Assignment No 1 v(1.0.0)

8/7/2019 Assignment No 1 v(1.0.0)

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Assignment No 1:

Research Methodology

Submitted to:

Sir Adnan Mujahid

Compiled by:

UmairTahir MS (SE)-2011

Bahria University

8/7/2019 Assignment No 1 v(1.0.0)


Q No 1: Differentiate between IEEE Magazines, IEEE Journals and IEEEConferences on the basis of type of Research published in them.

Focused Conferences with Respect to Journals:y Conferences are more timely. y It can take years for a journal publication to appear (or even for reviews to come back), whereas

the turnaround time for conference reviews is a few months , and the proceedings also appearquickly.

y Conferences have higher standards of novelty y Journals often only require 20-30% of the material to be new, compared to an earlier

conference version. y Conferences have higher quality, acceptance rates to good conferences are often around 10%

(at least in software engineering) whereas even the best journals are less selective. y Conferences provide higher visibility and greater impact. Many people will attend your talk, you

will have the opportunity to answer questions, and people will talk to both you and to oneanother in the hallways.

Focused Journals with Respect to Conference:

y Journals may have longer page limits. If you have too many experimental results to fit in aconference publication, then a journal affords an opportunity to include them. You can alsoinclude proofs that are too long (or boring) for a shorter publication. A journal paper could recapor given an overview of an entire research area.

y Journal reviews tend to be more detailed. A journal reviewer may spend days on a paper,whereas a conference reviewer cannot afford to do so.

y Journals give the opportunity to revise your work and re-submit it for review. Actually,conferences give this too: if a paper is rejected from one conference, then you can revise basedon the reviewers' comments and submit to a different conference or the same one the nextyear.

y Journals have higher acceptance rates, giving the opportunity to get your research published.

IEEE Magazines:

y Magazines are basically launched periodically.y The magazine is published by the IEEE Communications Society and includes special features,

technical articles, book reviews, conferences, short courses, standards, governmentalregulations and legislation, new products, and Society news.

y Magazines contain the mixture of reviews, news, ads, images etc

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8/7/2019 Assignment No 1 v(1.0.0)


12 Journal(springer)

World Wide Web

http://www.springer.com/computer/database+management+%26+information+retrieval/journal/11280

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Q No 3: Google to the official websites of your favorite (highly prestigiousconference) and print out the table of contents. (Submit first 2 pages inassignment only)

Index Type T itle/URL1 Conference 2009 International Conference on Electrical Engineering and

Informatics http://www.ftsm.ukm.my/iceei2009/

Table of Contents:INFORMATION SYSTEM/NETWORKS

y The Impact of CRM on Customer Retentionin Malaysia

y An Integrated E-Marketplace Framework: ACase Study of Wood-based ProductsIndustry in Malaysia

y Information and CommunicationTechnology for Decision-Making in theHigher Education in Yemen: A Review

y A Framework for Knowledge ManagementSystem in Higher Learning Institution: ACase Study of National University of Malaysia

y Digital Libraries Interoperability Issuesy Pattern Extraction for Islamic Concepty Logistic E-Marketplace For Agro-Based

Industries In Malaysiay Knowledge Retention in Knowledge

Management System: Reviewy Application of PIC Microcontroller for

Online Monitoring and Fiber FaultIdentification

y Temperature-Pattern Dependence of InitialCarrier Density of High-Speed DigitallyModulated Uncooled Semiconductor LaserDiodes: Theoretical Analysis

y Lock-on Range of Infrared Heat SeekerMissile

y High Mobility Data Pilot Based ChannelEstimation for Downlink OFDMA SystemBased on IEEE 802.16e Standard

y Generic Data Model Patterns using FullyCommunication Oriented InformationModeling (FCO-IM)

y Automatic Extraction of PerformanceIndicators from Financial Statements

y Novice Difficulties in Selection Structurey Employability and Service Science: Facing

the Challenges via Curriculum Design andRestructuring

y Engaging the SSME Challenge: An Agendafor Research at the Faculty of InformationScience and Technology

y Design of RK-X Meta: A Methodology forData Warehouse Development withMetadata Management Support

y Operability and Reliability Success Factorsof Rural IC T Infrastructure

y Mining Association Rules from Structured

XML Data 365y Introduction to Semantic Search Enginey Inferring Functional Dependencies for

