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THE EVOLVING PHILOSOPHERS PROBLEM - DYNAMIC CHANGE MANAGEMENT
Published versio
Heuristics Miners for Streaming Event Data
More and more business activities are performed using information systems.
These systems produce such huge amounts of event data that existing systems are
unable to store and process them. Moreover, few processes are in steady-state
and due to changing circumstances processes evolve and systems need to adapt
continuously. Since conventional process discovery algorithms have been defined
for batch processing, it is difficult to apply them in such evolving
environments. Existing algorithms cannot cope with streaming event data and
tend to generate unreliable and obsolete results.
In this paper, we discuss the peculiarities of dealing with streaming event
data in the context of process mining. Subsequently, we present a general
framework for defining process mining algorithms in settings where it is
impossible to store all events over an extended period or where processes
evolve while being analyzed. We show how the Heuristics Miner, one of the most
effective process discovery algorithms for practical applications, can be
modified using this framework. Different stream-aware versions of the
Heuristics Miner are defined and implemented in ProM. Moreover, experimental
results on artificial and real logs are reported
The Evolutionary Unfolding of Complexity
We analyze the population dynamics of a broad class of fitness functions that
exhibit epochal evolution---a dynamical behavior, commonly observed in both
natural and artificial evolutionary processes, in which long periods of stasis
in an evolving population are punctuated by sudden bursts of change. Our
approach---statistical dynamics---combines methods from both statistical
mechanics and dynamical systems theory in a way that offers an alternative to
current ``landscape'' models of evolutionary optimization. We describe the
population dynamics on the macroscopic level of fitness classes or phenotype
subbasins, while averaging out the genotypic variation that is consistent with
a macroscopic state. Metastability in epochal evolution occurs solely at the
macroscopic level of the fitness distribution. While a balance between
selection and mutation maintains a quasistationary distribution of fitness,
individuals diffuse randomly through selectively neutral subbasins in genotype
space. Sudden innovations occur when, through this diffusion, a genotypic
portal is discovered that connects to a new subbasin of higher fitness
genotypes. In this way, we identify innovations with the unfolding and
stabilization of a new dimension in the macroscopic state space. The
architectural view of subbasins and portals in genotype space clarifies how
frozen accidents and the resulting phenotypic constraints guide the evolution
to higher complexity.Comment: 28 pages, 5 figure
Fast Hardware Implementations of Static P Systems
In this article we present a simulator of non-deterministic static P systems
using Field Programmable Gate Array (FPGA) technology. Its major feature
is a high performance, achieving a constant processing time for each transition. Our
approach is based on representing all possible applications as words of some regular
context-free language. Then, using formal power series it is possible to obtain the
number of possibilities and select one of them following a uniform distribution, in
a fair and non-deterministic way. According to these ideas, we yield an implementation
whose results show an important speed-up, with a strong independence from
the size of the P system.Ministry of Science and Innovation of the Spanish Government under the project TEC2011-27936 (HIPERSYS)European Regional Development Fund (ERDF)Ministry of Education of Spain (FPU grant AP2009-3625)ANR project SynBioTI
Parameterized Verification of Algorithms for Oblivious Robots on a Ring
We study verification problems for autonomous swarms of mobile robots that
self-organize and cooperate to solve global objectives. In particular, we focus
in this paper on the model proposed by Suzuki and Yamashita of anonymous robots
evolving in a discrete space with a finite number of locations (here, a ring).
A large number of algorithms have been proposed working for rings whose size is
not a priori fixed and can be hence considered as a parameter. Handmade
correctness proofs of these algorithms have been shown to be error-prone, and
recent attention had been given to the application of formal methods to
automatically prove those. Our work is the first to study the verification
problem of such algorithms in the parameter-ized case. We show that safety and
reachability problems are undecidable for robots evolving asynchronously. On
the positive side, we show that safety properties are decidable in the
synchronous case, as well as in the asynchronous case for a particular class of
algorithms. Several properties on the protocol can be decided as well. Decision
procedures rely on an encoding in Presburger arithmetics formulae that can be
verified by an SMT-solver. Feasibility of our approach is demonstrated by the
encoding of several case studies
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