122,232 research outputs found
String Indexing for Patterns with Wildcards
We consider the problem of indexing a string of length to report the
occurrences of a query pattern containing characters and wildcards.
Let be the number of occurrences of in , and the size of
the alphabet. We obtain the following results.
- A linear space index with query time .
This significantly improves the previously best known linear space index by Lam
et al. [ISAAC 2007], which requires query time in the worst case.
- An index with query time using space , where is the maximum number of wildcards allowed in the pattern.
This is the first non-trivial bound with this query time.
- A time-space trade-off, generalizing the index by Cole et al. [STOC 2004].
We also show that these indexes can be generalized to allow variable length
gaps in the pattern. Our results are obtained using a novel combination of
well-known and new techniques, which could be of independent interest
Efficient Subgraph Matching on Billion Node Graphs
The ability to handle large scale graph data is crucial to an increasing
number of applications. Much work has been dedicated to supporting basic graph
operations such as subgraph matching, reachability, regular expression
matching, etc. In many cases, graph indices are employed to speed up query
processing. Typically, most indices require either super-linear indexing time
or super-linear indexing space. Unfortunately, for very large graphs,
super-linear approaches are almost always infeasible. In this paper, we study
the problem of subgraph matching on billion-node graphs. We present a novel
algorithm that supports efficient subgraph matching for graphs deployed on a
distributed memory store. Instead of relying on super-linear indices, we use
efficient graph exploration and massive parallel computing for query
processing. Our experimental results demonstrate the feasibility of performing
subgraph matching on web-scale graph data.Comment: VLDB201
Binary Jumbled String Matching for Highly Run-Length Compressible Texts
The Binary Jumbled String Matching problem is defined as: Given a string
over of length and a query , with non-negative
integers, decide whether has a substring with exactly 's and
's. Previous solutions created an index of size O(n) in a pre-processing
step, which was then used to answer queries in constant time. The fastest
algorithms for construction of this index have running time
[Burcsi et al., FUN 2010; Moosa and Rahman, IPL 2010], or in
the word-RAM model [Moosa and Rahman, JDA 2012]. We propose an index
constructed directly from the run-length encoding of . The construction time
of our index is , where O(n) is the time for computing
the run-length encoding of and is the length of this encoding---this
is no worse than previous solutions if and better if . Our index can be queried in time. While
in the worst case, preliminary investigations have
indicated that may often be close to . Furthermore, the algorithm
for constructing the index is conceptually simple and easy to implement. In an
attempt to shed light on the structure and size of our index, we characterize
it in terms of the prefix normal forms of introduced in [Fici and Lipt\'ak,
DLT 2011].Comment: v2: only small cosmetic changes; v3: new title, weakened conjectures
on size of Corner Index (we no longer conjecture it to be always linear in
size of RLE); removed experimental part on random strings (these are valid
but limited in their predictive power w.r.t. general strings); v3 published
in IP
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