XML Storagey Enterprise Architecture for E-Government

in Indonesiay Assessment for Knowledge Management

Readinessy B2B E-Commerce Readiness Assessment

Based on the Critical Success Factors 394y A Study ofRouting Protocol for Topology

Configuration Management in Mobile AdHoc Network

y Computation of Private Key for LUCCryptosystem

y An Approximation Approach for FreeSpace Wave Propagation

y A Comparative Review of IPv4 and IPv6for Research Test Bed

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y V ery Fast Pipelined RSA Architecture Basedon Montgomery's Algorithm 479

y Implementation of V ideo V oice Over IP inLocal Area Network Campus EnvironmentNA Finite Difference Approach on RBF

Networks for On-line System Identificationwith Lost Packety Recognizing Deviants in Social Networking

Sites: Case Study Fupei.comy Load Balancing in Energy Efficient

Connected Coverage Wireless SensorNetwork

y Application of SAN TAD in NetworkMonitoring and Fault Detection of F TT H-PON

y A Generic Framework for DevelopingMap-Based Mobile Application

y Observation of Medium Scale TravellingIonospheric Disturbance Using GPSSUGAR

y Bit-Error-Rate (BER) for ModulationTechnique using Software Defined Radio

y Restoration Scheme Implementation inAutomatic Protection Unit for F TT HPassive Optical Network

2 Conference 2009 Fourth International Conference on Internet Monitoring andProtection

http://www.ftsm.ukm.my/iceei2009/ Table of Contents:ICIMP 1: TRASI

y Effective Change Detection in LargeRepositories of Unsolicited Traffic

y A Technique for Detecting New Attacks inLow-Interaction Honeypot Traffic

y Framework for Zombie Detection UsingNeural Networks

y Scalable and Density-Aware MeasurementStrategies for Overlay Networks

ICIMP 3: IPERF, RTSEC, EMDRM, and SYVULy An Empirical Evaluation on Semantic

Search Performance of Keyword-Basedand Semantic Search Engines:Google, Yahoo, Msn and Hakia

y Extraction of Parameters from WellManaged Networked System in

AccessControly Domain Based Content Sharing in Digital

Homey Where Only Fools Dare to Tread: An

Empirical Study on the Prevalenceof Zero-Day Malware

ICIMP 5: RISK, SYDIA, and WIPy Security Assurance Metrics and

ICIMP 2: USSAFy Towards Developing Secure V ideo

Surveillance Systems over IPy Development of Social Networks in Email

Communicationy Security in Peer-to-Peer Networks: Empiric

Model of File Diffusion in Bit Torrenty Enhancing Privacy Implementations of

Database EnquiriesICIMP 4: REPORT and BIOTEC

y Behavior-Based Proactive Detection of Unknown Malicious Codes

y IT Security in Banking - Processes, PracticalExperiences and LessonsLearned

y Usability of V isual Evoked Potentials asBehavioral Characteristicsfor Biometric

Authenticationy Cognitive-Based Biometrics System for

Static User AuthenticationICIMP 6: MONIT

y Are Smaller Packets Less Likely to Be Lost?y Assolo, a New Method for Available

Bandwidth Estimation

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Aggregation Techniques for I T Systemsy Fuzzy Heuristic Design for Diagnosis of

Web-Based V ulnerabilitiesy A Probe Framework for Monitoring

Embedded Real- T ime Systemsy

Information Security Management is NotOnly Risk Management

3 Conference/Symposium

First European Semantic Web Symposium, ESWS 2004Symposium/Conference http://www.esws2004.org/

Table of Contents: Ontology Engineering

y Towards On-the-Fl y Ontolog y Construction -Focusing on Ontolog y

y Qualit y Improvement onto Edit EmpoweringSWAP : a Case Study in Supporting

y Distributed, Loosel y-Controlled and evolvingEngineering of

y Ontologies ( DILIGEN T) y Linguistic Anal ysis

Ontology Matching and Mappingy Formal Support for Representing and

Automating Semanticy Interoperabilit y y S-Match: an Algorithm and an Implementation

of Semantic Matchingy Ontolog y Mapping - An Integrated Approach

Ontology-Based Queryingy Application of Ontolog y Techniques to View-Based Semantic Searchand Browsing

y Active Ontologies for Data Source QueriesOntology Merging and Population

y K nowledge Discover y in an AgentsEnvironment

y The HCONE Approach to Ontolog y Mergingy Question Answering Towards Automatic

Augmentations of Ontolog y y Instances

Infrastructurey The SCAM Framework: Helping Semantic

Web Applications to Store and AccessMetadata

y Publish/ Subscribe for R DF-based P2P Networks

y Streaming O WL DLS emantic Web S ervices

y Mathematics on the ( Semantic ) NET y Approaches to Semantic Web Services: an

S ervice Discovery and Compositiony Director y Services for Incremental Service

Integrationy A Framework for Automated Service

Composition in Service-Orientedy Architecturesy Reusing Petri Nets through the Semantic Web

