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2004 | Buch

1. Introduction Kapitel lesen Erstes Kapitel lesen

Generic Model Management

Concepts and Algorithms

verfasst von: Sergey Melnik

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Professional Book Archive

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Über dieses Buch

Many challenging problems in information systems engineering involve the manipulation of complex metadata artifacts or models, such as database schema, interface specifications, or object diagrams, and mappings between models. Applications solving metadata manipulation problems are complex and hard to build. The goal of generic model management is to reduce the amount of programming needed to solve such problems by providing a database infrastructure in which a set of high-level algebraic operators are applied to models and mappings as a whole rather than to their individual building blocks.

This book presents a systematic study of the concepts and algorithms for generic model management. The first prototype of a generic model management system is described, the algebraic operators are introduced and analyzed, and novel algorithms for implementing them are developed. Using the prototype system and the operators presented, solutions are developed for several practically relevant problems, such as change propagation and reintegration.

Inhaltsverzeichnis

Frontmatter

A Programming Platform for Model Management

Frontmatter
1. Introduction
Abstract
This chapter highlights the background of the dissertation and outlines its structure. In Sect. 1.1, we introduce metadata management, the general subject of this work. The deficiencies of today’s metadata management techniques are examined in Sect. 1.2. In Sect. 1.3, we sketch the approach to metadata management explored in the dissertation, called generic model management, and formulate our main objectives. An overview of the structure and contributions of the dissertation is given in Sect. 1.4.
Sergey Melnik
2. Conceptual Structures and Operators
Abstract
In this chapter, we describe the conceptual structures and operators that are used in the prototype of a programming platform for model management that we developed.
Sergey Melnik
3. Implementation and Applications
Abstract
This chapter is devoted to the implementation and deployment aspects of the first prototype for model management developed as part of the thesis. The chapter is structured as follows:
  • In Sections 3.1 and 3.2, we describe the implementation of the conceptual structures and operators, respectively. In particular, we present new algorithms developed for the operators Extract and Merge.
  • In Sect. 3.3, we present our prototype in more detail and demonstrate how it can be extended to embrace new kinds of models.
  • In Sections 3.4 and 3.5, we examine the solutions for two further important model-management tasks, view reuse and reintegration, that involve manipulations of relational schemas, XML schemas, and SQL views.
We conclude the chapter and Part I in Sect. 3.6.
Sergey Melnik

A Semantics for Model Management Operators

Frontmatter
4. State-Based Semantics
Abstract
In Part I, we described the first prototype for model management, called Rondo, which offers a set of high-level operators for solving metadata-related problems. Using Rondo, we developed scripts for several practically relevant scenarios, such as change propagation, view reuse, and reintegration. We found that the scripts produce intuitively correct results and that the structural operator definitions that we give are useful for solving practical problems.
Sergey Melnik
5. Change Propagation Scenario
Abstract
In this chapter, we revisit the change propagation scenario.We present a solution for this scenario using the operators of Chap. 4.We argue the correctness of our solution by examining several special cases and by showing that the scripts that we developed match the intuition in these special cases. We cannot formally prove that our solution is correct.
Sergey Melnik
6. State-Based Semantics in Rondo
Abstract
In this chapter, we discuss the relationship between the structural operator definitions given in Chap. 2 and the state-based definitions presented in Chap. 4. To avoid ambiguity, we refer to the operators and scripts used in Rondo (Chap. 2) as structural and to those of Chap. 4 as state-based. The discussion that we present illustrates how the behavior of Rondo and other complex metadata management systems can be analyzed in terms of state-based semantics.
Sergey Melnik

Schema Matching

Frontmatter
7. Similarity Flooding Algorithm
Abstract
Finding correspondences between models is required in many application scenarios. This task is often referred to as matching. In generic model management, matching is embodied in the operator Match, which plays a critical role in many model-management scripts. The operator Match takes two models as input and returns a mapping between the models as output. Of all operators that we examined in the previous chapters, Match is the only one that lacks a formal definition and, in a way, enjoys a special status. The reason for its specialty is that matching typically involves information that is not contained in the input models. Uncovering how two models relate to each other requires reading documentation, examining instances of models, and talking to the engineers who designed or deploy the models.
Sergey Melnik
8. Filters
Abstract
In this chapter we examine several filters that can be used for choosing the best match candidates from the list of ranked map pairs returned by the Similarity Flooding algorithm. Usually, for every element in the matched models, the algorithm delivers a large set of match candidates. Hence, the immediate result of the fixpoint computation may still be too voluminous for many matching tasks. For instance, in a schema matching application the choice presented to a human user for every schema element may be overwhelming, even when the presented match candidates are ordered by rank. We refer to the immediate result of the iterative computation as multimapping, since it contains many potentially useful mappings as subsets.
Sergey Melnik
9. Evaluation and Tuning
Abstract
In this chapter, we suggest an accuracy metric for evaluating automatic schema matching algorithms and evaluate the effectiveness of the SF algorithm on the basis of a user study that we conducted.
Sergey Melnik

Model Management in Perspective

Frontmatter
10. Related Work
Abstract
The work on metadata management looks back onto over three decades of prolific research efforts ranging from the invention of database schemas (Mc-Gee 1959) to database design (Wiederhold 1977), from storing schemas and queries as first-class objects (Stonebraker et al. 1984; den Bussche et al. 1993; Lakshmanan et al. 2001), to transforming them using complex algorithms (Abiteboul et al. 1995; Halevy 2001).
Sergey Melnik
11. Conclusions and Outlook
Abstract
Many problems facing data management and other areas of computer-aided engineering involve the manipulation of models. Yet applications that manipulate models are complicated and hard to build. The goal of generic model management is to reduce the cost of developing such applications by raising the level of abstraction of model manipulation operations.
Sergey Melnik
A. User Study: Gathering Intended Match Results
Abstract
This user study attempts to collect various intended match results for a set of schema matching problems.
Sergey Melnik
B. Proofs of Simplification Theorems
Abstract
In this appendix, we prove the Theorems 4.2.1, 4.2.3, and 4.2.5, which provide simplified characterization of operators Extract, Merge, and Diff, respectively.
Sergey Melnik
Backmatter
Metadaten
Titel
Generic Model Management
verfasst von
Sergey Melnik
Copyright-Jahr
2004
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-24684-8
Print ISBN
978-3-540-21980-4
DOI
https://doi.org/10.1007/b97859