A comparative analysis of methodologies for database schema integration

Batini, Carlo; Lenzerini, Maurizio; Navathe, Shamkant B.

doi:10.1145/27633.27634

Cited by 1,270 publications

(616 citation statements)

References 36 publications

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“…There is a broad array of related work including form-driven database generation [11], DIY web services for form creation [1] schema mapping [20], view integration [7], schema evolution [26], and automatic understanding of Deep Web search interfaces [17]. However, the problem that we address here is a new and has not been addressed before in totality.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatically Mapping and Integrating Multiple Data Entry Forms into a Database

An¹,

Khare²,

Song³

et al. 2011

Conceptual Modeling – ER 2011

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Abstract. Forms are a standard way of gathering data into a database. Many applications need to support multiple users with evolving data gathering requirements. It is desirable to automatically link dynamic forms to the back-end database. We have developed the FormMapper system, a fully automatic solution that accepts user-created data entry forms, and maps and integrates them into an existing database in the same domain. The solution comprises of two components: tree extraction and form integration. The tree extraction component leverages a probabilistic process, Hidden Markov Model (HMM), for automatically extracting a semantic tree structure of a form. In the form integration component, we develop a merging procedure that maps and integrates a tree into an existing database and extends the database with desired properties. We conducted experiments evaluating the performance of the system on several large databases designed from a number of complex forms. Our experimental results show that the FormMapper system is promising: It generated databases that are highly similar (87% overlapped) to those generated by the human experts, given the same set of forms.

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Section: Related Workmentioning

confidence: 99%

“…Schema mapping aims to discover meaningful relationships between database schemas [20,21,23,5], while view/schema integration builds a database schema by unifying a set of views/schemas [7]. Both problems primarily focus on schema elements and do not consider database extension.…”

Section: Related Workmentioning

confidence: 99%

Automatically Mapping and Integrating Multiple Data Entry Forms into a Database

An¹,

Khare²,

Song³

et al. 2011

Conceptual Modeling – ER 2011

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show abstract

“…For the purposes of this study, we took data on projects and grants from RCs in three domains: engineering and physical sciences (EPSRC) 3 , biotechnology and biological sciences (BBSRC) 4 , and medicine (MRC) 5 . These RCs were chosen because their funding activities overlap in an area known as the Life Sciences Interface, which supports interdisciplinary research between engineering and physical sciences, and the life sciences.…”

Section: Data Gatheringmentioning

confidence: 99%

“…Integration of databases raises several well known challenges, such as resolving the conceptual differences between database schemas, identifying data duplications and inconsistencies, etc [4]. Ontologies have been widely proposed as a major role player in information integration [7][14] [15].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Research Collaborations Using Semantic Web Technologies

Alani

Gibbins

Glaser

et al. 2005

Lecture Notes in Computer Science

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Abstract. In the current research environment, funding agencies are increasingly required to demonstrate that the projects they fund represent value for money. When funds are disbursed in a speculative manner, in order to stimulate interdisciplinary collaboration, the determination of value for money relies on evidence that shows the generation of new collaborations. This paper summarises the work we carried out on behalf of the Engineering and Physical Sciences Research Council (EPSRC), in which we have implemented a set of applications to enable the research council to examine the existence and nature of collaborations between researchers. We have used Semantic Web technologies to construct a flexible application framework to provide multiple complementary visualisations of the data, while separating the issues of knowledge acquisition and curation from the more user-centric interface requirements.

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“…Moreover, these semantic differences can cause different kinds of conflicts, ranging from simple contradictions in name use (when different names are used by different sources to indicate the same or similar real-world concept), to structural conflicts (when different models/primitives are used to represent the same information). Complicating factors with respect to conventional view integration techniques [4] are related to the fact that semantic heterogeneity occurs on the large-scale. This heterogeneity involves terminology, structure, and domain of the sources, with respect to geographical, organizational, and functional aspects of the information use [48].…”

mentioning

confidence: 99%

MIKS: An Agent Framework Supporting Information Access and Integration

Beneventano¹,

Bergamaschi²,

Gelati³

et al. 2003

Intelligent Information Agents

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Abstract. Providing an integrated access to multiple heterogeneous sources is a challenging issue in global information systems for cooperation and interoperability. In the past, companies have equipped themselves with data storing systems building up informative systems containing data that are related one another, but which are often redundant, not homogeneous and not always semantically consistent. Moreover, to meet the requirements of global, Internet-based information systems, it is important that the tools developed for supporting these activities are semi-automatic and scalable as much as possible. To face the issues related to scalability in the large-scale, in this paper we propose the exploitation of mobile agents in the information integration area, and, in particular, their integration in the MOMIS infrastructure. MOMIS (Mediator EnvirOnment for Multiple Information Sources) is a system that has been conceived as a pool of tools to provide an integrated access to heterogeneous information stored in traditional databases (for example relational, object oriented databases) or in file systems, as well as in semi-structured data sources (XML-file). This proposal has been implemented within the MIKS (Mediator agent for Integration of Knowledge Sources) system and it is completely described in this paper.

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A comparative analysis of methodologies for database schema integration

Cited by 1,270 publications

References 36 publications

Automatically Mapping and Integrating Multiple Data Entry Forms into a Database

Automatically Mapping and Integrating Multiple Data Entry Forms into a Database

Monitoring Research Collaborations Using Semantic Web Technologies

MIKS: An Agent Framework Supporting Information Access and Integration

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