Data for the S emantic Weby Methods for Porting Resources to the Semantic

Weby Learning to Harvest Information for the

Semantic Weby Reverse Engineering of Relational Databases to

OntologiesK nowledge Representation

y No Registration Needed: How to UseDeclarative Policies and

y Negotiation to Access Sensitive Resources onthe Semantic Web

y Semantic Annotation Support in the Absence of Consensus

y Uncertaint y in K nowledge ProvenanceA pplications

y Collaborative Semantic Web Browsing withMagpie

y Toward a Framework for SemanticOrganizational Information Portal

y CS AK TiveSpace: Building a Semantic WebApplication

Content Managementy Cultural Heritage and the Semantic Weby Neptuno: Semantic Web Technologies for a

Digital Newspaper ArchiveInformation Management and Integration

y MI K SI - A Semantic and Service OrientedIntegration Platform

y Semantic Web Technologies for Economic andFinancial Information

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Overview and Comparisonsy OWL-S Semantics of Securit y Web Services: a

Case Study

y Management

4 Conference International Conference On Computer Design And Applications

http://webapps1.ieee.org/conferenceSearch/details.do;jsessionid=2szGLszG0dS1yxmZdX8pb8nxjDKjHKknxrKy0w V zHvPsjzyq81Jc!-429206047?tagNo=16801

Table of Contents:http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5520349

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Q No 4: Read all the titles carefully and understand how cleverly trimmedtitles can attract reader s attention. Shortlist the T able of Contents to 5 titlesthat inspired you the most. Carefully read its abstract and Introduction tograsp a basic understanding about the idea it presents. For each title, submitthe paperform.pdf (copy attached with this email), answering each questionasked about the paper. Choose your papers carefully as these papers will goa long way.

Index T itles Conference1 Research on Semantic Web Mining 201O International Conference On

Computer Design And Applications(ICCDA 2010)

2 An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines: Google,Yahoo, Msn and Hakia

2009 Fourth International Conferenceon Internet Monitoring and Protection

3 An Ontological-Semantic Frameworkfor Text Analysis

????

4 S-MATCH: AN ALGORITHM AND AN IMPLEMENTATIONOF SEMANTIC MATCHING

First European Semantic WebSymposium, ESWS 2004Symposium/Conference

5 Introduction to Semantic Search Engine 2009 International Conference onElectrical Engineering and Informatics

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T itle: Introduction to Semantic Search Engine Authors: Junaidh Mohamed Kassim, Mahathir Rahmany Published In: 5 August 2009

Name:Engineering Research PaperQuestion-Answer Form

What is your take-away message from this paper?

Every individual s Search must be fulfilled by the means of personalization concept. Like If two personsare searching for a Mercedes Car. Then their context must be known to the search engine one is lookingto purchase it and other one is for example looking for getting information about their models.

What is the motivation for this work (both people problem and technical problem),and its distillation into a research question? W hy doesn t the people problem have atrivial solution? W hat are the previous solutions and why are they inadequate?

Peoples Problem:

Search engine has become a primary need to explore the internet. Without Search Engine, there are nouses of information in website, blog, etc.; because without search engine, it is almost impossible to lookfor one by one website just for search information in internet. So brining relevant information whatpeople are looking for is the major issue of today s ( Traditional search engines).

Technical Problem:

Search engine is the most important tool todiscover any information in World Wide Web.Searched by keyword and do not understandpolysemy and synonymy are some reasons thetraditional search engine is not suitableanymore. This problem will be solved by theSemantic Search Engine.

Web 3.0 shows more intelligence: the "webmachine" learns, suggests and anticipates whatpeople like and would like to get!

The terrific increment of the web has made theEvolution of the web itself. From web 1.0 (firstgenerationof internet 1990 2000), web 2.0and now has become toweb 3.0.

