174,012 research outputs found
Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients
Sepsis is a condition caused by the body's overwhelming and life-threatening
response to infection, which can lead to tissue damage, organ failure, and
finally death. Common signs and symptoms include fever, increased heart rate,
increased breathing rate, and confusion. Sepsis is difficult to predict,
diagnose, and treat. Patients who develop sepsis have an increased risk of
complications and death and face higher health care costs and longer
hospitalization. Today, sepsis is one of the leading causes of mortality among
populations in intensive care units (ICUs). In this paper, we look at the
problem of early detection of sepsis by using temporal data mining. We focus on
the use of knowledge-based temporal abstraction to create meaningful
interval-based abstractions, and on time-interval mining to discover frequent
interval-based patterns. We used 2,560 cases derived from the MIMIC-III
database. We found that the distribution of the temporal patterns whose
frequency is above 10% discovered in the records of septic patients during the
last 6 and 12 hours before onset of sepsis is significantly different from that
distribution within a similar period, during an equivalent time window during
hospitalization, in the records of non-septic patients. This discovery is
encouraging for the purpose of performing an early diagnosis of sepsis using
the discovered patterns as constructed features
Significant Interval and Frequent Pattern Discovery in Web Log Data
There is a considerable body of work on sequence mining of Web Log Data. We
are using One Pass frequent Episode discovery (or FED) algorithm, takes a
different approach than the traditional apriori class of pattern detection
algorithms. In this approach significant intervals for each Website are
computed first (independently) and these interval used for detecting frequent
patterns/Episode and then the Analysis is performed on Significant Intervals
and frequent patterns That can be used to forecast the user's behavior using
previous trends and this can be also used for advertising purpose. This type of
applications predicts the Website interest. In this approach, time-series data
are folded over a periodicity (day, week, etc.) Which are used to form the
Interval? Significant intervals are discovered from these time points that
satisfy the criteria of minimum confidence and maximum interval length
specified by the user.Comment: International Journal of Computer Science Issues, IJCSI, Vol. 7,
Issue 1, No. 3, January 2010,
http://ijcsi.org/articles/Significant-Interval-and-Frequent-Pattern-Discovery-in-Web-Log-Data.ph
Burstiness Scale: a highly parsimonious model for characterizing random series of events
The problem to accurately and parsimoniously characterize random series of
events (RSEs) present in the Web, such as e-mail conversations or Twitter
hashtags, is not trivial. Reports found in the literature reveal two apparent
conflicting visions of how RSEs should be modeled. From one side, the
Poissonian processes, of which consecutive events follow each other at a
relatively regular time and should not be correlated. On the other side, the
self-exciting processes, which are able to generate bursts of correlated events
and periods of inactivities. The existence of many and sometimes conflicting
approaches to model RSEs is a consequence of the unpredictability of the
aggregated dynamics of our individual and routine activities, which sometimes
show simple patterns, but sometimes results in irregular rising and falling
trends. In this paper we propose a highly parsimonious way to characterize
general RSEs, namely the Burstiness Scale (BuSca) model. BuSca views each RSE
as a mix of two independent process: a Poissonian and a self-exciting one. Here
we describe a fast method to extract the two parameters of BuSca that,
together, gives the burstyness scale, which represents how much of the RSE is
due to bursty and viral effects. We validated our method in eight diverse and
large datasets containing real random series of events seen in Twitter, Yelp,
e-mail conversations, Digg, and online forums. Results showed that, even using
only two parameters, BuSca is able to accurately describe RSEs seen in these
diverse systems, what can leverage many applications
Cats & Co: Categorical Time Series Coclustering
We suggest a novel method of clustering and exploratory analysis of temporal
event sequences data (also known as categorical time series) based on
three-dimensional data grid models. A data set of temporal event sequences can
be represented as a data set of three-dimensional points, each point is defined
by three variables: a sequence identifier, a time value and an event value.
Instantiating data grid models to the 3D-points turns the problem into
3D-coclustering.
