disk-storage version of Moore’s law

Capacity 2 t / 18 months

Available storage doubles every 18 months!

More’s Law = More’s Less

The more volume one has, the less information one has. (AKA: Shannon’s Canon)

A simple illustration: Which phone book is more helpful?

BOOK-1 BOOK-2

Name Number Name Number

Smith 234-9816 Smith 234-9816

Jones 231-7237 Smith 231-7237

Jones234-9816

Jones231-7237

We all have volumes of data!

Soon we will volumes more! EROS Data Center (EDC) in the USA archives Earth Observing System

satellite data for the US Government. They expect 10 petabytes by 2005. Sloan Digital Sky Survey (aggregated astronomical data) will exceed that

by many orders of magnitude. Sensor networks will collect unheard-of data volumes. The WWW!! Micro-arrays, gene-chips and genome sequencing successes are creating

potentially life-saving data at a torrid pace. Homeland security data is voluminous and vital? (no matter where

your homeland happens to be) but the information MUST be teased out of it).

That’s where data mining comes in!

Data Mining and Querying

Most people have Data,but want information. Sometimes they get that information using simple query engines, provided they know exactly what they want and how to ask for it, otherwise, data mining is required. But, in fact, data mining is useful anyway, because there’s almost always a wealth of useful information in your data that you cannot query because you don’t know it’s there. There is a whole spectrum of techniques to get information from data:

Even on the query end, much work is yet to be done to solve the problem of delivering standard workload answers quickly (D. DeWitt, ACM SIGMOD’02).

On the Data Mining end, we have barely scratched the surface.

But those scratches, in some cases, have already made a HUGE difference - between becoming the biggest corporation in the world and filing for bankruptcy Walmart vs. KMart

SQLSELECTFROMWHERE

Complex queries(nested, EXISTS..)

FUZZY query,Search engines,BLAST searches

OLAP (rollup, drilldown, slice/dice..

Machine Learning Data Mining Standard querying Simple Searching and Aggregating

Supervised Learning – Classificatior Regression

Unsupervised Learning - Clustering

Association Rule Mining

Data Prospecting?

What is Data Mining?

Querying: asks specific questions - expect specific answers.

Data Mining: “Goes into the MOUNTAIN of DATA,

returns information gems”

(But also, likely, much fool’s gold. Relevance and interestingness

analysis assays help pick out the valuable information gems).

Outline Motivation of 3 challenges

More records (rows) More attributes (columns) New subject domains

Some answers to the challenges Thesis work

Generalized P-Tree structure Kernel-based semi-naïve Bayes classification

KDD-cup 02/03 and with Csci 366 students Data with graph relationship Outlook: Data with time dependence

Examples More records

Many stores save each transaction Data warehouses keep historic data Monitoring network traffic Micro sensors / sensor networks

More attributes Items in a shopping cart Keywords in text Properties of a protein (multi-valued

categorical) New subject domains

Data mining hype increases audience

Algorithmic Perspective More records

Standard scaling problem More attributes

Different algorithms needed for 1000 vs. 10 attributes New subject domains

New techniques needed Joining of separate fields

Algorithms should be domain-independent Need for experts does not scale well

Twice as many data sets Twice as many domain experts??

Ignore domain knowledge? No! Formulate it systematically

Some Answers to Challenges Large data quantity (Thesis)

Many records P-Tree concept and its generalization to

non-spatial data Many attributes

Algorithm that defies curse of dimensionality New techniques / Joining separate fields

Mining data on a graph Outlook: Mining data with time dependence

Challenge 1: Many Records Typical question

How many records satisfy given conditions on attributes?

Typical answer In record-oriented database systems

Database scan: O(N) Sorting / indexes?