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What is the proposed solution (hypothesis, idea, design)? W hy is it believed it will work? How does it represent an Improvement? How is the solution achieved?

Proposed solution is based on the concept of ontology which is the explicit specification of conceptualization. Ontology describes knowledge about the domain in terms of concept or vocabularieswithin the domain and relationships between them.

Hypothesis: By using semantically richer ontologies the following benefits can be obtained. Firstly,ontologies can be used to describe the domain knowledge and the terminology of the application inmore detail. For example, relations between categories in different views can be defined. Secondly,ontologies can be used for creating semantically more accurate annotations in terms of the domainknowledge. Thirdly, with the help of ontologies, the user can express the queries more precisely andunambiguously, which leads to better precision and recall rates. Fourthly, through ontological classdefinitions and inference mechanisms, such as property inheritance, instance-level metadata can be

enriched semantically

It is believed that it works because of the reasons that traditional search engines do not understand thepolysemy and synonymy and do not take into account stop words such as a, and is on with by after the.Whereas the Semantic Search makes it easier to locate relevant information to the user s subject of interest, saving the user a lot of time reading unrelated Web pages.

How does it represent an Improvement?

Semantic Search Engine can give you the following things:

1. In engine prompt, you are entering question2. Understand polysemy and synonymy3. Knowing meaning of the terms4. Take into account stop words such as a, and is, on of or the was with by after the.5. Is designed to try and understand the context that the words are used within the web page to

try and match it more accurately to the user s search query.6. Able to handle long-tail queries.

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What is the author's evaluation of the solution? W hat logic, argument, evidence,artifacts (e.g., a proof-of-concept system), or experiments are presented in support of the idea?

The author provides the following evidence/artifacts/experiments in support of the idea:

For the comparison between Traditional Search Engines vs. Semantic Search Engine,

Please see these two search engines.

1. Hakia ( www.hakia.com ) as a semantic search engine2. Dogpile ( www.dogpile.com ) as a traditional

The keyword phrase that is used for this comparison is what is the weather in Kuala Lumpur . See theresult below.

From the figures below is showed that Hakia seem knows what their user want. It is clear from the resultthat showed by Hakia. They show the information of Kuala Lumpur weather not the website thatcontains keyword of Kuala Lumpur weather like Dogpile.

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What is your analysis of the identified problem, idea and evaluation? Is this a good idea? W hat flaws do you perceive in the work? W hat are the most interesting or controversial ideas? For work that has practical implications, ask whether this will work, who would want it, what it will take to give it to them, and when might it become a reality?

Analysis of the identified Problem:According to my view point, traditional search engines definitely required an evolutionary change in thesearching. The user needs to invest his time after querying to the Search Engine in finding out hisrequired solution/answer. After the Implementation of ontologies to the text (explicit specification of conceptualization) the relevancy can be achieved at this optimum level.Definitely it s a great idea to achieve the relevancy in search results.

The most interesting thing is the literature review. He has completely covered up all the required areasabout search engines working to semantic search engines proposed solution. Yap this will definitely willcome into the reality.

What are the paper's contributions (author's and your opinion)? Ideas, methods,software, experimental results, experimental techniques...?

The author included the search results via following search engines ( Traditional Search Engine V sSemantic Search Engine):

1. Hakia ( www.hakia.com ) as a semantic search engine2. Dogpile ( www.dogpile.com ) as a traditional

What are future directions for this research (author's and yours, perhaps driven byshortcomings or other critiques)?

N/A

What questions are you left with? W hat questions would you like to raise in an opendiscussion of the work (review interesting and controversial points, above)? W hat do

you find difficult to understand? List as many as you can.Difficult to review the literature

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T itle: S-MAT CH: AN ALGORITHM AND ANIMPLEMENTATION OF SEMANTIC MAT CHING Authors: FaustoGiunchiglia, Pavel Shvaiko andMikalaiYatskevich Published In: Februar y 2004

Name: Umair TahirEngineering Research PaperQuestion-Answer Form

What is your take-away message from this paper?

Semantic Based Search Engine gives the most relevant results to the users. Like If two persons aresearching for a Mercedes Car. Then their context must be known to the search engine one is looking topurchase it and other one is for example looking for getting information about their models.

What is the motivation for this work (both people problem and technical problem),and its distillation into a research question? W hy doesn t the people problem have atrivial solution? W hat are the previous solutions and why are they inadequate?

