50,303 research outputs found
System Support for Managing Invalid Bindings
Context-aware adaptation is a central aspect of pervasive computing
applications, enabling them to adapt and perform tasks based on contextual
information. One of the aspects of context-aware adaptation is reconfiguration
in which bindings are created between application component and remote services
in order to realize new behaviour in response to contextual information.
Various research efforts provide reconfiguration support and allow the
development of adaptive context-aware applications from high-level
specifications, but don't consider failure conditions that might arise during
execution of such applications, making bindings between application and remote
services invalid. To this end, we propose and implement our design approach to
reconfiguration to manage invalid bindings. The development and modification of
adaptive context-aware applications is a complex task, and an issue of an
invalidity of bindings further complicates development efforts. To reduce the
development efforts, our approach provides an application-transparent solution
where the issue of the invalidity of bindings is handled by our system,
Policy-Based Contextual Reconfiguration and Adaptation (PCRA), not by an
application developer. In this paper, we present and describe our approach to
managing invalid bindings and compare it with other approaches to this problem.
We also provide performance evaluation of our approach
BioGUID: resolving, discovering, and minting identifiers for biodiversity informatics
Background: Linking together the data of interest to biodiversity researchers (including specimen records, images, taxonomic names, and DNA sequences) requires services that can mint, resolve, and discover globally unique identifiers (including, but not limited to, DOIs, HTTP URIs, and LSIDs).
Results: BioGUID implements a range of services, the core ones being an OpenURL resolver for bibliographic resources, and a LSID resolver. The LSID resolver supports Linked Data-friendly resolution using HTTP 303 redirects and content negotiation. Additional services include journal ISSN look-up, author name matching, and a tool to monitor the status of biodiversity data providers.
Conclusion: BioGUID is available at http://bioguid.info/. Source code is available from http://code.google.com/p/bioguid/
Context-aware, ontology-based, service discovery
Service discovery is a process of locating, or discovering, one or more documents, that describe a particular service. Most of the current service discovery approaches perform syntactic matching, that is, they retrieve services descriptions that contain particular keywords from the user’s query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or syntactically similar but semantically different from the terms in a service description. Another drawback of the existing service discovery mechanisms is that the query-service matching score is calculated taking into account only the keywords from the user’s query and the terms in the service descriptions. Thus, regardless of the context of the service user and the context of the services providers, the same list of results is returned in response to a particular query. This paper presents a novel approach for service discovery that uses ontologies to capture the semantics of the user’s query, of the services and of the contextual information that is considered relevant in the matching process
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
- …