This works in developing an integrated data management, retrieval and visualization system for earth science
datasets with extensibility, scalability, uniformity, transparency and heterogeneity. XML based
metadata mechanism is the foundation of data management in our system. Dynamically
generated query GUI makes it easy and convenient for scientists to access and retrieval
diverse datasets. Scientific visualization toolkits display huge amount of data graphically to help
researchers have better understanding of the data and gain valuable insights of the datasets under
investigation. This system helps earth scientists use, share and visualize data more efficiently.
Without knowing any information of the physical storage location, content, structure and format of
each dataset instance, and without programming a single line of codes, scientists can now query
heterogeneous data easily, and view and understand the retrieved data in analytical and graphical ways.
DATA QUERY AND RETRIEVAL
1. Dynamically Generated Query GUIs
To allow users to be able to query data system without background knowledge and training, it
provides query GUIs for all datasets in the system. Its approach is to create a data query system
that dynamically creates dataset query GUI for diverse datasets based on characteristic of diverse
datasets. These characteristics are described in XML metadata. By using stored metadata to
create the query interfaces, a standardized yet dynamic system is created that allows querying of
assorted datasets. By this way, the system eliminates the need to create custom programs for
different datasets. When adding new datasets to the system, query GUI for these datasets will be
dynamically generated if the characteristic of these datasets has been specified in metadata.
Therefore, the system provides an extensible and scalable query GUI framework. Through
dynamically generated query GUI, scientists can specify search conditions, customize the format
and the resolution type of the result files as needed.
2. Query Categories
To make it convenient, users are able to search for data by the category of physical data
types or data sources. Users may also define new categories by adding their definitions into
metadata and the system will then dynamically add them into GUIs.
3. Data Search and Retrieval
The data retrieval is based on the metadata system, that is, the description of the physical
datasets. After user submit query request through GUI, the query can be then devised to search over
the metadata catalogue, which is implemented in hierarchy. Once all data files that possibly satisfy
the query have been identified by searching the available metadata, the system will retrieve these
files holding the actual data and obtain useful result data. If the resolution type is not the same
as original request, it will do computation and obtain data in new resolution. Then it organizes
the result data file in the format that users specified.
The complex manufacturing processes have to be dynamically modelled and controlled to optimise the
diagnosis and the maintenance policies. The methodology that will help developing Dynamic Object
Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability
evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation
methodology consists in using the a priori knowledge of both the system’s functioning and malfunctioning. Networks are built
on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to
evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been
tested, in an industrial context, to model the reliability of a water (immersion) heater system.
One of the main challenges of the Extended Enterprise is to maintain and to optimise the quality of
the services delivered by industrial objects in a dynamic way along their life cycle. The purpose is to conceive
decision aiding systems to maintain the system in operation. Nevertheless, most of the automated systems
do not provide the means of intelligent interpretation of the information when great process disturbances have to
be considered. Moreover, decisions can be taken without a perfect perception of state of the system. This partial
perception argues in favour of using a probabilistic estimation of the system state. The Artificial Intelligence can be used to
bring help in decision aiding systems of manufacturing processes.