Semi-automated schema integration with SASMINT

Unal, Ozgul

doi:10.1007/s10115-009-0217-z

Cited by 13 publications

(10 citation statements)

References 51 publications

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“…Zhao and Ram [26] took into account the instance information in the process of integrating heterogeneous data sources. In one of the recent efforts, Ozgul and Afsarmanesh [27] used a variety of metrics and algorithms from the domains of Natural Language Processing and Graph theory for schema matching.…”

Section: Schema Integrationmentioning

confidence: 99%

Explication and semantic querying of enterprise information systems

et al. 2013

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Many researches show that the ability of independent, heterogeneous enterprises' information systems to interoperate is related to the challenges of making their semantics explicit and formal, so that the messages are not merely exchanged, but interpreted, without ambiguity. In this paper, we present an approach to overcome those challenges by developing a method for explication of the systems' implicit semantics. We define and implement the method for the generation of local ontologies, based on the databases of their systems. In addition, we describe an associated method for the translation between semantic and SQL queries, a process in which implicit semantics of the EIS's databases and explicit semantics of the local ontologies become interrelated. Both methods are demonstrated in the case of creating the local ontology and the semantic querying of OpenERP Enterprise Resource Planning system, for the benefit of the collaborative supply chain planning.

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Section: Schema Integrationmentioning

confidence: 99%

Explication and semantic querying of enterprise information systems

et al. 2013

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“…Examples of such constraints are data types, value ranges, uniqueness, optionality, relationship types, and cardinalities. For instance, OntoMatch [12], DIKE [13], and SASMINT [14] use this type of matcher. Our approach is different, since we use an uncertain approach for modeling and generating mediated schemas.…”

Section: Related Workmentioning

confidence: 99%

An Uncertain Data Integration System

Ayat

Akbarinia

Valduriez

2012

On the Move to Meaningful Internet Systems: OTM 2012

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Abstract. Data integration systems offer uniform access to a set of autonomous and heterogeneous data sources. An important task in setting up a data integration system is to match the attributes of the source schemas. In this paper, we propose a data integration system which uses the knowledge implied within functional dependencies for matching the source schemas. We build our system on a probabilistic data model to capture the uncertainty arising during the matching process. Our performance validation confirms the importance of functional dependencies and also using a probabilistic data model in improving the quality of schema matching. Our experimental results show significant performance gain compared to the baseline approaches. They also show that our system scales well.

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“…Semantic similarities and differences are difficult to recognize and resolve, it needs to understand the intended meaning of a concept for each element ( [5], [6]). The semantic conflict of schema during the schema integration can be in terms of naming conflict (homonym and synonym), type conflict, key and cardinality conflict, and etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Schema Integrator (ISI): A tool to solve the problem of naming conflict for schema integration

Ahmad

Chiew

Samad

2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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The data stored in the data warehouse are mostly coming from different sources. It may be developed using different model or structure for the schema. In order to improve the usability of these data, the process of combining or integrating is needed so that it can provide users with a unified view or a global view of these data. The most important issue in data integration is the schema integration: that is to solve the problem of "how can equivalent real-world entities from multiple data sources be matched up?" This is referred to as entity identification process. Terms may be given a different interpretation at different sources by different people. For example, how can data analyst be sure that customer_id in one database and cust_number in another refer to the same entity? In this paper, a tool which is called an Intelligent Schema Integrator (ISI) is built to increase the uses of data from the data warehouse and to make the process more simple, systematic and impressive. ISI is an intelligent tool which can be used to integrate two different schemas from different sources into a unified schema (global schema). ISI is developed to solve the problems of naming conflict which are homonym conflict and synonym conflict. Homonym conflict means the same element name is used to represent different concept. Synonym conflict means different element name is used to represent the same concept. Thesaurus is used to get the meaning of each element concept and compares it with the other concept. An interface is built to allow the user to choose which elements are going to be renamed or removed, if there are occurrences of homonym and synonym conflicts in the schemas. These are the intelligence features built for ISI. The methodology used in this study consists of 4 phases: Design the Input and Output, Extraction, Comparison, and Integration. The development of this tool is an important direction for more efficient and effective implementation of data integration in data warehousing.

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Semi-automated schema integration with SASMINT

Cited by 13 publications

References 51 publications

Explication and semantic querying of enterprise information systems

Explication and semantic querying of enterprise information systems

An Uncertain Data Integration System

Intelligent Schema Integrator (ISI): A tool to solve the problem of naming conflict for schema integration

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