| 摘要(英) |
Since the end of 20th century, the rapid developments of Internet and information
technology have changed everyone’s daily life and the way we manage business. With
knowledge booming and economy evolving, the information and data circulating within a
business entity can build up substantially and rapidly. The data accumulated through time
can be structural, non-structural, homogeneous or heterogeneous. It is essential for a
business to thrive in a global competition that internal information can be retrieved
effectively. This means that the internal search engine can locate and provide accurate
information to fulfill business requirements in a timely manner.
This case study looks into a world leading personal computer company. Currently, this
company focuses on ICT products in the global market. As the company expands,
information gathered from various functions in branch companies all over the world surged.
Now the company is faced with the challenge to consolidate and utilize the information
collected from various sources. Currently, the search engine in the company does not
always find the information that users needed. Often times, the system would provide
results that are not relevant at all. The search engine can barely identify the relevancy of the
subjects searched.
Therefore, this study proposes a more effective way for data searching and display. The
proposed method is more intuitive, and can prioritize the search results according to
relevancy, and refine the query results such that relevant results can be more easily
identified, and the frequency of downloading physical files can be drastically reduced.
Thus, this study enhances the competitive advantage by establishing a platform where
information can be effectively communicated and shared for operational purposes.
This study identifies several major problems such as the lack of relevancy, insufficient
summary data, and improperly prioritized results. To model a solution, literature on Internet
search and enterprise search were reviewed. We have also factored users’ behaviors in to
our proposal. The solutions include:
1. Improve the query terminologies by leveraging domain expert knowledge through
the Navigators system.
2. A fast preview function which can generate meaningful summaries.
3. Gather feedbacks from users to improve how search engine prioritizes results.
iii
In this study, we built a prototype to prove the concept, and hope to extend it with
more functional features. We analyzed the feasibility of the proposal from four aspects:
marketing, technical, legal and economic. Analysis shows the proposed scheme to be
feasible from these aspects.
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| 參考文獻 |
參考文獻
期刊及書籍：
Abrol, M., Latarche, N., Mahadevan, U., Mao, J., Mukherjee, R., Raghavan, P., et al. (2001).
Navigating large-scale semi-structured data in business portals. Paper presented at
the Proceedings of the 27th VLDB Conference, Roma, Italy.
Babu, G. (2002). Fundamentals of Modern Statistical Methods. Technometrics, 44(1),
83-83.
Dmitriev, P., Eiron, N., Fontoura, M., & Shekita, E. (2006). Using annotations in enterprise
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Gantz, J., Chute, C., Manfrediz, A., Minton, S., Reinsel, D., Schlichting, W., et al. (2008). The
diverse and exploding digital universe. IDC White Paper, 2.
Hawking, D. (2004). Challenges in enterprise search. Paper presented at the Fifteenth
Australian Database Conference.
Lucas, W., & Topi, H. (2002). Form and function: the impact of query term and operator
usage on web search results. Journal of the American Society for Information Science
and Technology, 53(2), 95-108.
Mukherjee, R., & Mao, J. (2004). Enterprise search: Tough stuff. Queue, 2(2), 36-46.
Spink, A. (1995). Term relevance feedback and mediated database searching:Implications
for information retrieval practice and design. Inf. Proc. Manage(31, Jun), 161-171.
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45
Tiwana, A. (2000). The knowledge management toolkit: practical techniques for building a
knowledge management system: Prentice Hall PTR Upper Saddle River, NJ, USA.
網路資料：
Andrews, W. (2009, 2 Sep). Magic Quadrant for Information Access Technology.
Retrieved 21 May 2010, from
http://www.gartner.com/technology/media-products/reprints/microsoft/vol7/article2
/article2.html
Google. (2010a). Google Fun Facts. Retrieved 11 Jun, 2010, from
http://www.google.com/press/funfacts.html
Google. (2010b). More search help : Google search basics - Web Search Help. Retrieved
10 May 2010, from
http://www.google.com/support/websearch/bin/answer.py?hl=en&answer=136861
Kearn, M. (2006, 6 Jun). MOSS Architecture and Shared Services. Retrieved 17 May
2010, from
http://blogs.msdn.com/martinkearn/archive/2006/06/06/MOSS-Architecture-_2600_
-Shared-Services.aspx
Kulshreshtha, V. (2009, 6 Mar). FAST ESP: Architecture. Retrieved 10 Jun 2010, from
http://blogs.technet.com/b/vedant/archive/2009/03/06/fast-esp-architecture.aspx
Lormiston. (2008, Jun 20). FAST ESP Web Parts for SharePoint Server 2007. Retrieved 8
Jun 2010, from http://espwebparts.codeplex.com/
Microsoft. (2008, 9 Jun). Fast ESP data Sheet. Retrieved 9 Jun 2010, from
http://download.microsoft.com/download/7/3/A/73AB39D7-52FE-4EB0-8941-AFF78
BDD35F0/FAST_ESP_Data_Sheet.pdf
Microsoft. (2010a). Retrieved 11 Jun 2010, from
http://www.microsoft.com/global/enterprisesearch/en/us/PublishingImages/scrn-con
nect-tutorial-lg.jpg
46
Microsoft. (2010b, 19 Apr). Microsoft FAST Search Server 2010 for SharePoint Evaluation
Guide. Retrieved 18 May 2010, from
http://www.microsoft.com/downloads/details.aspx?FamilyID=f1e3fb39-6959-4185-8b
28-5315300b6e6b&displaylang=en
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