1 Random Thought on Research Methods in CS/CIS CSCI 6530 July 1, 2010 Kwok-Bun Yue University of Houston-Clear Lake.

1

Random Thought on Research Methods

in CS/CIS

CSCI 6530July 1, 2010

Kwok-Bun YueUniversity of Houston-Clear Lake

Random

• Random: not organized.

7/1/2010 Bun Yue: [email protected], http://dcm.uhcl.edu/yue slide 2

Merriam-Webster

• Research– 1 : careful or diligent search– 2 : studious inquiry or examination; especially :

investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws

– 3 : the collecting of information about a particular subject


For what?

• Finding new things: facts, theories, processes, tools, relationships, techniques.

• Solving problems


Why Research?

• Solving problems.• Enhancing understanding.• Career enhancement.• Curiosity and fun.• …


Research Methods

• Discipline dependent.– E.g. medical research: double blind test

with control.• Scientific methods.• Empirical methods.


Starting Research

• What do you need to start your research?– Talk! Talk! Talk!– Think! Think! Think!– Read! Read! Read!


Asking Questions

• ASK! ASK! ASK!


Not Asking Questions

• Easy• Comfortable• Familiar• …


Asking is crucial

• Get a context of the problem from many angles.

• Organize your thought.• Model and refine your understanding.• Discover new information and insight.


Intellectual Curiosity

• A key for deep understanding, important discovery and … fun.

• Sometimes not too output driven: need of ‘down’ time.


• Recommended reading: Surely You're Joking, Mr. Feynman! (Adventures of a Curious Character) by Richard Feynman.

Keeping an open mind

• Keep an open mind as long as possible.– Do not jump to the first solution that you

have come up with.


Research in Physics

• Scientific Methods:1. Observe, ask questions and understand2. Make hypothesis and model3. Make (precise) predictions using the

hypothesis.4. Test the predictions.


Questions in Physics

• Fundamental questions: e.g.– Can the four fundamental forces be unified:

theory of everything?– Where do our universe come back?– What are elementary particles make of?


Results in Physics

• Theories: e.g.– Superstring theory.– Big bang theory– Quarks

• New facts.


Validations in Physics

• Experiment with predictions by theories.• E.g.: Big bang theory predicts

abundance of light elements.– Positive results: add confidence.– Negative results: reject theory.


Questions in Computing

• Much more diverse. Have aspects from most other areas: engineering, science, humanities, …

• Can create your own ‘universe’. (vs economic, for example)


Result in CS

• New theories, algorithms, processes, methods, facts, etc.

• New models, problems and application areas.


Validations

• Direct validation• Theoretical analysis• Simulation• Benchmarking• Statistical methods• …


Planning: Goals

• Output oriented incentives can be too ‘far away’.

• Setting plans and goals.– Create a detail plan of steps and

benchmarks.– Small goals every step.– Consider input-oriented goals.


Early Web Business Model


BuildWebsites

AttractHuge Traffic

Somethinghappens

Rich!

Thesis


UnderstandProblem

Design and ImplementSolution

Good thinghappens

Done!

Detailed Plan

• Create a road map with enough details to the final goals.– Preparation.– Planning– Risk Management

• Recommended reading: Ed Viesturs, “No Shortcuts to the Top: Climbing the World's 14 Highest Peaks”


Areas of My Research Interest

• Internet Computing• XML and semi-structured data • CS and IS education• Concurrent Programming


(Older) XML Projects

• Storage of XML in relational database (Used as an example)

• XML Metrics



Storing XML in RDB

• Advantages:– Mature database technologies.– May be queried by

• XML technology: e.g. XPath, XQuery.• RDB technology: e.g. SQL.

• Disadvantages: – impedance mismatch: XML and relations

are different data models.


Related Issues

• Effective mapping XML DTDs (~ ordered tree model) to relational schemas.

• Mapping of XML queries (e.g. XQuery) to RDB queries (e.g. SQL).

• Mapping of RDB query results back to XML format.


Related Work and Context

• Mapping – With or without schemas for XML.– With or without user input.

