臺灣博碩士論文加值系統

晚近由於資料庫與網路技術的快速發展，讓任何人都可以利用網路來尋找所需要的資料，是以在網路環境下的資料存取需求變得日益殷切。但是，分散在各處的資料包括了傳統的文數字資料、圖像資料、或是多媒體檔案，各自有其不同的儲存格式。這些資料格式可能隨時間而有所變化，彼此間互不相容，甚或衝突，於是在網路上構成一種異質性資訊源環境。在這種複雜且多變的環境下，如何有效且快速地對使用者提供整合性的服務，並且確保整合作業時的資料品質，已成為當下極具學理與實用價值的研究課題。
本文倡議一種包含前置整合階段、查詢階段及資料品質保證階段的三階段式運作機制來整合異質性資訊源。在前置整合階段，我們提出運用本體論來驅動資訊整合的作業模式，由於本體論是以概念的表徵為核心，所以在這個層次上的整合作用可以具有一定程度的語意互通效果。我們發展一套系統化的作業流程及相關的演算法；首先我們從現存的知識環境及已知的部分資訊源中構築個別性的本體論原型，而後將各個原型本體論作校正及合併等調整，以得到在特定應用領域中具有整合效用的本體論。
根據整合性的本體論，在查詢階段我們結合資料倉儲及調節器二項主要的整合方法。針對資訊源中週期性及靜態性的資料，我們運用調節器在網路執行檢索、蒐集及擷取工作，這些資料經過轉換、調合、彙總後再匯入資料倉儲，使得日後這類資料的查詢績效得以改善。至於具時效性及動態性的資料，則以本體論加以調合歧義化後，由調節器逕行回覆給使用者。
在資料品質保證階段，我們分成二個步驟進行；首先建立資料品質的實體模型，再運用資料品質成本/效益評估模型，尋求資料品質``最適使用"的狀態。資料品質實體模型是以屬性為基礎，並延用傳統的實體關聯式模型加以擴充，用以將資料品質的因子融入資訊整合的運作過程中，將資料區分為一般應用的屬性資料及提供品質訊息的品質資料，因而得以辨識資料的良窳與適用與否。其次，經由成本/效益評估模型的運算，可以在有限成本控制下針對最嚴重的不良資料作優先的修正處理。
本文闡述的方法可以使得資訊源的整合成果獲得相當改善，特別是在語意互通及資料品質這二方面。我們分別以「整合性環境資訊系統」及「XML/EDI電子商務系統」二個應用實例來佐證本文所倡議的方法的可行性。

Recently, remarkable advances in database and networking technologies have allowed people to search the Internet for the data they need. This raises the demand for information accessing on a network environment. However, the data scattered on a network environment (including text, graphic files, and multimedia) are usually stored in a variety of formats. This yields an environment of heterogeneous information sources because those data formats are usually varied as time goes on; not only are they not compatible, but some are in conflict with each other. In such a complicated and fast changing environment, the related problems of how to provide users an integrated service efficiently and effectively--meanwhile with data quality considerations--have become a significant research issue in both academia and the industry.
This dissertation proposes a three-phase operation mechanism, which consists of the front-end integration phase, the query phase, and the quality assurance phase to integrate information in a heterogeneous environment. We present a lightweight ontology-driven model (LODM), which is the major component to drive the integration process in the front-end integration phase.
An integrated ontology can be viewed as the kernel of concept representation; hence the semantic interoperability among heterogeneous information sources can be achieved at the concept level. We develop a systematic process and related algorithms to support the construction of the integrated ontology. First, we capture the ontologies from existing knowledge sources to form distinct prototype ontologies. Then, those distinct ontologies are aligned and merged to construct an integrated ontology for a specific application domain.
During the query phase, we associate the integrated ontology with two major integration methodologies, namely data warehousing approach and mediation approach. We develop an agent-based mediator for retrieving and gathering historic and static data from underlying sources. The gathered data will be transformed into the warehouse after appropriate conversion, reconcilement, and summarization. Therefore, queries for such information may be submitted to the warehouse exactly as to any database and obtain better performance. Regarding dynamic and real-time data, the mediator handles the queries instantaneously. The results from underlying sources will reconcile the ambiguity by using the integrated ontology, and return to users directly after integrated by the mediator.
Concerning the quality assurance phase, we introduce a two-step process to ensure data quality when performing information integration. We first construct a practical data quality model, then employ the cost/benefit evaluation model to determine the ``fit to use" criteria of data quality. The data quality model is built based upon the E-R Model extended with the consideration of data quality indicators and classifies data into the attribute data and the quality data. The cost/benefit evaluation model is then used to ferret out the poor-quality data and set priorities for improvement given limited resources.
The proposed approach in this dissertation can overall improve the results of information integration in both system performance and data quality. We use two examples, integrated environmental information system and XML/EDI e-Business system, to justify the feasibility of our methodologies.

