164 research outputs found
SKTR: Trace Recovery from Stochastically Known Logs
Developments in machine learning together with the increasing usage of sensor
data challenge the reliance on deterministic logs, requiring new process mining
solutions for uncertain, and in particular stochastically known, logs. In this
work we formulate {trace recovery}, the task of generating a deterministic log
from stochastically known logs that is as faithful to reality as possible. An
effective trace recovery algorithm would be a powerful aid for maintaining
credible process mining tools for uncertain settings. We propose an algorithmic
framework for this task that recovers the best alignment between a
stochastically known log and a process model, with three innovative features.
Our algorithm, SKTR, 1) handles both Markovian and non-Markovian processes; 2)
offers a quality-based balance between a process model and a log, depending on
the available process information, sensor quality, and machine learning
predictiveness power; and 3) offers a novel use of a synchronous product
multigraph to create the log. An empirical analysis using five publicly
available datasets, three of which use predictive models over standard video
capturing benchmarks, shows an average relative accuracy improvement of more
than 10 over a common baseline.Comment: Submitted version -- Accepted to the 5th International Conference on
Process Mining (ICPM), 202
A Dual Framework and Algorithms for Targeted Data Delivery
A variety of emerging wide area applications challenge existing techniques for
data delivery to users and applications accessing data from multiple autonomous
servers. In this paper, we develop a framework for comparing pull based
solutions and present dual optimization approaches. Informally, the first
approach maximizes user utility of profiles while satisfying constraints on the
usage of system resources. The second approach satisfies the utility of user
profiles while minimizing the usage of system resources.
We present a static optimal solution (SUP) for the latter approach and formally
identify sufficient conditions for SUP to be optimal for both.
A shortcoming of static solutions to pull-based delivery is that they cannot
adapt to the dynamic behavior of Web source updates.
Therefore, we present an adaptive algorithm (fbSUP) and show how it can
incorporate feedback to improve user utility with only a moderate increase in
probing. Using real and synthetic data traces, we analyze the behavior of SUP
and fbSUP under various update models
Adaptive Pull-Based Data Freshness Policies for Diverse Update Patterns
An important challenge to effective data delivery in wide area
environments is maintaining the data freshness of objects using
solutions that can scale to a large number of clients without
incurring significant server overhead. Policies for maintaining data
freshness are traditionally either push-based or pull-based.
Push-based policies involve pushing data updates by servers; they may
not scale to a large number of clients. Pull-based policies require
clients to contact servers to check for updates; their effectiveness
is limited by the difficulty of predicting updates. Models to predict
updates generally rely on some knowledge of past updates. Their
accuracy of prediction may vary and determining the most appropriate
model is non-trivial. In this paper, we present an adaptive
pull-based solution to this challenge. We first present several
techniques that use update history to estimate the freshness of cached objects, and identify update patterns for which each technique
is most effective. We then introduce adaptive policies that can
(automatically) choose a policy for an object based on its observed
update patterns. Our proposed policies improve the freshness of
cached data and reduce costly contacts with remote servers
without incurring the large server overhead of push-based
policies, and can scale to a large number of clients. Using trace
data from a data-intensive website as well as two email logs, we show
that our adaptive policies can adapt to diverse update patterns and
provide significant improvement compared to a single policy.
(UMIACS-TR-2004-01
10042 Abstracts Collection -- Semantic Challenges in Sensor Networks
From 24.01. to 29.01.2010, the Dagstuhl Seminar 10042 ``Semantic Challenges in Sensor Networks \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Self-adaptive event recognition for intelligent transport management
Intelligent transport management involves the use of voluminous amounts of uncertain sensor data to identify and effectively manage issues of congestion and quality of service. In particular, urban traffic has been in the eye of the storm for many years now and gathers increasing interest as cities become bigger, crowded, and “smart”. In this work we tackle the issue of uncertainty in transportation systems stream reporting. The variety of existing data sources opens new opportunities for testing the validity of sensor reports and self-adapting the recognition of complex events as a result. We report on the use of a logic-based event reasoning tool to identify regions of uncertainty within a stream and demonstrate our method with a real-world use-case from the city of Dublin. Our empirical analysis shows the feasibility of the approach when dealing with voluminous and highly uncertain streams
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