An Efficient and Adaptive Framework to Access Heterogeneous Health Information Sources

In health informatics, the need for a consistent and integrated view of distributed and heterogeneous information sources is inevitable. Healthcare, medical education and research could benefit of integrating medical information of patients or about a disease, a treatment or side effects of a drug. This article proposes a flexible incremental update method for the materialized part of the integration system. It permits us to manage the integration system according to the characteristics of the data sources, which can be changed. We present a hybrid data integration approach.In this approach the materialized part of the system in mediator is the object indexation structure based on an instance classification of the sources objects which correspond to the global schema. The object identifier of each object in the indexation structure is materialized together with the attributes which are needed for the incremental updating of this indexation (classifying attributes).

Reasoning about Disclosure in Data Integration in the Presence of Source Constraints

Data integration systems allow users to access data sitting in multiple sources by means of queries over a global schema, related to the sources via mappings. Datasources often contain sensitive information, and thus an analysis is needed to verify that a schema satisfies a privacy policy, given as a set of queries whose answers should not be accessible to users. Such an analysis should take into account not only knowledge that an attacker may have about the mappings, but also what they may know about the semantics of the sources.In this paper, we show that source constraints can have a dramatic impact on disclosure analysis. We study the problem of determining whether a given data integration system discloses a source query to an attacker in the presence of constraints, providing both lower and upper bounds on source-aware disclosure analysis.

Schema Integration of Web Tables (SIWeT)

Schema integration has been mainly applied in database environment whether schemas to be integrated belonged to a single organization or multiple ones. Schema extraction is a relatively new area where schema is extracted from a web table. The extracted schema is not as much concretely defined as in a typical database environment. The work in this paper brings two areas together where extracted schemas from multiple web tables are integrated to form a global schema. The data are also extracted from web tables and placed into global table. This creates a large repository of data of the same domain extracted dynamically from websites which is then available for different types of ad-hoc queries. This work also imposes challenges on schema integration to be studied in the context of schema extraction and other way round.

Solutions for system analysis and information support of the various activities in the Arctic

Comprehensive use of data and knowledge obtained within different disciplines is necessary for the scientific substantiation of activities in the Arctic zone and for a system analysis of the possible consequences of this activity. Information resources created so far allow the access to a variety of data on the Arctic. The authors propose the solution for task of data consistency ensuring in the field of combined presentation and use of data and knowledge of interdisciplinary research. The proposed solution is based on the joint use of relational database and ontology. The developed structure and mechanisms of the database maintenance provide a uniform representation of the information about results of the researches executed in the framework of various disciplines. The ontology is a high-level global schema of the information system and it provides a dictionary that is used to formulate a database query in terms of a subject domain. In this work, ontology is implemented as a system of small fragments - ontology design patterns. The patterns use makes it possible to perform efficient preliminary database indexing, which ensures faster execution of user queries.

The MAV-ES Data Integration Approach for Decisional Information Systems (DIS)

Massive data to facilitate decision making for organizations and their corporate users exist in many forms, types and formats. Importantly, the acquisition and retrieval of relevant supporting information should be timely, precise and complete. Unfortunately, due to differences in syntax and semantics, the extraction and integration of available semi-structured data from different sources often fail. Needs for seamless and effective data integration so as to access, retrieve and use information from diverse data sources cannot be overly emphasized. Moreover, information external to organizations may also often have to be sourced for the intended users through a smart data integration system. Owing to the open, dynamic and heterogeneity nature of data, data integration is becoming an increasingly complex process. A new data integration approach encapsulating mediator systems and data warehouse is proposed here. Aside from the heterogeneity of data sources, other data integration design problems include distinguishing the definition of the global schema, the mappings and query processing. In order to meet all of these challenges, the authors of this paper advocate an approach named MAV-ES, which is characterized by an architecture based on a global schema, partial schemas and a set of sources. The primary benefit of this architecture is that it combines the two basic GAV and LAV approaches so as to realize added-value benefits of the mixed approach.

Study on Storage and Query System of Legal Documents

There is a logically centralized level global data centers to meet the global schema database for centralized storage needs. This will not only ensure efficient query dataset brings advantages, without compromising the autonomy of each data source. Logically centralized layer needs to have at least a central database, data dump module. This paper studies storage and query system of legal documents based on the information integration system and its implementation methods, the application can be completed more intelligent reasoning queries.

Integrating Heterogeneous Data for Big Data Analysis

Data is an integral part of most business-critical applications. As business data increases in volume and in variety due to technological, business, and other factors, managing this diverse volume of data becomes more difficult. A new paradigm, data virtualization, is used for data management. Although a lot of research has been conducted on developing techniques to accurately store huge amounts of data and to process this data with optimal resource utilization, research remains on how to handle divergent data from multiple data sources. In this chapter, the authors first look at the emerging problem of “big data” with a brief introduction to the emergence of data virtualization and at an existing system that implements data virtualization. Because data virtualization requires techniques to integrate data, the authors look at the problems of divergent data in terms of value, syntax, semantic, and structural differences. Some proposed methods to help resolve these differences are examined in order to enable the mapping of this divergent data into a homogeneous global schema that can more easily be used for big data analysis. Finally, some tools and industrial examples are given in order to demonstrate different approaches of heterogeneous data integration.