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Supervisor: Mr. Phan Trường Lâm Supervisor:. Team information.

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Presentation on theme: "Supervisor: Mr. Phan Trường Lâm Supervisor:. Team information."— Presentation transcript:

1 Supervisor: Mr. Phan Trường Lâm Supervisor:

2 Team information

3 Agenda Introduction Project plan System Requirement Specifications System Analysis and Design Testing Deployment and User Guide Summary Demo and Q&A

4 Introduction Initial IdeaLiterature Review of Existing SystemProposal & Product

5 Initial Idea

6 We decide to develop a new system that integrated:  Collect documents  Organize these documents  Extract keyword  Ranking  Searching

7 Literature Review of Existing System  Methods that these websites use to build their systems: Big database Search Ranking and highlight return results Compare documents to detect plagiarism

8 Literature Review  Achievements of the existing systems Attractive Easy to use Speed & Reliability Quality Results Ensuring Security Awareness  Limitations of the existing systems  Costs  Privacy

9 Proposal Collect and manage Capstone projects Support looking up Capstone projects Avoid repeating and copying idea Ranking results Refer to other materials Friendly interface like Google Chipper to build Free to use Public for everyone Inside and outside University

10 Product (in future) Mobile application Web application

11 Project Plan Development environment Process Project organization Project schedule Risk management

12 Development Environment Gb of RAM 100Gb of hard disk Core 2 Duo 2.0 GHz 2 Gb of RAM 100Gb of hard disk Core 2 Duo 2.0 GHz HARD WARE SOFT WARE

13 Process Follow Waterfall model

14 Project organization

15  Controlling and Monitoring Meeting Assign task Tracking task Issue resolve Review task Report Project organization

16  Communication control  Online activity Chat Phone  Offline activity Kick-Off project Team building Project organization

17 Project Schedule Overall plan

18 Risk Management RiskManagement People risk Estimation risk Technology risk Requirement risk Schedule risk

19 System Requirement Specifications User Requirements System Requirements Non-functional requirements

20 User Requirements  Lecturers and Students: Search project documents. Download documents.  Librarians: Edit profile. Search documents. Add/Edit/Delete document. Add/Edit/Delete category.  Administrator Edit profile. Add/Edit/Delete account.

21 User Requirements  Other requirement Searched results will be ranked. Document has following information:  Name  Author  Supervisor  Category  Description

22 User Requirements Input files:  Keyword file  Abstract file  Full document file  Other materials

23 System Requirements  Communicate via the protocol HTTP to complete interactions based on service with client computers and use standard protocols.  Configuration  Server: Windows Server 2008 operating system.NET framework 3.5 SQL server 2008 IIS 7  Client: Web browser

24 Non-functional Requirements Usability Availability Security Reliability Performance Security Maintainability

25 System Analysis and Design Architectural design Detail design Database design Coding convention Extract Keyword algorithm Ranking

26 Architectural design Overall architecture MVC architecture design pattern

27 Detail design CProDMS Component Diagram

28 Database design Entity diagram

29 Coding convention Follow:  Microsoft.NET Library Standards  FxCop rules and Code Analysis for Managed Code Warnings

30 Extract Keyword Algorithm Introduction Study Algorithm Evaluation Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information (YUTAKA MATSUO and MITSURU ISHIZUKA) (Dec. 10, 2003)

31 Algorithm – What is the keyword? Position Meaning Frequency

32 Algorithm – Step by step Preprocessing Processing Discard stop words Stem Extract frequency Calculate X’ 2 value Calculate X’ 2 value Output Expected probability Select frequent term

33 Algorithm – Studying Original Text Information is the most powerful weapon in the modern society. Every day we are overflowed with a huge amount of data in form of electronic newspaper articles, s, web pages and search results. Often, information we receive is incomplete, such that further search activities are required to enable correct interpretation and usage of this information. Example: Information powerful weapon modern society day overflowed huge amount data electronic newspaper articles s web pages search results Often information receive incomplete such further search activities required enable correct interpretation usage information Stemmed Words Information is the most powerful weapon in the modern society. Every day we are overflowed with a huge amount of data in form of electronic newspaper articles, s, web pages and search results. Often, information we receive is incomplete, such that further search activities are required to enable correct interpretation and usage of this information. Discarded Stop Words Step1 Step2 Using Porter Stemming Algorithm Information is the most powerful weapon in the modern society. Every day we are overflowed with a huge amount of data in form of electronic newspaper articles, s, web pages and search results. Often, information we receive is incomplete, such that further search activities are required to enable correct interpretation and usage of this information. Informat power weapon modern societi day overflow huge amoun data electronic newspaper articl web page search result Often informat receive incomplet such further search activ requir enable correct interpret usag informat

