4,293 research outputs found
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Random field sampling for a simplified model of melt-blowing considering turbulent velocity fluctuations
In melt-blowing very thin liquid fiber jets are spun due to high-velocity air
streams. In literature there is a clear, unsolved discrepancy between the
measured and computed jet attenuation. In this paper we will verify numerically
that the turbulent velocity fluctuations causing a random aerodynamic drag on
the fiber jets -- that has been neglected so far -- are the crucial effect to
close this gap. For this purpose, we model the velocity fluctuations as vector
Gaussian random fields on top of a k-epsilon turbulence description and develop
an efficient sampling procedure. Taking advantage of the special covariance
structure the effort of the sampling is linear in the discretization and makes
the realization possible
Falls Prediction in Care Homes Using Mobile App Data Collection
Falls are one of the leading causes of unintentional injury related deaths in older adults. Although, falls among elderly is a well documented phenomena; falls of care homesâ residents was under-researched, mainly due to the lack of documented data. In this study, we use data from over 1,769 care homes and 68,200 residents across the UK, which is based on carers who routinely documented the residentsâ activities, using the Mobile Care Monitoring mobile app over three years. This study focuses on predicting the first fall of elderly living in care homes a week ahead. We intend to predict continuously based on a time window of the last weeks. Due to the intrinsic longitudinal nature of the data and its heterogeneity, we employ the use of Temporal Abstraction and Time Intervals Related Patterns discovery, which are used as features for classification. We had designed an experiment that reflects real-life conditions to evaluate the framework. Using four weeks of observation time window performed best
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
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