Contents
1 Introduction 1
1.1 Challenges of Information Integration . . . . . . . . . . . . . . . . . . 2
1.2 An Overview of Various Types of Integration . . . . . . . . . . . . . . 5
1.2.1 Organization viewpoint . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Technology viewpoint . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 The Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Research Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Related Work and Literature Review 13
2.1 Related Work on Information Integration Systems . . . . . . . . . . . 13
2.1.1 Data warehouse systems . . . . . . . . . . . . . . . . . . . . . 13
2.1.2 Mediator systems . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2 Related Work on Ontology Issues . . . . . . . . . . . . . . . . . . . . 19
2.2.1 What is an ontology? . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.2 Projects in ontology design and development . . . . . . . . . . 20
2.3 Related Work on Data Quality . . . . . . . . . . . . . . . . . . . . . . 24
3 Integrating Domain-dependent Ontologies 26
3.1 The Role of Ontologies and Why Need It? . . . . . . . . . . . . . . . 26
3.2 Lightweight Ontology-Driven Model (LODM) . . . . . . . . . . . . . 29
3.3 Developing Ontologies for Information Integration . . . . . . . . . . . 30
3.3.1 Ontologies alignment and integration . . . . . . . . . . . . . . 35
3.3.2 Ontology enrichment . . . . . . . . . . . . . . . . . . . . . . . 38
3.4 Components for Query Phase . . . . . . . . . . . . . . . . . . . . . . 40
3.4.1 Wrappers and uniform protocol . . . . . . . . . . . . . . . . . 40
3.4.2 Agent-based mediators . . . . . . . . . . . . . . . . . . . . . . 42
3.4.3 Intelligent interfaces . . . . . . . . . . . . . . . . . . . . . . . 45
3.4.4 Ontology server . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4.5 Constraints/noti‾cation manager (CNM) . . . . . . . . . . . . 46
4 Ensuring Data Quality 48
4.1 Framework of Data Quality Assurance . . . . . . . . . . . . . . . . . 49
4.1.1 Data quality hierarchy . . . . . . . . . . . . . . . . . . . . . . 50
4.1.2 Process of data quality management . . . . . . . . . . . . . . 53
4.2 Attribute-Based Metadata Model . . . . . . . . . . . . . . . . . . . . 56
4.2.1 Metadata to support data quality . . . . . . . . . . . . . . . . 56
4.2.2 Understanding quality with metadata . . . . . . . . . . . . . . 57
4.3 Identifying and Evaluating Data Quality . . . . . . . . . . . . . . . . 60
4.3.1 Data quality identi‾cation . . . . . . . . . . . . . . . . . . . . 60
4.3.2 Cost/bene‾t evaluation . . . . . . . . . . . . . . . . . . . . . . 62
5 Case Studies 69
5.1 Integrated Environmental Information System . . . . . . . . . . . . . 69
5.1.1 The problems of environmental information . . . . . . . . . . 70
5.1.2 A query for integrated air quality information . . . . . . . . . 71
5.1.3 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 Development of the Internet e-Business Systems . . . . . . . . . . . . 79
5.2.1 Coupling ontologies with Internet-based applications . . . . . 80
5.2.2 Combining UML and XML . . . . . . . . . . . . . . . . . . . 81
5.2.3 An e-Business example . . . . . . . . . . . . . . . . . . . . . . 83
5.2.4 The implementation process . . . . . . . . . . . . . . . . . . . 85
6 Conclusions and Future Work 92
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Appendix: Source Speci‾cation for Air Quality Information 96
BIBLIOGRAPHY 104

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