Ozgul Unal

Semi-automated schema integration with SASMINT

The emergence of increasing number of collaborating organizations has made clear the need for supporting interoperability infrastructures, enabling sharing and exchange of data among organizations. Schema matching and schema integration are the crucial components of the interoperability infrastructures, and their semi-automation to interrelate or integrate heterogeneous and autonomous databases in collaborative networks is desired. The Semi-Automatic Schema Matching and INTegration (SASMINT) System introduced in this paper identifies and resolves several important syntactic, semantic, and structural conflicts among schemas of relational databases to find their likely matches automatically. Furthermore, after getting the user validation on the matched results, it proposes an integrated schema. SASMINT uses a combination of a variety of metrics and algorithms from the Natural Language Processing and Graph Theory domains for its schema matching. For the schema integration, it utilizes a number of derivation rules defined in the scope of the research work explained in this paper. Furthermore, a derivation language called SASMINT Derivation Markup Language (SDML) is defined for capturing and formulating both the results of matching and the integration that can be further used, for example for federated query processing from independent databases. In summary, the paper focuses on addressing: (1) conflicts among schemas that make automatic schema matching and integration difficult, (2) the main components of the SASMINT approach and system, (3) in-depth exploration of SDML, (4) heuristic rules designed and implemented as part of the schema integration component of the SASMINT system, and (5) experimental evaluation of SASMINT.

Interoperability in Collaborative Network of Biodiversity Organizations

Schematic and semantic heterogeneity are two important types of heterogeneities that need to be resolved in order to enable interoperability and exchange of data among distributed and heterogeneous databases in a collaborative network of biodiversity nodes. This paper describes the SASMINT system, which performs schema matching and integration among databases. SASMINT identifies syntactic/semantic/structural similarities between two schemas as automatically as possible, resolving their heterogeneity and creating mappings among the pairs of matched components. Unlike other systems that are typically limited to specific algorithms, SASMINT combines a number of algorithms from the NLP and graph theory domains. After obtaining the user-input on validation/enhancement of matching results, SASMINT exploits the results of schema matching to automatically generate an integrated schema.

SASMINT system for database interoperability in collaborative networks

In most suggested systems aiming to enable interoperability and collaboration among heterogeneous databases, schema matching and integration is performed manually The SASMINT system introduced in this paper proposes a (semi-) automated approach to tackle the following: 1) identification of the syntactic/semantic/structural similarities between the donor and recipient schemas to resolve their heterogeneities, 2) suggestion of corresponding mappings among the pairs of matched components, 3) facilitation of user-interaction with the system, necessary for validation/enhancement of results, and 4) generation of a proposed integrated schema, and a set of derivation rules for each of its components to support query processing against integrated sources Unlike other systems that typically apply one specific algorithm, SASMINT applies a hybrid approach for schema matching that combines a selection of algorithms from NLP and graph theory Furthermore, SASMINT exploits the user-validated schema matching results in its semi-automatic generation of the integrated schema and its necessary derivations.

Using ontologies for collaborative information management: some challenges and ideas

Emerging collaborative networks (CNs) bring together the need for an effective collaborative information management (CIM) infrastructure. Achieving a common understanding of the concepts and entities in CNs, especially with the presence of different types of heterogeneity involved in such environments, represents a big challenge for the developers of the CIM infrastructure. This paper proposes some ontology-based solutions to this challenge in three different research domains. Among the described solutions, the first one addresses the challenges of data representation and implementation regarding the translation of semantic model into appropriate data structures, the second one suggests an ontology-based semi-automatic approach for the resolution of schematic and semantic heterogeneity, and the third one aims to assist scientists from different e-science domains when customizing their experimentation environments with user-defined experiments and data types.

An Agile Innovation Framework Supported through Business Incubators

Entrepreneurs and SMEs that develop new ideas are the main sources of innovation in products and services in the society. However, due to the lack of required resources and know-how innovation ideas may not develop into full-fledged results. Business incubators can play an important role here. They can provide innovators with the required enabling environment, guidelines, and tools supporting the innovation process. This paper proposes an enabling agile innovation framework, which is based on using VBE and VO paradigms. The paper first defines the innovation process, and then addresses a set of tools and mechanisms that need to be utilized to facilitate the innovation process. An example case in intelligent buildings is also provided.

Building a Virtual Laboratory for Scientific Experimentation in Molecular Biology

This paper describes the application of the generic framework provided by the Grid-based Virtual Laboratory Amsterdam (VLAM-G), in the support of complex experimentation scenarios in the domain of molecular biology. The focus of the paper lies on both the analysis of some reference experimentation scenarios, and the on-going extension and tuning of Virtual Laboratory environment to better support advanced scientific experiments in this domain.