Query Reformulation: Data Integration Approach to Multi Domain Query Answering System

Data integration gives the user with a unified view of all heterogeneous data sources. The basic service provided by data integration is query processing. Whatever query posed to the system is being given to global schema which has to reformulate to sub queries that are to be posed to the local sources. Reformulation is being accomplished by mapping between global and local sources by Global-as-View (GAV), Local-as-view (LAV) and Global-local-as-view (GLAV) approach. When a query involves multiple domains, it is difficult to extract information in case of general service engines

Dynamic Integration of Evolving Distributed Databases using Services

This thesis investigates the integration of many separate existing heterogeneous and distributed databases which, due to organizational changes, must be merged and appear as one database. A solution to some database evolution problems is presented. It presents an Evolution Adaptive Service-Oriented Data Integration Architecture (EA-SODIA) to dynamically integrate heterogeneous and distributed source databases, aiming to minimize the cost of the maintenance caused by database evolution. An algorithm, named Relational Schema Mapping by Views (RSMV), is designed to integrate source databases that are exposed as services into a pre-designed global schema that is in a data integrator service. Instead of producing hard-coded programs, views are built using relational algebra operations to eliminate the heterogeneities among the source databases. More importantly, the definitions of those views are represented and stored in the meta-database with some constraints to test their validity. Consequently, the method, called Evolution Detection, is then able to identify in the meta-database the views affected by evolutions and then modify them automatically. An evaluation is presented using case study. Firstly, it is shown that most types of heterogeneity defined in this thesis can be eliminated by RSMV, except semantic conflict. Secondly, it presents that few manual modification on the system is required as long as the evolutions follow the rules. For only three types of database evolutions, human intervention is required and some existing views are discarded. Thirdly, the computational cost of the automatic modification shows a slow linear growth in the number of source database. Other characteristics addressed include EA-SODIA’ scalability, domain independence, autonomy of source databases, and potential of involving other data sources (e.g.XML). Finally, the descriptive comparison with other data integration approaches is presented. It shows that although other approaches may provide better performance of query processing in some circumstances, the service-oriented architecture provide better autonomy, flexibility and capability of evolution

Query optimization by using derivability in a data warehouse environment

Materialized summary tables and cached query results are frequently used for the optimization of aggregate queries in a data warehouse. Query rewriting techniques are incorporated into database systems to use those materialized views and thus avoid the access of the possibly huge raw data. A rewriting is only possible if the query is derivable from these views. Several approaches can be found in the literature to check the derivability and find query rewritings. The specific application scenario of a data warehouse with its multidimensional perspective allows the consideration of much more semantic information, e.g. structural dependencies within the dimension hierarchies and different characteristics of measures. The motivation of this article is to use this information to present conditions for derivability in a large number of relevant cases which go beyond previous approaches

Using Complex Substitution Strategies for View Synchronization

Abstract Large-scale information systems typically contain autonomous information sources (ISs) that dy- namically modify their content interfaces as well as their query services regardless of the data ware- houses (views) that are built on top of them Current view technology fails to provide adaptation techniques for such changes giving support to only static views in the sense that views become unde- fined when ISs undergo capability changes We propose to address this new view evolution problem - which we call view synchronization - by allowing view definitions to be dynamically evolved when they become undefined The foundations of our approach to view synchronization include the Evolvable- SQL view definition language (E-SQL) the model for information source description (MISD) and the concept of legal view rewritings In this paper we now introduce the concept of the strongest synch-equivalent view definition that explicitly defines the evolution semantics associated with an E-SQL view definition Plus we propose a strategy and proofs of correctness for transforming any user-specified E-SQL view definition into the strongest E-SQL query We also present the Complex View Synchronization (CVS) algorithm that fully exploits the constraints defined in MISD by al- lowing relation substitution to be done by a sequence of joins among candidate relations Examples illustrating this multi-step approach are given throughout the pape

Active Ontology: An Information Integration Approach for Dynamic Information Sources

In this paper we describe an ontology-based information integration approach that is suitable for highly dynamic distributed information sources, such as those available in Grid systems. The main challenges addressed are: 1) information changes frequently and information requests have to be answered quickly in order to provide up-to-date information; and 2) the most suitable information sources have to be selected from a set of different distributed ones that can provide the information needed. To deal with the first challenge we use an information cache that works with an update-on-demand policy. To deal with the second we add an information source selection step to the usual architecture used for ontology-based information integration. To illustrate our approach, we have developed an information service that aggregates metadata available in hundreds of information services of the EGEE Grid infrastructure

MDDQL: an ontology driven, multi-lingual query language and system for an integrated view of heterogeneous data sources

Query languages and keywords based search engines are conventionally specified and implemented with the emphasis put on syntactic rules to which query typing and answering must be bound. MDDQL is a query language and system that operates on a semantic model in terms of a graph based ontology. As a software technology, MDDQL allows the meaning of/and associations between information to be known and processed at execution time at following levels: (a) driving the user to the construction of, as meaningful as possible, queries with an advanced concept-based search method, (b) resolving high level queries into various data source specific query statements. In addition, queries can be posed in more than one natural sub-language. The major goal behind this approach has been the simplification and scalability of both tasks: query construction, even within multi-lingual user communities, and addressing of a large number of possibly semantically heterogeneous data sources in a distributed environment