11,738 research outputs found
CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling
In this paper, we present a compressed data structure for moving object
trajectories in a road network, which are represented as sequences of road
edges. Unlike existing compression methods for trajectories in a network, our
method supports pattern matching and decompression from an arbitrary position
while retaining a high compressibility with theoretical guarantees.
Specifically, our method is based on FM-index, a fast and compact data
structure for pattern matching. To enhance the compression, we incorporate the
sparsity of road networks into the data structure. In particular, we present
the novel concepts of relative movement labeling and PseudoRank, each
contributing to significant reductions in data size and query processing time.
Our theoretical analysis and experimental studies reveal the advantages of our
proposed method as compared to existing trajectory compression methods and
FM-index variants
An optimized TOPS+ comparison method for enhanced TOPS models
This article has been made available through the Brunel Open Access Publishing Fund.Background
Although methods based on highly abstract descriptions of protein structures, such as VAST and TOPS, can perform very fast protein structure comparison, the results can lack a high degree of biological significance. Previously we have discussed the basic mechanisms of our novel method for structure comparison based on our TOPS+ model (Topological descriptions of Protein Structures Enhanced with Ligand Information). In this paper we show how these results can be significantly improved using parameter optimization, and we call the resulting optimised TOPS+ method as advanced TOPS+ comparison method i.e. advTOPS+.
Results
We have developed a TOPS+ string model as an improvement to the TOPS [1-3] graph model by considering loops as secondary structure elements (SSEs) in addition to helices and strands, representing ligands as first class objects, and describing interactions between SSEs, and SSEs and ligands, by incoming and outgoing arcs, annotating SSEs with the interaction direction and type. Benchmarking results of an all-against-all pairwise comparison using a large dataset of 2,620 non-redundant structures from the PDB40 dataset [4] demonstrate the biological significance, in terms of SCOP classification at the superfamily level, of our TOPS+ comparison method.
Conclusions
Our advanced TOPS+ comparison shows better performance on the PDB40 dataset [4] compared to our basic TOPS+ method, giving 90 percent accuracy for SCOP alpha+beta; a 6 percent increase in accuracy compared to the TOPS and basic TOPS+ methods. It also outperforms the TOPS, basic TOPS+ and SSAP comparison methods on the Chew-Kedem dataset [5], achieving 98 percent accuracy. Software Availability: The TOPS+ comparison server is available at http://balabio.dcs.gla.ac.uk/mallika/WebTOPS/.This article is available through the Brunel Open Access Publishing Fun
Faster subsequence recognition in compressed strings
Computation on compressed strings is one of the key approaches to processing
massive data sets. We consider local subsequence recognition problems on
strings compressed by straight-line programs (SLP), which is closely related to
Lempel--Ziv compression. For an SLP-compressed text of length , and an
uncompressed pattern of length , C{\'e}gielski et al. gave an algorithm for
local subsequence recognition running in time . We improve
the running time to . Our algorithm can also be used to
compute the longest common subsequence between a compressed text and an
uncompressed pattern in time ; the same problem with a
compressed pattern is known to be NP-hard
Fast Searching in Packed Strings
Given strings and the (exact) string matching problem is to find all
positions of substrings in matching . The classical Knuth-Morris-Pratt
algorithm [SIAM J. Comput., 1977] solves the string matching problem in linear
time which is optimal if we can only read one character at the time. However,
most strings are stored in a computer in a packed representation with several
characters in a single word, giving us the opportunity to read multiple
characters simultaneously. In this paper we study the worst-case complexity of
string matching on strings given in packed representation. Let be
the lengths and , respectively, and let denote the size of the
alphabet. On a standard unit-cost word-RAM with logarithmic word size we
present an algorithm using time O\left(\frac{n}{\log_\sigma n} + m +
\occ\right). Here \occ is the number of occurrences of in . For this improves the bound of the Knuth-Morris-Pratt algorithm.
Furthermore, if our algorithm is optimal since any
algorithm must spend at least \Omega(\frac{(n+m)\log
\sigma}{\log n} + \occ) = \Omega(\frac{n}{\log_\sigma n} + \occ) time to
read the input and report all occurrences. The result is obtained by a novel
automaton construction based on the Knuth-Morris-Pratt algorithm combined with
a new compact representation of subautomata allowing an optimal
tabulation-based simulation.Comment: To appear in Journal of Discrete Algorithms. Special Issue on CPM
200
Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity
The relationship between the Bayesian approach and the minimum description
length approach is established. We sharpen and clarify the general modeling
principles MDL and MML, abstracted as the ideal MDL principle and defined from
Bayes's rule by means of Kolmogorov complexity. The basic condition under which
the ideal principle should be applied is encapsulated as the Fundamental
Inequality, which in broad terms states that the principle is valid when the
data are random, relative to every contemplated hypothesis and also these
hypotheses are random relative to the (universal) prior. Basically, the ideal
principle states that the prior probability associated with the hypothesis
should be given by the algorithmic universal probability, and the sum of the
log universal probability of the model plus the log of the probability of the
data given the model should be minimized. If we restrict the model class to the
finite sets then application of the ideal principle turns into Kolmogorov's
minimal sufficient statistic. In general we show that data compression is
almost always the best strategy, both in hypothesis identification and
prediction.Comment: 35 pages, Latex. Submitted IEEE Trans. Inform. Theor
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