11,340 research outputs found
Query Stability in Monotonic Data-Aware Business Processes [Extended Version]
Organizations continuously accumulate data, often according to some business
processes. If one poses a query over such data for decision support, it is
important to know whether the query is stable, that is, whether the answers
will stay the same or may change in the future because business processes may
add further data. We investigate query stability for conjunctive queries. To
this end, we define a formalism that combines an explicit representation of the
control flow of a process with a specification of how data is read and inserted
into the database. We consider different restrictions of the process model and
the state of the system, such as negation in conditions, cyclic executions,
read access to written data, presence of pending process instances, and the
possibility to start fresh process instances. We identify for which facet
combinations stability of conjunctive queries is decidable and provide
encodings into variants of Datalog that are optimal with respect to the
worst-case complexity of the problem.Comment: This report is the extended version of a paper accepted at the 19th
International Conference on Database Theory (ICDT 2016), March 15-18, 2016 -
Bordeaux, Franc
Bounded LTL Model Checking with Stable Models
In this paper bounded model checking of asynchronous concurrent systems is
introduced as a promising application area for answer set programming. As the
model of asynchronous systems a generalisation of communicating automata,
1-safe Petri nets, are used. It is shown how a 1-safe Petri net and a
requirement on the behaviour of the net can be translated into a logic program
such that the bounded model checking problem for the net can be solved by
computing stable models of the corresponding program. The use of the stable
model semantics leads to compact encodings of bounded reachability and deadlock
detection tasks as well as the more general problem of bounded model checking
of linear temporal logic. Correctness proofs of the devised translations are
given, and some experimental results using the translation and the Smodels
system are presented.Comment: 32 pages, to appear in Theory and Practice of Logic Programmin
Partial Orders for Efficient BMC of Concurrent Software
This version previously deposited at arXiv:1301.1629v1 [cs.LO]The vast number of interleavings that a concurrent program can have is typically identified as the root cause of the difficulty of automatic analysis of concurrent software. Weak memory is generally believed to make this problem even harder. We address both issues by modelling programs' executions with partial orders rather than the interleaving semantics (SC). We implemented a software analysis tool based on these ideas. It scales to programs of sufficient size to achieve first-time formal verification of non-trivial concurrent systems code over a wide range of models, including SC, Intel x86 and IBM Power
Constructing Conditional Plans by a Theorem-Prover
The research on conditional planning rejects the assumptions that there is no
uncertainty or incompleteness of knowledge with respect to the state and
changes of the system the plans operate on. Without these assumptions the
sequences of operations that achieve the goals depend on the initial state and
the outcomes of nondeterministic changes in the system. This setting raises the
questions of how to represent the plans and how to perform plan search. The
answers are quite different from those in the simpler classical framework. In
this paper, we approach conditional planning from a new viewpoint that is
motivated by the use of satisfiability algorithms in classical planning.
Translating conditional planning to formulae in the propositional logic is not
feasible because of inherent computational limitations. Instead, we translate
conditional planning to quantified Boolean formulae. We discuss three
formalizations of conditional planning as quantified Boolean formulae, and
present experimental results obtained with a theorem-prover
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Automated generation of computationally hard feature models using evolutionary algorithms
This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and
the Andalusian Government
New Classes of Distributed Time Complexity
A number of recent papers -- e.g. Brandt et al. (STOC 2016), Chang et al.
(FOCS 2016), Ghaffari & Su (SODA 2017), Brandt et al. (PODC 2017), and Chang &
Pettie (FOCS 2017) -- have advanced our understanding of one of the most
fundamental questions in theory of distributed computing: what are the possible
time complexity classes of LCL problems in the LOCAL model? In essence, we have
a graph problem in which a solution can be verified by checking all
radius- neighbourhoods, and the question is what is the smallest such
that a solution can be computed so that each node chooses its own output based
on its radius- neighbourhood. Here is the distributed time complexity of
.
The time complexity classes for deterministic algorithms in bounded-degree
graphs that are known to exist by prior work are , , , , and . It is also known
that there are two gaps: one between and , and
another between and . It has been conjectured
that many more gaps exist, and that the overall time hierarchy is relatively
simple -- indeed, this is known to be the case in restricted graph families
such as cycles and grids.
We show that the picture is much more diverse than previously expected. We
present a general technique for engineering LCL problems with numerous
different deterministic time complexities, including
for any , for any , and
for any in the high end of the complexity
spectrum, and for any ,
for any , and
for any in the low end; here
is a positive rational number
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