透過您的圖書館登入
IP:3.133.156.156
  • 學位論文

物件關聯式資料倉儲之實體化視域選擇

Materialized View Selection In Object-Relational Data Warehouse

指導教授 : 劉俞志
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


實體化視域為一有效提高資料倉儲查詢效能的技術,傳統關聯式資料倉儲的實體化視域選擇與維護已被深入研究,但對於導入物件觀念、具有多重複雜資料型態的物件關聯式資料倉儲(Object-Relational Data Warehouse,簡稱ORDW)而言,迄今尚未有相關之研究文獻發表。為能有效提高ORDW查詢上的效能,ORDW之實體化視域選擇與維護有其發展之必要性,因此,本文針對ORDW具有之特性,提出適用於物件關聯資料模式的實體化視域選擇演算法,以此來規劃出整體之視域處理與維護路徑,再以總體查詢處理及維護成本為考量,決定最終實體化視域選擇結果。此外,本研究除考量系統儲存空間的限制外,亦可選擇性的限制個別視域處理的反應時間,以達到個別視域不同之查詢需求。

並列摘要


Materialized view is a technique, which can improve the query performance in a data warehouse environment. Many researches are related to the materialized view selection applied in the traditional Relational Data Warehouse. However, none of research has been done about this technique used in Object-Relational Data Warehouse (ORDW), which has multi-complex data type and involves the idea of object-oriented. In order to enhance the query performance of ORDW, it is necessary to develop materialized view selection for ORDW. Therefore, the objective of this study is to develop an algorithm to apply materialized view selection for ORDW. Base on this algorithm, an overview of processing plan is portrayed. In this processing plan, we take into account the total query processing cost and maintenance cost to select the applicable materialized views. Beside, for the different needs, we consider not only the constraint of system space, but also the option of restricting response time for different view selection in our research.

參考文獻


22. 林聖斌、劉俞志,「物件關聯式資料倉儲之實體化視域選擇」, 2002資訊管理國際研討會,第73-80頁,2002年。
2. A Gupta, I.S. Mumick, and V.S. Subrahmanian, ”Maintaining Views Incrementally” , Proc. Of SIGOD conf., pp.157-165, 1993.
5. J. Liu, M. Vincent, and M. Mohania, “Incremental Maintenance of Nested Relational Views” , Prof. of IDEAS’99, pp.197-205,1999.
6. J. Liu, M. Vincent, and M. Mohania, ”Maintaining Views in Object-Relational Database” , CIKM 2000, 2000.
7. P. Roy, S. Seshadri, S. Sudarshan, and S. Bhobe, “Efficient and Extensible Algorithms for Multi Query Optimization” , ACM SIGMOD Intl. Conf. on Management of Data, pp.249-260,2000.

延伸閱讀