An Overview of Ontology-Driven Data Integration

2009 ◽  
pp. 471-480
Author(s):  
Agustina Buccella ◽  
Alejandra Cechich
Keyword(s):  
Data Integration ◽  
Database Management ◽  
Database Management Systems ◽  
Software Requirements ◽  
Management Systems ◽  
Critical Data ◽  
The World ◽  
Common Interface ◽  
Large Enterprises

New software requirements have emerged because of innovation in technology, specially involving network aspects. The possibility enterprises, institutions and even common users can improve their connectivity allowing them to work as they are at the same time, generates an explosion in this area. Besides, nowadays it is very common to hear that large enterprises fuse with others. Therefore, requirements as interoperability and integrability are part of any type of organization around the world. In general, large modern enterprises use different database management systems to store and search their critical data. All of these databases are very important for an enterprise but the different interfaces they possibly have make difficult their administration. Therefore, recovering information through a common interface becomes crucial in order to realize, for instance, the full value of data contained in the databases (Hass & Lin, 2002).

An Overview of Ontology-Driven Data Integration

2011 ◽  
pp. 207-216
Author(s):  
Agustina Buccella ◽  
Alejandra Cechich
Keyword(s):  
Data Integration ◽  
Database Management ◽  
Database Management Systems ◽  
Software Requirements ◽  
Management Systems ◽  
Critical Data ◽  
The World ◽  
Common Interface ◽  
Large Enterprises

New software requirements have emerged because of innovation in technology, specially involving network aspects. The possibility enterprises, institutions and even common users can improve their connectivity allowing them to work as they are at the same time, generates an explosion in this area. Besides, nowadays it is very common to hear that large enterprises fuse with others. Therefore, requirements as interoperability and integrability are part of any type of organization around the world. In general, large modern enterprises use different database management systems to store and search their critical data. All of these databases are very important for an enterprise but the different interfaces they possibly have make difficult their administration. Therefore, recovering information through a common interface becomes crucial in order to realize, for instance, the full value of data contained in the databases (Hass & Lin, 2002).

Ontology-Based Data Integration

2005 ◽  
pp. 450-456 ◽  
Author(s):  
Agustina Buccella ◽  
Alejandra Cechich ◽  
Nieves Rodríguez Brisaboa
Keyword(s):  
Data Integration ◽  
Geographical Distribution ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Critical Data ◽  
Common Interface

Nowadays, different areas of large modern enterprises use different database management systems to store and search their critical data. Competition, evolving technology, geographical distribution, and the inevitable growing decentralization contribute to this diversity. All of these databases are very important to an enterprise, but the their different interfaces make their administration difficult. Therefore, recovering information through a common interface becomes crucial to realize, for instance, the full value of data contained in the databases (Hass & Lin, 2002).

scholarly journals Machine Learning for Database Management Systems

2020 ◽  
Vol 9 (08) ◽  
pp. 25132-25147
Author(s):  
Sai Tanishq N
Keyword(s):  
Machine Learning ◽  
Problem Solving ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Complex Problems ◽  
Prediction Algorithms ◽  
The World ◽  
Data Explosion

Machine Learning (ML) is transforming the world with research breakthroughs that are leading to the progress of every field. We are living in an era of data explosion. This further improves the output as data that can be fed to the models is more than it has ever been. Therefore, prediction algorithms are now capable of solving many of the complex problems that we face by leveraging the power of data. The models are capable of correlating a dataset and its features with an accuracy that humans fail to achieve. Bearing this in mind, this research takes an in-depth look into the of the problem- solving potential of ML in the area of Database Management Systems (DBMS). Although ML hallmarks significant scientific milestones, the field is still in its infancy. The limitations of ML models are also studied in this paper.

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