69,167 research outputs found
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Adaptive development and maintenance of user-centric software systems
A software system cannot be developed without considering the various facets of its environment. Stakeholders â including the users that play a central role â have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its
full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and
maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems
Monitoring land use changes using geo-information : possibilities, methods and adapted techniques
Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets
A semantic web approach for built heritage representation
In a built heritage process, meant as a structured system of activities
aimed at the investigation, preservation, and management of architectural
heritage, any task accomplished by the several actors involved in it is deeply
influenced by the way the knowledge is represented and shared. In the current
heritage practice, knowledge representation and management have shown several
limitations due to the difficulty of dealing with large amount of extremely heterogeneous
data. On this basis, this research aims at extending semantic web
approaches and technologies to architectural heritage knowledge management in
order to provide an integrated and multidisciplinary representation of the artifact
and of the knowledge necessary to support any decision or any intervention and
management activity. To this purpose, an ontology-based system, representing
the knowledge related to the artifact and its contexts, has been developed through
the formalization of domain-specific entities and relationships between them
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Images seen during test time are often not from the same distribution as
images used for learning. This problem, known as domain shift, occurs when
training classifiers from object-centric internet image databases and trying to
apply them directly to scene understanding tasks. The consequence is often
severe performance degradation and is one of the major barriers for the
application of classifiers in real-world systems. In this paper, we show how to
learn transform-based domain adaptation classifiers in a scalable manner. The
key idea is to exploit an implicit rank constraint, originated from a
max-margin domain adaptation formulation, to make optimization tractable.
Experiments show that the transformation between domains can be very
efficiently learned from data and easily applied to new categories. This begins
to bridge the gap between large-scale internet image collections and object
images captured in everyday life environments
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving beyond fixed training datasets will lead to more robust visual
systems, especially when deployed on robots in new environments which must
train on the objects they encounter there. To make this possible, it is
important to break free from the need for manual annotators. Recent work has
begun to investigate how to use the massive amount of images available on the
Web in place of manual image annotations. We contribute to this research thread
with two findings: (1) a study correlating a given level of noisily labels to
the expected drop in accuracy, for two deep architectures, on two different
types of noise, that clearly identifies GoogLeNet as a suitable architecture
for learning from Web data; (2) a recipe for the creation of Web datasets with
minimal noise and maximum visual variability, based on a visual and natural
language processing concept expansion strategy. By combining these two results,
we obtain a method for learning powerful deep object models automatically from
the Web. We confirm the effectiveness of our approach through object
categorization experiments using our Web-derived version of ImageNet on a
popular robot vision benchmark database, and on a lifelong object discovery
task on a mobile robot.Comment: 8 pages, 7 figures, 3 table
Development of a data model for an Adaptive Multimedia Presentation System (AMPS)
We investigate the requirements and nature of data models for a multimedia learning system that presents adaptable learning objects based on a range of stimuli provided by the student and tutor. A conceptual model is explored together with a proposal for an implementation using the well-known relational data model. We also investigate how to describe the learning objects in the form of hierarchical subject ontology. An ontological calculus is created to allow knowledge metrics to be constructed for evaluation within data models. We further consider the limitations of the relational abstract data model to accurately represent the meaning and understanding of learning objects and contrast this with less structured data models implicit in ontological hierarchies. Our findings indicate that more consideration is needed into how to match traditional data models with ontological structures, especially in the area of database integrity constraints
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Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems
There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation âchallengeâ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying âchallengeâ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings
Supporting Special-Purpose Health Care Models via Web Interfaces
The potential of the Web, via both the Internet and intranets, to facilitate development of clinical information systems has been evident for some time. Most Web-based clinical workstations interfaces, however, provide merely a loose collection of access channels. There are numerous examples of systems for access to either patient data or clinical guidelines, but only isolated cases where clinical decision support is presented integrally with the process of patient care, in particular, in the form of active alerts and reminders based on patient data. Moreover, pressures in the health industry are increasing the need for doctors to practice in accordance with Âżbest practiceÂż guidelines and often to operate under novel health-care arrangements. We present the Care Plan On-Line (CPOL) system, which provides intranet-based support for the SA HealthPlus Coordinated Care model for chronic disease management. We describe the interface design rationale of CPOL and its implementation framework, which is flexible and broadly applicable to support new health care models over intranets or the Internet
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