40,593 research outputs found
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201
Bloom Filters in Adversarial Environments
Many efficient data structures use randomness, allowing them to improve upon
deterministic ones. Usually, their efficiency and correctness are analyzed
using probabilistic tools under the assumption that the inputs and queries are
independent of the internal randomness of the data structure. In this work, we
consider data structures in a more robust model, which we call the adversarial
model. Roughly speaking, this model allows an adversary to choose inputs and
queries adaptively according to previous responses. Specifically, we consider a
data structure known as "Bloom filter" and prove a tight connection between
Bloom filters in this model and cryptography.
A Bloom filter represents a set of elements approximately, by using fewer
bits than a precise representation. The price for succinctness is allowing some
errors: for any it should always answer `Yes', and for any it should answer `Yes' only with small probability.
In the adversarial model, we consider both efficient adversaries (that run in
polynomial time) and computationally unbounded adversaries that are only
bounded in the number of queries they can make. For computationally bounded
adversaries, we show that non-trivial (memory-wise) Bloom filters exist if and
only if one-way functions exist. For unbounded adversaries we show that there
exists a Bloom filter for sets of size and error , that is
secure against queries and uses only
bits of memory. In comparison, is the best
possible under a non-adaptive adversary
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
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