1,740 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
Partition into heapable sequences, heap tableaux and a multiset extension of Hammersley's process
We investigate partitioning of integer sequences into heapable subsequences
(previously defined and established by Mitzenmacher et al). We show that an
extension of patience sorting computes the decomposition into a minimal number
of heapable subsequences (MHS). We connect this parameter to an interactive
particle system, a multiset extension of Hammersley's process, and investigate
its expected value on a random permutation. In contrast with the (well studied)
case of the longest increasing subsequence, we bring experimental evidence that
the correct asymptotic scaling is . Finally
we give a heap-based extension of Young tableaux, prove a hook inequality and
an extension of the Robinson-Schensted correspondence
Fast linear-space computations of longest common subsequences
AbstractSpace saving techniques in computations of a longest common subsequence (LCS) of two strings are crucial in many applications, notably, in molecular sequence comparisons. For about ten years, however, the only linear-space LCS algorithm known required time quadratic in the length of the input, for all inputs. This paper reviews linear-space LCS computations in connection with two classical paradigms originally designed to take less than quadratic time in favorable circumstances. The objective is to achieve the space reduction without alteration of the asymptotic time complexity of the original algorithm. The first one of the resulting constructions takes time O(n(mâl)), and is thus suitable for cases where the LCS is expected to be close to the shortest input string. The second takes time O(ml log(min[s, m, 2nl])) and suits cases where one of the inputs is much shorter than the other. Here m and n (mâ©œn) are the lengths of the two input strings, l is the length of the longest common subsequences and s is the size of the alphabet. Along the way, a very simple O(m(mâl)) time algorithm is also derived for the case of strings of equal length
- âŠ