163,896 research outputs found
CASP-DM: Context Aware Standard Process for Data Mining
We propose an extension of the Cross Industry Standard Process for Data
Mining (CRISPDM) which addresses specific challenges of machine learning and
data mining for context and model reuse handling. This new general
context-aware process model is mapped with CRISP-DM reference model proposing
some new or enhanced outputs
Enhancing Context Specifications for Dependable Adaptive Systems: A Data Mining Approach
Context: Adaptive systems are expected to cater for various operational contexts by having multiple strategies in achieving their objectives and the logic for
matching strategies to an actual context. The prediction of relevant contexts at
design time is paramount for dependability. With the current trend on using data
mining to support the requirements engineering process, this task of understanding context for adaptive system at design time can benefit from such techniques
as well.
Objective: The objective is to provide a method to refine the specification of
contextual variables and their relation to strategies for dependability. This refinement shall detect dependencies between such variables, priorities in monitoring
them, and decide on their relevance in choosing the right strategy in a decision
tree.
Method: Our requirements-driven approach adopts the contextual goal modelling structure in addition to the operationalization values of sensed information
to map contexts to the systemâs behaviour. We propose a design time analysis process using a subset of data mining algorithms to extract a list of relevant contexts
and their related variables, tasks, and/or goals.
Results: We experimentally evaluated our proposal on a Body Sensor Network
system (BSN), simulating 12 resources that could lead to a variability space of
4096 possible context conditions. Our approach was able to elicit subtle contexts that would significantly affect the service provided to assisted patients and
relations between contexts, assisting the decision on their need, and priority in
monitoring.
Conclusion: The use of some data mining techniques can mitigate the lack
of precise definition of contexts and their relation to system strategies for dependability. Our method is practical and supportive to traditional requirements
specification methods, which typically require intense human intervention
Eliciting usage contexts of safety-critical medical devices
This position paper outlines our approach to improve the usage choice of suitable devices in different health care environments (contexts). Safety-critical medical devices are presumed to have undergone a thorough (user-centred) design process to optimize the device for the intended purpose, user group and environment. However, in real-life health care scenarios, actual usage may not reflect the original design parameters. We suggest the identification of further usage contexts for safety-critical medical devices through ethnographic and other studies, to assist better modelling of the challenges of different usage environments. In combination with system and interaction models, these context models can then be used for decision-support in choosing medical devices that are suitable for the intended environment
Context-based task ontologies for clinical guidelines
Evidence-based medicine relies on the execution of clinical practice guidelines and protocols. A great deal of of effort has been invested in the development of various tools which automate the representation and execution of the recommendations contained within such guidelines and protocols by creating Computer Interpretable Guideline Models (CIGMs). Context-based task ontologies (CTOs), based on standard terminology systems like UMLS, form one of the core components of such a model. We have created DAML+OIL-based CTOs for the tasks mentioned in the WHO guideline for hypertension management, drawing comparisons also with other related guidelines. The advantages of CTOs include: contextualization of ontologies, providing ontologies tailored to specific aspects of the phenomena of interest, dividing the complexity involved in creating ontologies into different levels, providing a methodology by means of which the task recommendations contained within guidelines can be integrated into the clinical practices of a health care set-up
Word Embeddings: A Survey
This work lists and describes the main recent strategies for building
fixed-length, dense and distributed representations for words, based on the
distributional hypothesis. These representations are now commonly called word
embeddings and, in addition to encoding surprisingly good syntactic and
semantic information, have been proven useful as extra features in many
downstream NLP tasks.Comment: 10 pages, 2 tables, 1 imag
- âŠ