A Survey on the Evolution of Models of Data Integration

Object-oriented …, 1995

ACM SIGSOFT Software Engineering Notes, 2011

The integration of multiple autonomous and heterogeneous data sources (both across the web and via a company intranet) has received much attention throughout the years, particularly due to its many applications in the fields of Artificial Intelligence and medical research data sharing. Data integration systems embody this work and have come very far in the past twenty years. The problem of designing such systems is characterized by a number of issues that are interesting from a theoretical point of view: answering queries using logical views, query containment and completeness, automatic integration of existing data sources via schema mapping tools, etc. In this work we discuss these issues, compare and contrast various proposed solutions (federated database systems and data warehouses), and finally propose a novel extension of the MVC (model, view, controller) web-based framework that allows for the rapid development and implementation of data integration systems solutions suitable for use on the web.

International Journal of Database Theory and Application

Physical integration of heterogeneous data sources is too high a price for small and medium level enterprises. This is primarily because of the variance in data base management system (MySQL, MsSQL, Oracle, etc.), underlying operating systems (Linux, Windows, etc.), besides numerous legacy sources (DBase, Foxplus, etc.). Enterprises are spending huge amounts for establishing data warehouses while as the data of interest increases gradually [Harmeek Bedi]. Over the years enterprises have accumulated numerous data sources along with legacy repositories which have variant data, schema and context and their integration is increasingly difficult. In this paper we propose a LDIM (Logical Data Integration Model) which will describe data, schema and context for integration of heterogeneous data sources including legacy repositories.

International …, 2001

Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

Indian Journal of Science and Technology, 2016

Objectives: This paper studies various query reformulation techniques, which are used to convert the intermediate schema to the targeted schema. The techniques such as Ontology based information integration and data integration languages are also reviewed. Methods/Statistical Analysis: This paper discusses the techniques used for data integration and also to resolve inconsistencies from the integrated data. Data integration techniques mainly focusing on integration of data in several levels and applying independent or unified query over the data available. Findings: Analysis of various techniques done in the paper has led to the identification of several shortcomings and scope for improvements in the available techniques. This identified research directions includes vertical enhancement of wrappers by utilizing a single unified wrapper for all the data sources. Optimizing the queries depending on the data source is also another major requirement to provide efficient and faster results reducing the data retrieval latencies. The paper also advocates other research directions that include identifying duplicates from the retrieved data and performing effective elimination strategies to reduce space consumption. Identifying conflicts and applying strategies to eliminate conflicts is another major area with a huge scope for improvement. Application/Improvements: The comprehensive survey also recommends further works in the area of data integration techniques.

2009

Dans la partie I de cette thèse nous avons étudié diérents dés et solutions d'intégration de données. Les approches hybrides d'intégration de données sont étudiées dans la partie II. Nous avons également présenté notre architecture hybride, son prototypage et son implémentation dans la partie II. The main idea of this thesis is to develop a semi-materialized data integration framework, which represents a new aspect of a hybrid method [Kermanshahani, 2008]. Motivation The existing hybrid approaches provide a rapid access to the materialized data. Other data remain in the local sources and are queried directly from the sources when necessary. As a consequence, only the queries to the materialized part of the system are optimized. A typical

2003

The information required for decision making by executives in organizations is normally scattered across disparate data sources including databases and legacy systems. To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. Particular problems occur when applying traditional integration approaches, for example, the global schema needs to be recreated when the component schema has been modified. This research investigates the following heterogeneities between heterogeneous data sources: Data Model Heterogeneities, Schematic Heterogeneities and Semantic Heterogeneities. The problems of existing integration approaches are reviewed and solved by introducing and designing a new integration approach to logically interoperate heterogeneous data sources and to resolve three previ...

Vietnam Journal of Computer Science

The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated databas...

Proceedings of the twenty-first ACM SIGMOD-SIGACT- …, 2002

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.