494 research outputs found
Combined Data Structure for Previous- and Next-Smaller-Values
Let be a static array storing elements from a totally ordered set. We
present a data structure of optimal size at most
bits that allows us to answer the following queries on in constant time,
without accessing : (1) previous smaller value queries, where given an index
, we wish to find the first index to the left of where is strictly
smaller than at , and (2) next smaller value queries, which search to the
right of . As an additional bonus, our data structure also allows to answer
a third kind of query: given indices , find the position of the minimum in
. Our data structure has direct consequences for the space-efficient
storage of suffix trees.Comment: to appear in Theoretical Computer Scienc
LRM-Trees: Compressed Indices, Adaptive Sorting, and Compressed Permutations
LRM-Trees are an elegant way to partition a sequence of values into sorted
consecutive blocks, and to express the relative position of the first element
of each block within a previous block. They were used to encode ordinal trees
and to index integer arrays in order to support range minimum queries on them.
We describe how they yield many other convenient results in a variety of areas,
from data structures to algorithms: some compressed succinct indices for range
minimum queries; a new adaptive sorting algorithm; and a compressed succinct
data structure for permutations supporting direct and indirect application in
time all the shortest as the permutation is compressible.Comment: 13 pages, 1 figur
Linear-Space Data Structures for Range Mode Query in Arrays
A mode of a multiset is an element of maximum multiplicity;
that is, occurs at least as frequently as any other element in . Given a
list of items, we consider the problem of constructing a data
structure that efficiently answers range mode queries on . Each query
consists of an input pair of indices for which a mode of must
be returned. We present an -space static data structure
that supports range mode queries in time in the worst case, for
any fixed . When , this corresponds to
the first linear-space data structure to guarantee query time. We
then describe three additional linear-space data structures that provide
, , and query time, respectively, where denotes the
number of distinct elements in and denotes the frequency of the mode of
. Finally, we examine generalizing our data structures to higher dimensions.Comment: 13 pages, 2 figure
Factorised Representations of Query Results
Query tractability has been traditionally defined as a function of input
database and query sizes, or of both input and output sizes, where the query
result is represented as a bag of tuples. In this report, we introduce a
framework that allows to investigate tractability beyond this setting. The key
insight is that, although the cardinality of a query result can be exponential,
its structure can be very regular and thus factorisable into a nested
representation whose size is only polynomial in the size of both the input
database and query.
For a given query result, there may be several equivalent representations,
and we quantify the regularity of the result by its readability, which is the
minimum over all its representations of the maximum number of occurrences of
any tuple in that representation. We give a characterisation of
select-project-join queries based on the bounds on readability of their results
for any input database. We complement it with an algorithm that can find
asymptotically optimal upper bounds and corresponding factorised
representations.Comment: 44 pages, 13 figure
Succinct Color Searching in One Dimension
In this paper we study succinct data structures for one-dimensional color reporting and color counting problems.
We are given a set of n points with integer coordinates in the range [1,m] and every point is assigned a color from the set {1,...sigma}.
A color reporting query asks for the list of distinct colors that occur in a query interval [a,b] and a color counting query asks for the number of distinct colors in [a,b].
We describe a succinct data structure that answers approximate color counting queries in O(1) time and uses mathcal{B}(n,m) + O(n) + o(mathcal{B}(n,m)) bits,
where mathcal{B}(n,m) is the minimum number of bits required to represent an arbitrary set of size n from a universe of m elements. Thus we show, somewhat counterintuitively,
that it is not necessary to store colors of points in order to answer approximate color counting queries.
In the special case when points are in the rank space (i.e., when n=m), our data structure needs only O(n) bits.
Also, we show that Omega(n) bits are necessary in that case.
Then we turn to succinct data structures for color reporting.
We describe a data structure that uses mathcal{B}(n,m) + nH_d(S) + o(mathcal{B}(n,m)) + o(nlgsigma) bits and answers queries in O(k+1) time,
where k is the number of colors in the answer, and nH_d(S) (d=log_sigma n) is the d-th order empirical entropy of the color sequence. Finally, we consider succinct color reporting under restricted updates. Our dynamic data structure uses nH_d(S)+o(nlgsigma) bits and supports queries in O(k+1) time
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Submodular maximization is a general optimization problem with a wide range
of applications in machine learning (e.g., active learning, clustering, and
feature selection). In large-scale optimization, the parallel running time of
an algorithm is governed by its adaptivity, which measures the number of
sequential rounds needed if the algorithm can execute polynomially-many
independent oracle queries in parallel. While low adaptivity is ideal, it is
not sufficient for an algorithm to be efficient in practice---there are many
applications of distributed submodular optimization where the number of
function evaluations becomes prohibitively expensive. Motivated by these
applications, we study the adaptivity and query complexity of submodular
maximization. In this paper, we give the first constant-factor approximation
algorithm for maximizing a non-monotone submodular function subject to a
cardinality constraint that runs in adaptive rounds and makes
oracle queries in expectation. In our empirical study, we use
three real-world applications to compare our algorithm with several benchmarks
for non-monotone submodular maximization. The results demonstrate that our
algorithm finds competitive solutions using significantly fewer rounds and
queries.Comment: 12 pages, 8 figure
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