34 Algorithm – Studying The top ten frequent terms (denoted as G) and the probability of occurrence, normalized so that the sum is to be 1. Select frequent Term As study, number of keyword is about 10% number of term in document and no more than 30 terms.

35 Algorithm – Studying Two terms in a sentence are considered to co-occur once. Co-occurrence and Importance Example: The imitation game could then be played with the machine in question and the mimicking digital computer and the interrogator would be unable to distinguish them. “imitation” and “digital computer” have one co-occurrence

36 Algorithm – Studying Co-occurrence and Importance

37 Algorithm – Studying The degree of biases of co-occurrence can be used as a indicator of term importance Co-occurrence and Importance

38 Algorithm – Studying The statistical value of χ 2 is defined as p g Unconditional probability of a frequent term g ∈ G (the expected probability) n w The total number of co-occurrence of term w and frequent terms G freq (w, g) Frequency of co-occurrence of term w and term g

39 Algorithm – Studying p g (the sum of the total number of terms in sentences where g appears) divided by (the total number of terms in the document) n w The total number of terms in the sentences where w appears including w We consider the length of each sentence and revise our definitions

40 Algorithm – Studying

41 the following function to measure robustness of bias values Subtracts the maximal term from the X 2 value

42 Algorithm – Studying

43 To improve extracted keyword, we will cluster terms Two major approaches (Hofmann & Puzicha 1998) are:  Similarity-based clustering If terms w1 and w2 have similar distribution of co-occurrence with other terms, w1 and w2 are considered to be the same cluster.  Pairwise clustering If terms w1 and w2 co-occur frequently, w1 and w2 are considered to be the same cluster. Eg: Monday is a day in week. Tuesday is a day in week. Wednesday is a day in week Algorithm – Studying

44 Similarity-based clustering centers upon Red Circles Pairwise clustering focuses on Green Circles Algorithm – Studying

45 Where: Similarity-based clustering Cluster a pair of terms whose Jensen-Shannon divergence is and: Algorithm – Studying

46 Cluster a pair of terms whose mutual information is Pairwise clustering Where: Algorithm – Studying

47 Algorithm – Evaluation Precision: Ratio of right keyword to number of keywordCoverage: Ratio of indispensable keyword in list to all the indispensable terms Frequency index: average frequency of keyword in list

48 Ranking – Why? Ranking Result

49 Ranking

50 Ranking Use rank calculate formula Term in a collection documents: ( Automatic Keyword Extraction for Database Search First examiner : Prof. Dr. techn. Dipl.-Ing. Wolfgang Nejdl Second examiner : Prof. Dr. Heribert Vollmer Supervisor : MSc. Dipl.-Inf. Elena Demidova ) R(t) = Fd(t)*log(1 + N/N(t)) (1) Rank of Term t in all the collection Total number of documents in the collection Frequency of Term t in the given document Total number of documents that contain Term t Ranking formula : Rank = d * Rd(t) / R(t) (2) =>Rank = d * Rd(t) / (Fd(t)*log(1 + N/N(t))) (3) reliability coefficient Rank of Term t in document, which extracted by Extract Service

51 Searching

52 Testing V - model

53 Testing

54 Testing NoTesterModule codePassFailUntestedN/ANumber of test cases 1 AnhNT Master Page AnhNT Home Page AnhNT Search Result AnhNT User Account AnhNT Error Page NamH Category NamH Document NamH Authenticated NamH User Document Detail Sub total Test coverage % Test successful coverage % Test result

55 Deployment  Package Source Code  Client side  Server side

56 User guide

57 Summary  Strong point Enthusiasm Creative Cope with change  Weak point Lack of technical skill Lack of management skills  Lessons learned Improve technical & management skills Release on-time product with the restriction of time and resource Improve communication skills & problem solving

58 Demo & Q&A

59


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