• Schemas for XML:– Document Type Definition (DTD)– XML Schema

• We consider mapping with DTD and without user input.


Naïve Mapping

• An XML element is mapped to a relation.

Example 1a:XML:

<a><b><c><d>hello</d></c></b></a>-> Relations: a, b, c and d.


Problems of Naïve Mapping

• Many relations.• Ineffective queries: multiple query joins.Example 1b:XPath Query: //aSQL Query: need to join the relations a, b,

c and d.


Inlining Algorithms

• First proposed by Shanmugasundaram, et. al.

• Expanded by Lu, Lee, Chu and others.• Extended in various directions by various

researchers, e.g.,– Preserving XML element orders.– Preserving XML constraints.

• Do not consider extensions here.


Basic Idea of Inlining Algorithms

• Inline child element into the relation for the parent element when appropriate.

• Different inlining algorithms differ in inlining criteria.

Example 1c: XML: <a><b><c><d>hello</d></c></b></a>

Inlined Relation: a.


Inlining Algorithms

• Child elements & attributes may be inlined.

• Child elements may not have their own relations.

• Results in less number of relations.• In general, more inlining -> less joins.


Inlining Algorithm Structure

1. Simplification of DTD.2. Generation of DTD graphs3. Generation of Relational Schemas

Our work

• Improved on simplification of DTD and generation of DTD graphs.

• Constructed a new aggressive inlining algorithm.

• Student: Alakappan.


Internet Computing

• Web bias (older project)• Web 2.0 framework (IS project)• Content Management Software (CMS):

Joomla (CS/IS Education)• Mashup: Yahoo Pipe (CS/IS Education)


Measuring Web Bias

• Search engines dominate how information are accessed.

• Search results have major social, political and commercial consequences.

• Are search engines biased?• How bias are them?



Previous Works

• To measure bias, results should be compared to a norm.

• The norm may be from human experts.• Mowshowitz and Kawaguchi: the

average search result of a collection of popular search engines as the norm.


Mowshowitz and Kawaguchi

SE1

SEn

URLS1

URLSn

NORMURLS

URLVector1

URLVectorn

union NORMURL

Vector

Bias1

Biasn


Limitations

• Based on URL Vector -> cannot measure bias quality.


Our Approach

• Use Kleinberg’s HITS algorithm to create clusters, authorities and hubs of the result norm URLs.

• Use them as norm clusters, authorities and hubs.

• Measure distances between norms and individual results as bias.


Our Approach

SE1

SEn

URLS1

URLSn

NORMURLS

URLVector1

URLVectorn

union NORMClusterVector

Bias1

Biasn

NORMCluster

ClusterVector1

ClusterVectorn

Recent Projects

• Web 2.0 framework:– A model and framework to study Web 2.0

technologies, implications and trends.– Collaborator: Mr. Tracy Gate.– Publications: Pre-ICIS Workshop and

Communications of AIS.


CMS: Joomla

• Question: Using CMS/Joomla for capstone project.

• Methodology: projects and surveys.• Collaborator:

– Capstone project teams.– Industrial mentor: Dilhar DeSilva

• Publication: Journal of Information Systems Education.


End User Programming

• Use of Yahoo/Pipeline in constructing Web Mashup.

• Methodology: projects and surveys.• Collaborators: students in the XML

class in Summer 2009.• Publication: Journal of Information

Systems Education.


Ongoing projects

• Googlewave as communications/collaboration tools in capstone projects and software project management.

• Collaborators: capstone project students.

• Publications: under preparation.


Open Source Software

• Use of OSS in educational institutes.• Methodology: meta-analysis.• Collaborators: two master students.


Other recent projects

• Assessment• Scholarship• Student Response Systems


Interested?

• Come and talk with me.



Conclusions

• Good time to do applied computing research in the Web, XML and other areas.

• Style: hands-on supervision + publications.

• Don't forget to donate a scholarship to the School if your future research leads to a windfall.


Questions?

• Any Questions?• Thanks!

1 Random Thought on Research Methods in CS/CIS CSCI 6530 July 1, 2010 Kwok-Bun Yue University of Houston-Clear Lake.

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