Supporting Multiple Representations inSpatio-Temporal databases

Successful information management implies the ability to design accurate representations of the real world of interest to targeted applications. Current systems do not provide representation schemes supporting the diversity of user needs. In the context of interoperability, as in Web access to heterogeneous data sources, they cannot properly integrate the diversity of stored representations. The objective of the MurMur project is to enhance GIS (or DBMS) functionality so that, relying on more flexible representation schemes, users may easily manage information using multiple representations. The added functionality will support multiple coexisting representations of the same real-word phenomena (semantic flexibility), including representations of geographic data at multiple resolutions (cartographic flexibility). This will in particular make possible a semantically meaningful management of multi-scale, integrated, and temporal geo-databases

Database Integration: an Overview of Issues and Approaches

In many large companies the widespread usage of computers has led a number of different application-specific databases to be installed. As company structures evolve, boundaries between departments move, creating new business units. Their new applications will use existing data from various data stores, rather than new data entering the organization. Henceforth, the ability to make data stores interoperable becomes a crucial factor for the development of new information systems. Data interoperability may come in various degrees. At the lowest level, commercial gateways connect specific pairs of database management systems (DBMSs). Software providing facilities for defining persistent views over different databases [6] simplifies access to distant data but does not support automatic enforcement of consistency constraints among different databases. Full interoperability is achieved by distributed or federated database systems, which support integration of existing data into virtual databases (i.e. databases which are logically defined but not physically materialized). The latter allow existing databases to remain under control of their respective owners, thus supporting a harmonious coexistence of scalable data integration and site autonomy requirements [9]. Federated systems are very popular today. However, before they become marketable, many issues remain to be solved. Design issues focus on either human-centered aspects (cooperative work, including autonomy issues and negotiation procedures) or database-centered aspects (data integration, schema/database evolution). Operational issues investigate system interoperability mainly in terms of support of new transaction types, new query processing algorithms, security concerns, etc. General overviews may be found elsewhere [4, 9]. This paper is devoted to database integration, possibly the most critical issue. Simply stated, database integration is the process which takes as input a set of databases, and produces as output a single unified description of the input schemas (the integrated schema) and the associated mapping information supporting integrated access to existing data through the integrated schema. As such, database integration is also used in the process of re-engineering an exist i ng l egacy system. Database integration has attracted many diverse and diverging contributions. The purpose, and the main intended contribution of this article is to provide a clear picture of what are the approaches and the current solutions and what remains to be achieved

A Modular Data Infrastructure for Location-Based Services

Knowledgable Location-ha,sed Services (LBS) aim at enabling mobile users to specify their requests and profiles oil the move and providing there with context-aware and personalized local information relevant to their current, activity and request. Therefore, it opens up new challenges ill data infrastructure; knowledge representations and data management etc. In this paper, firstly we will discuss the characteristics of LBS in terms of data management, and then present our data architecture. Finally, we will explain how the knowledge is incrementally set up and maintained in a nodular manner

Spatial Data Warehouse Modelling

is concerned with multidimensional data models for spatial data warehouses. It first draws a picture of the research area, and then introduces a novel spatial multidimensional data model for spatial objects with geometry: the Multigranular Spatial Data warehouse (MuSD). The main novelty of the model is the representation of spatial measures at multiple levels of geometric granularit

Design of a Service for the Management of Heart Failure Patients Using Telemedicine

The tremendous prevalence and mortality of heart failure (HF), along with the social and economic impact of its consequences, make an appropriate disease management utmost important. In this context, telemedicine offers promising possibilities. Current clinical guidelines and technological solutions do not address the problem of monitoring at-risk patients and patients affected by mild HF for prevention purposes. The goal of this work is to design a service based on a telemedicine framework for the management of heart failure patients. The proposed service grounds the monitoring of the patient on a custom multi-sensor array that we designed and developed for the purpose. The description of the processes involved in the service was carried out by means of Process Modelling tools, and in particular through Swim Lane Activity Diagrams. The results look promising for the implementation of the service in a real-life scenario. The main strength of the service resides in a) the use of noninvasive monitoring technologies to include patients with a mild HF or at-risk patients; and b) the integration of hospital and territory services to grant continuity and coherence in the treatment

Database Integration: the Key to Data Interoperability

Most of new databases are no more built from scratch, but re-use existing data from several autonomous data stores. To facilitate application development, the data to be re-used should preferably be redefined as a virtual database, providing for the logical unification of the underlying data sets. This unification process is called database integration. This chapter provides a global picture of the issues raised and the approaches that have been proposed to tackle the problem

Semantic Integration in MADS Conceptual Model

Our vision of a viable way for transparent and meaningful processing of heterogeneous spatio-temporal data is to put data semantics in the foundation of an integration process. We present and correlate means of integration as components of the mediation level of an interoperable system. For our domain of interest we present MADS domain ontologies and MADS conceptual data model dedicated to modeling of spatio-temporal data. Using as example two MADSschemas we outline an integration methodology based on semantic interschema correspondence assertions and integration goals

MurMur: Database Management of MultipleRepresentations

Successful information management implies the ability to design accurate representations of the real world of interest to targeted applications. Current systems do not provide representation schemes supporting the diversity of user needs. In the context of interoperability, as in Web access to heterogeneous data sources, they can-not properly integrate the diversity of stored representations. The objective of the European project described inthis paper, MurMur 1 , is to enhance GIS (or DBMS) functionality so that, relying on more flexible representation schemes, users may easily manage information using multiple representations. The added functionality will support multiple coexisting representations of the same real-word phenomena (semantic flexibility), including representations of geographic data at multiple resolutions (cartographic flexibility). This will in particular make possible a semantically meaningful management of multiscale, integrated, and temporal geo-databases

Spatio-Temporal Conceptual Models: Data Structures + Space + Time

Nowadays, many applications need data modeling facilities for the description of complex objects with spatial and/or temporal facilities. Responses to such requirements may be found in Geographic Information Systems (GIS), in some DBMS, or in the research literature. However, most of existing models cover only partly the requirements (they address either spatial or temporal modeling), and most are at the logical level, hence not well suited for database design. This paper proposes a spatio-temporal modeling approach at the conceptual level, called MADS. The proposal stems from the identification of the criteria to be met for a conceptual model. It is advocated that orthogonality is the key issue for achieving a powerful and intuitive conceptual model. Thus, the proposal focuses on highlighting similarities in the modeling of space and time, which enhance readability and understandability of the model