62,578 research outputs found
Analysing Temporal Relations – Beyond Windows, Frames and Predicates
This article proposes an approach to rely on the standard
operators of relational algebra (including grouping and ag-
gregation) for processing complex event without requiring
window specifications. In this way the approach can pro-
cess complex event queries of the kind encountered in appli-
cations such as emergency management in metro networks.
This article presents Temporal Stream Algebra (TSA) which
combines the operators of relational algebra with an analy-
sis of temporal relations at compile time. This analysis de-
termines which relational algebra queries can be evaluated
against data streams, i. e. the analysis is able to distinguish
valid from invalid stream queries. Furthermore the analysis
derives functions similar to the pass, propagation and keep
invariants in Tucker's et al. \Exploiting Punctuation Seman-
tics in Continuous Data Streams". These functions enable
the incremental evaluation of TSA queries, the propagation
of punctuations, and garbage collection. The evaluation of
TSA queries combines bulk-wise and out-of-order processing
which makes it tolerant to workload bursts as they typically
occur in emergency management. The approach has been
conceived for efficiently processing complex event queries on
top of a relational database system. It has been deployed
and tested on MonetDB
Temporal Stream Algebra
Data stream management systems (DSMS) so far focus on
event queries and hardly consider combined queries to both
data from event streams and from a database. However,
applications like emergency management require combined
data stream and database queries. Further requirements are
the simultaneous use of multiple timestamps after different
time lines and semantics, expressive temporal relations between multiple time-stamps and
exible negation, grouping
and aggregation which can be controlled, i. e. started and
stopped, by events and are not limited to fixed-size time
windows. Current DSMS hardly address these requirements.
This article proposes Temporal Stream Algebra (TSA) so
as to meet the afore mentioned requirements. Temporal
streams are a common abstraction of data streams and data-
base relations; the operators of TSA are generalizations of
the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally.
In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent
blocking expressions on a "syntactical" level
Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees
Sampling efficiency in a highly constrained environment has long been a major
challenge for sampling-based planners. In this work, we propose
Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal
multi-query planner. RRdT* uses multiple disjointed-trees to exploit
local-connectivity of spaces via Markov Chain random sampling, which utilises
neighbourhood information derived from previous successful and failed samples.
To balance local exploitation, RRdT* actively explore unseen global spaces when
local-connectivity exploitation is unsuccessful. The active trade-off between
local exploitation and global exploration is formulated as a multi-armed bandit
problem. We argue that the active balancing of global exploration and local
exploitation is the key to improving sample efficient in sampling-based motion
planners. We provide rigorous proofs of completeness and optimal convergence
for this novel approach. Furthermore, we demonstrate experimentally the
effectiveness of RRdT*'s locally exploring trees in granting improved
visibility for planning. Consequently, RRdT* outperforms existing
state-of-the-art incremental planners, especially in highly constrained
environments.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Practical and Efficient Split Decomposition via Graph-Labelled Trees
Split decomposition of graphs was introduced by Cunningham (under the name
join decomposition) as a generalization of the modular decomposition. This
paper undertakes an investigation into the algorithmic properties of split
decomposition. We do so in the context of graph-labelled trees (GLTs), a new
combinatorial object designed to simplify its consideration. GLTs are used to
derive an incremental characterization of split decomposition, with a simple
combinatorial description, and to explore its properties with respect to
Lexicographic Breadth-First Search (LBFS). Applying the incremental
characterization to an LBFS ordering results in a split decomposition algorithm
that runs in time , where is the inverse Ackermann
function, whose value is smaller than 4 for any practical graph. Compared to
Dahlhaus' linear-time split decomposition algorithm [Dahlhaus'00], which does
not rely on an incremental construction, our algorithm is just as fast in all
but the asymptotic sense and full implementation details are given in this
paper. Also, our algorithm extends to circle graph recognition, whereas no such
extension is known for Dahlhaus' algorithm. The companion paper [Gioan et al.]
uses our algorithm to derive the first sub-quadratic circle graph recognition
algorithm
Partition strategies for incremental Mini-Bucket
Los modelos en grafo probabilÃsticos, tales como los campos aleatorios de
Markov y las redes bayesianas, ofrecen poderosos marcos de trabajo para la
representación de conocimiento y el razonamiento en modelos con gran número
de variables. Sin embargo, los problemas de inferencia exacta en modelos de
grafos son NP-hard en general, lo que ha causado que se produzca bastante
interés en métodos de inferencia aproximados.