Unsuitable for most problems P-Trees

Compressed bit-column-wise storage Bit-wise AND replaces database scan

P-Trees: Compression Aspect

P-Trees: Ordering Aspect Compression relies on long

sequences of 0 or 1 Images

Neighboring pixels are probably similar Peano-ordering

Other data? Peano-ordering can be generalized Peano-order sorting

Peano-Order Sorting

Impact of Peano-Order SortingImpact of Sorting on Execution Speed

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Speed improvement especially for large data sets

Less than O(N) scaling for all algorithms

So Far Answer to challenge 1: Many records

P-Tree concept allows scaling better than O(N) for AND (equivalent to database scan)

Introduced effective generalization to non-spatial data (thesis)

Challenge 2: Many attributes Focus: Classification Curse of dimensionality Some algorithms suffer more than others

Curse of Dimensionality Many standard classification algorithms

E.g., decision trees, rule-based classification For each attribute 2 halves: relevant irrelevant How often can we divide by 2 before small size of

“relevant” part makes results insignificant? Inverse of

Double number of rice grains for each square of the chess board

Many domains have hundreds of attributes Occurrence of terms in text mining Properties of genes

Possible Solution Additive models

Each attribute contributes to a sum Techniques exist (statistics)

Computationally intensive Simplest: Naïve Bayes

x(k) is value of kth attribute

Considered additive model Logarithm of probability additive

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Semi-Naïve Bayes Classifier Correlated attributes are joined

Has been done for categorical data Kononenko ’91, Pazzani ’96 Previously: Continuous data discretized

New (thesis) Kernel-based evaluation of correlation

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Results Error decrease in units of standard deviation for

different parameter sets Improvement for wide range of correlation thresholds:

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Semi-Naive Classifier Compard with P-Tree Naive Bayes

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So Far Answer to challenge 1: More records

Generalized P-tree structure Answer to challenge 2: More attributes

Additive algorithms Example: Kernel-based semi-naïve Bayes

Challenge 3: New subject domains Data on a graph Outlook: Data with time dependence

Standard Approach to Data Mining

Conversion to a relation (table) Domain knowledge goes into table

creation Standard table can be mined with

standard tools Does that solve the problem?

To some degree, yes But we can do better

“Everything should be made as simple as

possible, but not simpler”

Albert Einstein

Claim: Representation as single relation is not rich enough Example:

Contribution of a graph structure to standard mining problems Genomics

Protein-protein interactions

WWW Link structure

Scientific publications Citations

Scientific American 05/03

Data on a Graph: Old Hat? Common Topics

Analyze edge structure Google Biological Networks

Sub-graph matching Chemistry

Visualization Focus on graph structure

Our work Focus on mining node data Graph structure provides connectivity

Protein-Protein Interactions Protein data

From Munich Information Center for Protein Sequences (also KDD-cup 02)

Hierarchical attributes Function Localization Pathways

Gene-related properties

Interactions From experiments Undirected graph

Questions Prediction of a property

(KDD-cup 02: AHR*) Which properties in

neighbors are relevant? How should we integrate

neighbor knowledge? What are interesting

patterns? Which properties say

more about neighboring nodes than about the node itself?

But not:

*AHR: Aryl Hydrocarbon Receptor Signaling Pathway

AHR

Possible Representations OR-based

At least one neighbor has property Example: Neighbor essential true

AND-based All neighbors have property Example: Neighbor essential false

Path-based (depends on maximum hops) One record for each path Classification: weighting? Association Rule Mining:

Record base changes

essential

AHR essential

AHR not essential

Association Rule Mining OR-based representation Conditions

Association rule involves AHR Support across a link greater than within a

node Conditions on minimum confidence and support Top 3 with respect to support:

(Results by Christopher Besemann, project CSci 366)

AHR essential

AHR nucleus (localization)

AHR transcription (function)

Classification Results Problem

(especially path-based representation) Varying amount of information per record Many algorithms unsuitable in principle

E.g., algorithms that divide domain space KDD-cup 02

Very simple additive model Based on visually identifying relationship Number of interacting essential genes adds to

probability of predicting protein as AHR

KDD-Cup 02: Honorable Mention

NDSU Team

Outlook: Time-Dependent Data KDD-cup 03

Prediction of citations of scientific papers Old: Time-series prediction New: Combination with similarity-based

prediction

Conclusions and Outlook Many exciting problems in data mining Various challenges

Scaling of existing algorithms (more records) Different types of algorithms gain importance

(more attributes) Identifying and solving new challenges in a

domain-independent way (new subject areas) Examples of general structural components

that apply to many domains Graph-structure Time-dependence Relationships between attributes

Software engineering aspects Software design of scientific applications Rows vs. columns