799 research outputs found
Composition with Target Constraints
It is known that the composition of schema mappings, each specified by
source-to-target tgds (st-tgds), can be specified by a second-order tgd (SO
tgd). We consider the question of what happens when target constraints are
allowed. Specifically, we consider the question of specifying the composition
of standard schema mappings (those specified by st-tgds, target egds, and a
weakly acyclic set of target tgds). We show that SO tgds, even with the
assistance of arbitrary source constraints and target constraints, cannot
specify in general the composition of two standard schema mappings. Therefore,
we introduce source-to-target second-order dependencies (st-SO dependencies),
which are similar to SO tgds, but allow equations in the conclusion. We show
that st-SO dependencies (along with target egds and target tgds) are sufficient
to express the composition of every finite sequence of standard schema
mappings, and further, every st-SO dependency specifies such a composition. In
addition to this expressive power, we show that st-SO dependencies enjoy other
desirable properties. In particular, they have a polynomial-time chase that
generates a universal solution. This universal solution can be used to find the
certain answers to unions of conjunctive queries in polynomial time. It is easy
to show that the composition of an arbitrary number of standard schema mappings
is equivalent to the composition of only two standard schema mappings. We show
that surprisingly, the analogous result holds also for schema mappings
specified by just st-tgds (no target constraints). This is proven by showing
that every SO tgd is equivalent to an unnested SO tgd (one where there is no
nesting of function symbols). Similarly, we prove unnesting results for st-SO
dependencies, with the same types of consequences.Comment: This paper is an extended version of: M. Arenas, R. Fagin, and A.
Nash. Composition with Target Constraints. In 13th International Conference
on Database Theory (ICDT), pages 129-142, 201
Probabilistic Algorithmic Knowledge
The framework of algorithmic knowledge assumes that agents use deterministic
knowledge algorithms to compute the facts they explicitly know. We extend the
framework to allow for randomized knowledge algorithms. We then characterize
the information provided by a randomized knowledge algorithm when its answers
have some probability of being incorrect. We formalize this information in
terms of evidence; a randomized knowledge algorithm returning ``Yes'' to a
query about a fact \phi provides evidence for \phi being true. Finally, we
discuss the extent to which this evidence can be used as a basis for decisions.Comment: 26 pages. A preliminary version appeared in Proc. 9th Conference on
Theoretical Aspects of Rationality and Knowledge (TARK'03
Performance Evaluation and Optimization of Math-Similarity Search
Similarity search in math is to find mathematical expressions that are
similar to a user's query. We conceptualized the similarity factors between
mathematical expressions, and proposed an approach to math similarity search
(MSS) by defining metrics based on those similarity factors [11]. Our
preliminary implementation indicated the advantage of MSS compared to
non-similarity based search. In order to more effectively and efficiently
search similar math expressions, MSS is further optimized. This paper focuses
on performance evaluation and optimization of MSS. Our results show that the
proposed optimization process significantly improved the performance of MSS
with respect to both relevance ranking and recall.Comment: 15 pages, 8 figure
On the k-Boundedness for Existential Rules
The chase is a fundamental tool for existential rules. Several chase variants
are known, which differ on how they handle redundancies possibly caused by the
introduction of nulls. Given a chase variant, the halting problem takes as
input a set of existential rules and asks if this set of rules ensures the
termination of the chase for any factbase. It is well-known that this problem
is undecidable for all known chase variants. The related problem of boundedness
asks if a given set of existential rules is bounded, i.e., whether there is a
predefined upper bound on the number of (breadth-first) steps of the chase,
independently from any factbase. This problem is already undecidable in the
specific case of datalog rules. However, knowing that a set of rules is bounded
for some chase variant does not help much in practice if the bound is unknown.
Hence, in this paper, we investigate the decidability of the k-boundedness
problem, which asks whether a given set of rules is bounded by an integer k. We
prove that k-boundedness is decidable for three chase variants, namely the
oblivious, semi-oblivious and restricted chase.Comment: 20 pages, revised version of the paper published at RuleML+RR 201
A recommender system for process discovery
Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft
Randomisation and Derandomisation in Descriptive Complexity Theory
We study probabilistic complexity classes and questions of derandomisation
from a logical point of view. For each logic L we introduce a new logic BPL,
bounded error probabilistic L, which is defined from L in a similar way as the
complexity class BPP, bounded error probabilistic polynomial time, is defined
from PTIME. Our main focus lies on questions of derandomisation, and we prove
that there is a query which is definable in BPFO, the probabilistic version of
first-order logic, but not in Cinf, finite variable infinitary logic with
counting. This implies that many of the standard logics of finite model theory,
like transitive closure logic and fixed-point logic, both with and without
counting, cannot be derandomised. Similarly, we present a query on ordered
structures which is definable in BPFO but not in monadic second-order logic,
and a query on additive structures which is definable in BPFO but not in FO.
The latter of these queries shows that certain uniform variants of AC0
(bounded-depth polynomial sized circuits) cannot be derandomised. These results
are in contrast to the general belief that most standard complexity classes can
be derandomised. Finally, we note that BPIFP+C, the probabilistic version of
fixed-point logic with counting, captures the complexity class BPP, even on
unordered structures
Effective representation of RT-LOTOS terms by finite time petri nets
The paper describes a transformational approach for the
specification and formal verification of concurrent and real-time systems. At upper level, one system is specified using the timed process algebra RT-LOTOS. The output of the proposed transformation is a Time Petri net (TPN). The paper particularly shows how a TPN can be automatically constructed from an RT-LOTOS specification using a compositionally defined mapping. The proof of the translation consistency is sketched in the paper and developed in [1]. The RT-LOTOS to TPN translation patterns formalized in the paper are being implemented. in a prototype tool. This enables reusing TPNs verification techniques and tools for the profit of RT-LOTOS
Reasoning with global assumptions in arithmetic modal logics
We establish a generic upper bound ExpTime for reasoning with global assumptions in coalgebraic modal logics. Unlike earlier results of this kind, we do not require a tractable set of tableau rules for the in- stance logics, so that the result applies to wider classes of logics. Examples are Presburger modal logic, which extends graded modal logic with linear inequalities over numbers of successors, and probabilistic modal logic with polynomial inequalities over probabilities. We establish the theoretical upper bound using a type elimination algorithm. We also provide a global caching algorithm that offers potential for practical reasoning
An Optimal Self-Stabilizing Firing Squad
Consider a fully connected network where up to processes may crash, and
all processes start in an arbitrary memory state. The self-stabilizing firing
squad problem consists of eventually guaranteeing simultaneous response to an
external input. This is modeled by requiring that the non-crashed processes
"fire" simultaneously if some correct process received an external "GO" input,
and that they only fire as a response to some process receiving such an input.
This paper presents FireAlg, the first self-stabilizing firing squad algorithm.
The FireAlg algorithm is optimal in two respects: (a) Once the algorithm is
in a safe state, it fires in response to a GO input as fast as any other
algorithm does, and (b) Starting from an arbitrary state, it converges to a
safe state as fast as any other algorithm does.Comment: Shorter version to appear in SSS0
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