9,108 research outputs found
Logical Algorithms meets CHR: A meta-complexity result for Constraint Handling Rules with rule priorities
This paper investigates the relationship between the Logical Algorithms
language (LA) of Ganzinger and McAllester and Constraint Handling Rules (CHR).
We present a translation schema from LA to CHR-rp: CHR with rule priorities,
and show that the meta-complexity theorem for LA can be applied to a subset of
CHR-rp via inverse translation. Inspired by the high-level implementation
proposal for Logical Algorithm by Ganzinger and McAllester and based on a new
scheduling algorithm, we propose an alternative implementation for CHR-rp that
gives strong complexity guarantees and results in a new and accurate
meta-complexity theorem for CHR-rp. It is furthermore shown that the
translation from Logical Algorithms to CHR-rp combined with the new CHR-rp
implementation, satisfies the required complexity for the Logical Algorithms
meta-complexity result to hold.Comment: To appear in Theory and Practice of Logic Programming (TPLP
Reordering Rows for Better Compression: Beyond the Lexicographic Order
Sorting database tables before compressing them improves the compression
rate. Can we do better than the lexicographical order? For minimizing the
number of runs in a run-length encoding compression scheme, the best approaches
to row-ordering are derived from traveling salesman heuristics, although there
is a significant trade-off between running time and compression. A new
heuristic, Multiple Lists, which is a variant on Nearest Neighbor that trades
off compression for a major running-time speedup, is a good option for very
large tables. However, for some compression schemes, it is more important to
generate long runs rather than few runs. For this case, another novel
heuristic, Vortex, is promising. We find that we can improve run-length
encoding up to a factor of 3 whereas we can improve prefix coding by up to 80%:
these gains are on top of the gains due to lexicographically sorting the table.
We prove that the new row reordering is optimal (within 10%) at minimizing the
runs of identical values within columns, in a few cases.Comment: to appear in ACM TOD
Dynamic Ordered Sets with Exponential Search Trees
We introduce exponential search trees as a novel technique for converting
static polynomial space search structures for ordered sets into fully-dynamic
linear space data structures.
This leads to an optimal bound of O(sqrt(log n/loglog n)) for searching and
updating a dynamic set of n integer keys in linear space. Here searching an
integer y means finding the maximum key in the set which is smaller than or
equal to y. This problem is equivalent to the standard text book problem of
maintaining an ordered set (see, e.g., Cormen, Leiserson, Rivest, and Stein:
Introduction to Algorithms, 2nd ed., MIT Press, 2001).
The best previous deterministic linear space bound was O(log n/loglog n) due
Fredman and Willard from STOC 1990. No better deterministic search bound was
known using polynomial space.
We also get the following worst-case linear space trade-offs between the
number n, the word length w, and the maximal key U < 2^w: O(min{loglog n+log
n/log w, (loglog n)(loglog U)/(logloglog U)}). These trade-offs are, however,
not likely to be optimal.
Our results are generalized to finger searching and string searching,
providing optimal results for both in terms of n.Comment: Revision corrects some typoes and state things better for
applications in subsequent paper
Substring filtering for low-cost linked data interfaces
Recently, Triple Pattern Fragments (TPFS) were introduced as a low-cost server-side interface when high numbers of clients need to evaluate SPARQL queries. Scalability is achieved by moving part of the query execution to the client, at the cost of elevated query times. Since the TPFS interface purposely does not support complex constructs such as SPARQL filters, queries that use them need to be executed mostly on the client, resulting in long execution times. We therefore investigated the impact of adding a literal substring matching feature to the TPFS interface, with the goal of improving query performance while maintaining low server cost. In this paper, we discuss the client/server setup and compare the performance of SPARQL queries on multiple implementations, including Elastic Search and case-insensitive FM-index. Our evaluations indicate that these improvements allow for faster query execution without significantly increasing the load on the server. Offering the substring feature on TPF servers allows users to obtain faster responses for filter-based SPARQL queries. Furthermore, substring matching can be used to support other filters such as complete regular expressions or range queries
On the Necessary Memory to Compute the Plurality in Multi-Agent Systems
We consider the Relative-Majority Problem (also known as Plurality), in
which, given a multi-agent system where each agent is initially provided an
input value out of a set of possible ones, each agent is required to
eventually compute the input value with the highest frequency in the initial
configuration. We consider the problem in the general Population Protocols
model in which, given an underlying undirected connected graph whose nodes
represent the agents, edges are selected by a globally fair scheduler.
The state complexity that is required for solving the Plurality Problem
(i.e., the minimum number of memory states that each agent needs to have in
order to solve the problem), has been a long-standing open problem. The best
protocol so far for the general multi-valued case requires polynomial memory:
Salehkaleybar et al. (2015) devised a protocol that solves the problem by
employing states per agent, and they conjectured their upper bound
to be optimal. On the other hand, under the strong assumption that agents
initially agree on a total ordering of the initial input values, Gasieniec et
al. (2017), provided an elegant logarithmic-memory plurality protocol.
In this work, we refute Salehkaleybar et al.'s conjecture, by providing a
plurality protocol which employs states per agent. Central to our
result is an ordering protocol which allows to leverage on the plurality
protocol by Gasieniec et al., of independent interest. We also provide a
-state lower bound on the necessary memory to solve the problem,
proving that the Plurality Problem cannot be solved within the mere memory
necessary to encode the output.Comment: 14 pages, accepted at CIAC 201
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