Peoples Problem:

Search engine has become a primary need to explore the internet. Without Search Engine, there are nouses of information in website, blog, etc.; because without search engine, it is almost impossible to lookfor one by one website just for search information in internet. So brining relevant information whatpeople are looking for is the major issue of today s ( Traditional search engines).

Technical Problem:

Search engine is the most important tool to discover any information in WorldWide Web. Searched by keyword and do not understand polysemy andsynonymy are some reasons the traditional search engine is not suitableanymore. This problem will be solved by the Semantic Search Engine (which isimplementation of semantic matching Algorithm)

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What is the proposed solution (hypothesis, idea, design)? W hy is it believed it will work? How does it represent an Improvement? How is the solution achieved?

Proposed solution is based on the algorithm implementing semantic matching, and they have discussedits implementationwithin the S-Match system . They also performed test S-M atch against threestate of

the art matching systems. The results, though preliminary, look promising,in particular for whatconcerns precision and recall.

Hypothesis: By using semantically richer ontologies, the relevance in results can be achieved. For thesemantic algorithm implementation they think of M atch as an operator which takes two graph-likestructures(e.g., conceptual hierarchies or ontologies) and produces a mapping betweenthose nodes of the two graphs that correspond semantically to each other.

M atch is a critical operator in many well-known application domains, such as schema/ontologyintegration, data warehouses, and XML message mapping.

It is believed that it works because of the reasons that Its major advantage is that the algorithm wehave proposed is correct and complete in the sense that it always computes the strongest semanticrelation between concepts of nodes (contrarily to what happens with syntactic matchers, see also thediscussion of the testing results in the next section). This is, in fact, an obvious consequence of the factthat we have translated the problem of computing concepts of nodes

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How does it represent an Improvement?

Semantic Search Engine can give you the following things:

7. In engine prompt, you are entering question8. Understand polysemy and synonymy9. Knowing meaning of the terms10. Take into account stop words such as a, and is, on of or the was with by after the.11. Is designed to try and understand the context that the words are used within the web page to

try and match it more accurately to the user s search query.12. Able to handle long-tail queries.

What is the author's evaluation of the solution? W hat logic, argument, evidence,artifacts (e.g., a proof-of-concept system), or experiments are presented in support of the idea?

The author provides the following evidence/artifacts/experiments in support of the idea:

The Author has presented an algorithm implementing semantic matching and itspreliminaryimplementation within a semantic matching platform, S-M atch . He has tested the currentimplementation against three state of the art systems and the results look verypromising, with a pointof attention on computation times.

For the comparison we have used the three examples:

The simple catalog matching problem,presented in the paper and two small examples from the academy

and businessdomains.

y The business example describes two company profiles: a standard one (mini) and Yahoo Finance(mini).

y The academy example describes courses taught at Cornell University (mini) and at the Universityof Washington (mini).1

y Table 3 provides some indicators of the complexity of the test schemas.

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What is your analysis of the identified problem, idea and evaluation? Is this a good idea? W hat flaws do you perceive in the work? W hat are the most interesting or controversial ideas? For work that has practical implications, ask whether this will work, who would want it, what it will take to give it to them, and when might it become a reality?

Analysis of the identified Problem:The Semantic Search Engine can be implemented with the algorithm of S Match. After theImplementation of ontologies to the text (explicit specification of conceptualization) the relevancy canbe achieved at this optimum level.

Definitely it s a great idea to achieve the relevancy in search results: by S Match Algorithm

The most interesting thing is the literature review. He has completely covered up all the required areasabout search engines working to semantic search engines proposed solution. Yap this will definitely willcome into the reality.

What are the paper's contributions (author's and your opinion)? Ideas, methods,software, experimental results, experimental techniques...?

The author included the search results via following search engines ( Traditional Search Engine V s.Semantic Search Engine):

The Author have tested the current implementation against three state of the art systems and theresults look verypromising, with a point of attention on computation times.

What are future directions for this research (author's and yours, perhaps driven byshortcomings or other critiques)?

N/A

What questions are you left with? W hat questions would you like to raise in an opendiscussion of the work (review interesting and controversial points, above)? W hat doyou find difficult to understand? List as many as you can.

Difficult to review the Algorithm of S-MA TCH: AN ALGORITHM AND AN MPLEMENTATIONOF SEMANTIC MATCHING

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