El mini-bucket incremental es un marco de trabajo para inferencia aproximada
que produce como resultado lÃmites aproximados inferior y superior de la
función de partición exacta, a base de -empezando a partir de un modelo con
todos los constraints relajados, es decir, con las regiones más pequeñas posibleincrementalmente
añadir regiones más grandes a la aproximación. Los métodos
de inferencia aproximada que existen actualmente producen lÃmites superiores
ajustados de la función de partición, pero los lÃmites inferiores suelen ser demasiado
imprecisos o incluso triviales.
El objetivo de este proyecto es investigar estrategias de partición que mejoren
los lÃmites inferiores obtenidos con el algoritmo de mini-bucket, trabajando dentro
del marco de trabajo de mini-bucket incremental.
Empezamos a partir de la idea de que creemos que deberÃa ser beneficioso
razonar conjuntamente con las variables de un modelo que tienen una alta correlación,
y desarrollamos una estrategia para la selección de regiones basada en
esa idea. Posteriormente, implementamos nuestra estrategia y exploramos formas
de mejorarla, y finalmente medimos los resultados obtenidos usando nuestra
estrategia y los comparamos con varios métodos de referencia.
Nuestros resultados indican que nuestra estrategia obtiene lÃmites inferiores
más ajustados que nuestros dos métodos de referencia. También consideramos
y descartamos dos posibles hipótesis que podrÃan explicar esta mejora.Els models en graf probabilÃstics, com bé els camps aleatoris de Markov i les
xarxes bayesianes, ofereixen poderosos marcs de treball per la representació
del coneixement i el raonament en models amb grans quantitats de variables.
Tanmateix, els problemes d’inferència exacta en models de grafs son NP-hard
en general, el qual ha provocat que es produeixi bastant d’interès en mètodes
d’inferència aproximats.
El mini-bucket incremental es un marc de treball per a l’inferència aproximada
que produeix com a resultat lÃmits aproximats inferior i superior de la
funció de partició exacta que funciona començant a partir d’un model al qual
se li han relaxat tots els constraints -és a dir, un model amb les regions més
petites possibles- i anar afegint a l’aproximació regions incrementalment més
grans. Els mètodes d’inferència aproximada que existeixen actualment produeixen
lÃmits superiors ajustats de la funció de partició. Tanmateix, els lÃmits
inferiors acostumen a ser massa imprecisos o fins aviat trivials.
El objectiu d’aquest projecte es recercar estratègies de partició que millorin
els lÃmits inferiors obtinguts amb l’algorisme de mini-bucket, treballant dins del
marc de treball del mini-bucket incremental.
La nostra idea de partida pel projecte es que creiem que hauria de ser beneficiós
per la qualitat de l’aproximació raonar conjuntament amb les variables del
model que tenen una alta correlació entre elles, i desenvolupem una estratègia
per a la selecció de regions basada en aquesta idea. Posteriorment, implementem
la nostra estratègia i explorem formes de millorar-la, i finalment mesurem els
resultats obtinguts amb la nostra estratègia i els comparem a diversos mètodes
de referència.
Els nostres resultats indiquen que la nostra estratègia obté lÃmits inferiors
més ajustats que els nostres dos mètodes de referència. També considerem i
descartem dues possibles hipòtesis que podrien explicar aquesta millora.Probabilistic graphical models such as Markov random fields and Bayesian networks
provide powerful frameworks for knowledge representation and reasoning
over models with large numbers of variables. Unfortunately, exact inference
problems on graphical models are generally NP-hard, which has led to signifi-
cant interest in approximate inference algorithms.
Incremental mini-bucket is a framework for approximate inference that provides
upper and lower bounds on the exact partition function by, starting from
a model with completely relaxed constraints, i.e. with the smallest possible
regions, incrementally adding larger regions to the approximation. Current
approximate inference algorithms provide tight upper bounds on the exact partition
function but loose or trivial lower bounds.
This project focuses on researching partitioning strategies that improve the
lower bounds obtained with mini-bucket elimination, working within the framework
of incremental mini-bucket.
We start from the idea that variables that are highly correlated should be
reasoned about together, and we develop a strategy for region selection based
on that idea. We implement the strategy and explore ways to improve it, and
finally we measure the results obtained using the strategy and compare them to
several baselines.
We find that our strategy performs better than both of our baselines. We
also rule out several possible explanations for the improvement
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