36,772 research outputs found
Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework
While many existing formal concept analysis algorithms are efficient, they
are typically unsuitable for distributed implementation. Taking the MapReduce
(MR) framework as our inspiration we introduce a distributed approach for
performing formal concept mining. Our method has its novelty in that we use a
light-weight MapReduce runtime called Twister which is better suited to
iterative algorithms than recent distributed approaches. First, we describe the
theoretical foundations underpinning our distributed formal concept analysis
approach. Second, we provide a representative exemplar of how a classic
centralized algorithm can be implemented in a distributed fashion using our
methodology: we modify Ganter's classic algorithm by introducing a family of
MR* algorithms, namely MRGanter and MRGanter+ where the prefix denotes the
algorithm's lineage. To evaluate the factors that impact distributed algorithm
performance, we compare our MR* algorithms with the state-of-the-art.
Experiments conducted on real datasets demonstrate that MRGanter+ is efficient,
scalable and an appealing algorithm for distributed problems.Comment: 17 pages, ICFCA 201, Formal Concept Analysis 201
An exercise in transformational programming: Backtracking and Branch-and-Bound
We present a formal derivation of program schemes that are usually called Backtracking programs and Branch-and-Bound programs. The derivation consists of a series of transformation steps, specifically algebraic manipulations, on the initial specification until the desired programs are obtained. The well-known notions of linear recursion and tail recursion are extended, for structures, to elementwise linear recursion and elementwise tail recursion; and a transformation between them is derived too
Parallel simulation of Population Dynamics P systems: updates and roadmap
Population Dynamics P systems are a type of
multienvironment P systems that serve as a formal modeling
framework for real ecosystems. The accurate simulation of
these probabilisticmodels, e.g. with Direct distribution based
on Consistent Blocks Algorithm, entails large run times.
Hence, parallel platforms such as GPUs have been employed
to speedup the simulation. In 2012, the first GPU simulator of
PDP systems was presented. However, it was able to run only
randomly generated PDP systems. In this paper, we present
current updates made on this simulator, involving an input
modu le for binary files and an output module for CSV files.
Finally, the simulator has been experimentally validated with
a real ecosystem model, and its performance has been tested
with two high-end GPUs: Tesla C1060 and K40.Ministerio de EconomĂa y Competitividad TIN2012-37434Junta de AndalucĂa P08-TIC-0420
Synthesis of Attributed Feature Models From Product Descriptions: Foundations
Feature modeling is a widely used formalism to characterize a set of products
(also called configurations). As a manual elaboration is a long and arduous
task, numerous techniques have been proposed to reverse engineer feature models
from various kinds of artefacts. But none of them synthesize feature attributes
(or constraints over attributes) despite the practical relevance of attributes
for documenting the different values across a range of products. In this
report, we develop an algorithm for synthesizing attributed feature models
given a set of product descriptions. We present sound, complete, and
parametrizable techniques for computing all possible hierarchies, feature
groups, placements of feature attributes, domain values, and constraints. We
perform a complexity analysis w.r.t. number of features, attributes,
configurations, and domain size. We also evaluate the scalability of our
synthesis procedure using randomized configuration matrices. This report is a
first step that aims to describe the foundations for synthesizing attributed
feature models
An Integrated Semantic Web Service Discovery and Composition Framework
In this paper we present a theoretical analysis of graph-based service
composition in terms of its dependency with service discovery. Driven by this
analysis we define a composition framework by means of integration with
fine-grained I/O service discovery that enables the generation of a graph-based
composition which contains the set of services that are semantically relevant
for an input-output request. The proposed framework also includes an optimal
composition search algorithm to extract the best composition from the graph
minimising the length and the number of services, and different graph
optimisations to improve the scalability of the system. A practical
implementation used for the empirical analysis is also provided. This analysis
proves the scalability and flexibility of our proposal and provides insights on
how integrated composition systems can be designed in order to achieve good
performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201
- âŠ