The sequences are partitioned into clusters, the time variable is discretized
into intervals and the events are partitioned into clusters. The cross-product
of the univariate partitions forms a multivariate partition of the
representation space, i.e., a grid of cells and it also represents a
nonparametric estimator of the joint distribution of the sequences, time and
events dimensions. Thus, the sequences are grouped together because they have
similar joint distribution of time and events, i.e., similar distribution of
events along the time dimension. The best data grid is computed using a
parameter-free Bayesian model selection approach. We also suggest several
criteria for exploiting the resulting grid through agglomerative hierarchies,
for interpreting the clusters of sequences and characterizing their components
through insightful visualizations. Extensive experiments on both synthetic and
real-world data sets demonstrate that data grid models are efficient, effective
and discover meaningful underlying patterns of categorical time series data
Discovering Compressing Serial Episodes from Event Sequences
Most pattern mining methods output a very large number of frequent patterns
and isolating a small but relevant subset is a challenging problem of current
interest in frequent pattern mining. In this paper we consider discovery of a
small set of relevant frequent episodes from data sequences. We make use of the
Minimum Description Length principle to formulate the problem of selecting a
subset of episodes. Using an interesting class of serial episodes with
inter-event constraints and a novel encoding scheme for data using such
episodes, we present algorithms for discovering small set of episodes that
achieve good data compression. Using an example of the data streams obtained
from distributed sensors in a composable coupled conveyor system, we show that
our method is very effective in unearthing highly relevant episodes and that
our scheme also achieves good data compression.Comment: 27 pages 3 figur
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
We aim to mine temporal causal sequences that explain observed events
(consequents) in time-series traces. Causal explanations of key events in a
time-series has applications in design debugging, anomaly detection, planning,
root-cause analysis and many more. We make use of decision trees and interval
arithmetic to mine sequences that explain defining events in the time-series.
We propose modified decision tree construction metrics to handle the
non-determinism introduced by the temporal dimension. The mined sequences are
expressed in a readable temporal logic language that is easy to interpret. The
application of the proposed methodology is illustrated through various
examples.Comment: This article appears in the Journal of Artificial Intelligenc
Water Disaggregation via Shape Features based Bayesian Discriminative Sparse Coding
As the issue of freshwater shortage is increasing daily, it is critical to
take effective measures for water conservation. According to previous studies,
device level consumption could lead to significant freshwater conservation.
Existing water disaggregation methods focus on learning the signatures for
appliances; however, they are lack of the mechanism to accurately discriminate
parallel appliances' consumption. In this paper, we propose a Bayesian
Discriminative Sparse Coding model using Laplace Prior (BDSC-LP) to extensively
enhance the disaggregation performance. To derive discriminative basis
functions, shape features are presented to describe the low-sampling-rate water
consumption patterns. A Gibbs sampling based inference method is designed to
extend the discriminative capability of the disaggregation dictionaries.
Extensive experiments were performed to validate the effectiveness of the
proposed model using both real-world and synthetic datasets.Comment: 20 page
Temporal data mining for root-cause analysis of machine faults in automotive assembly lines
Engine assembly is a complex and heavily automated distributed-control
process, with large amounts of faults data logged everyday. We describe an
application of temporal data mining for analyzing fault logs in an engine
assembly plant. Frequent episode discovery framework is a model-free method
that can be used to deduce (temporal) correlations among events from the logs
in an efficient manner. In addition to being theoretically elegant and
computationally efficient, frequent episodes are also easy to interpret in the
form actionable recommendations. Incorporation of domain-specific information
is critical to successful application of the method for analyzing fault logs in
the manufacturing domain. We show how domain-specific knowledge can be
incorporated using heuristic rules that act as pre-filters and post-filters to
frequent episode discovery. The system described here is currently being used
in one of the engine assembly plants of General Motors and is planned for
adaptation in other plants. To the best of our knowledge, this paper presents
the first real, large-scale application of temporal data mining in the
manufacturing domain. We believe that the ideas presented in this paper can
help practitioners engineer tools for analysis in other similar or related
application domains as well
A framework for event co-occurrence detection in event streams
This paper shows that characterizing co-occurrence between events is an
important but non-trivial and neglected aspect of discovering potential causal
relationships in multimedia event streams. First an introduction to the notion
of event co-occurrence and its relation to co-occurrence pattern detection is
given. Then a finite state automaton extended with a time model and event
parameterization is introduced to convert high level co-occurrence pattern
definition to its corresponding pattern matching automaton. Finally a
processing algorithm is applied to count the occurrence frequency of a
collection of patterns with only one pass through input event streams. The
method proposed in this paper can be used for detecting co-occurrences between
both events of one event stream (Auto co-occurrence), and events from multiple
event streams (Cross co-occurrence). Some fundamental results concerning the
characterization of event co-occurrence are presented in form of a visual co-
occurrence matrix. Reusable causality rules can be extracted easily from
co-occurrence matrix and fed into various analysis tools, such as
recommendation systems and complex event processing systems for further
analysis
Event Identification in Social Networks
Social networks enable users to freely communicate with each other and share
their recent news, ongoing activities or views about different topics. As a
result, they can be seen as a potentially viable source of information to
understand the current emerging topics/events. The ability to model emerging
topics is a substantial step to monitor and summarize the information
originating from social sources. Applying traditional methods for event
detection which are often proposed for processing large, formal and structured
documents, are less effective, due to the short length, noisiness and
informality of the social posts. Recent event detection techniques address
these challenges by exploiting the opportunities behind abundant information
available in social networks. This article provides an overview of the state of
the art in event detection from social networks.Comment: It will appear in Encyclopedia with Semantic Computing to be
published by World Scientifi
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