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Mapping extended feature models to constraint logic programming over finite domains

As feature models for realistic product families may be quite complicated, automated analysis of feature models is desirable. Although several approaches reported in the literature addressed this issue, complex featureattribute and attribute-attribute relationships in extended feature models were not handled effectively. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate basic, cardinality-based, and extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships, into constraint logic programs. It thus enables use of offthe-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also briefly discuss the ramifications of including feature-attribute relationships in operations of analysis. We believe that this proposal will be effective for further leveraging of constraint logic programming for automated analysis of feature models.

A Mashup-Based Strategy for Migration to Service-Oriented Computing

Service-Oriented Computing holds great promises to realize the vision of on-demand services available anytime and anywhere. It is still not clear, however, when and how the existing systems will benefit from this new wave. The problem is particularly acute for the software embedded in myriad devices. This work charts a roadmap for migration of legacy software to pervasive service-oriented computing. A key idea is to achieve integration even at the presentation layer, not only at backend layers like application or data. This requires re-inventing the popular mashup technology for the enterprise level. Domain-Specific-Kits, which has been originally introduced within the context of software factory automation, has been reshaped as another enabling technology towards migrating to the service harmonization platform.

Modeling and querying fuzzy spatiotemporal databases

Modeling spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations, is a topic of great importance in geographic information systems, computer vision, environmental data management systems, etc. Because of complex requirements, it is challenging to represent spatiotemporal data and its features in databases and to effectively query them. This article presents a new approach to model and query the spatiotemporal data of fuzzy spatial and complex objects and/or spatial relations. In our case study, we use a meteorological database application in an intelligent database architecture, which combines an object-oriented database with a knowledgebase for modeling and querying spatiotemporal objects.

Legacy Migration to Service-Oriented Computing with Mashups

Although service-oriented computing holds great promises, it is still not clear when and how the existing systems will exploit this new computational model. The problem is particularly severe for the software having several years of use. This work provides a roadmap for the migration of legacy software to service-oriented computing by means of right levels of abstraction. The key idea is having integration even at the presentation layer, not only at backend layers such as application or data. This requires re-inventing the popular MASHUP technology of Web 2.0 at the enterprise level. Domain- specific-kits and choreography engine concepts that were originally introduced by the software factory automation approach have been reshaped as another enabling technology towards migrating to the service harmonization platform. The paper also exemplifies the proposed approach on a simple case problem.

Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as Word Net, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy.

Evidential location estimation for events detected in Twitter

Event detection from microblogs and social networks, especially from Twitter, is an active and rich research topic. By grouping similar tweets in clusters, people can extract events and follow the happenings in a community. In this work, we focus on estimating the geographical locations of events that are detected in Twitter. An important novelty of our work is the application of evidential reasoning techniques, namely the Demspter-Shafer Theory (DST), for this problem. By utilizing several features of tweets, we aim to produce belief intervals for a set of possible discrete locations. DST helps us deal with uncertainties, assign belief values to subsets of solutions, and combine pieces of evidence obtained from different tweet features. The initial results on several real cases suggest the applicability and usefulness of DST for the problem.

An ontology for trajectory simulation

From the concept exploration for a weapon system to training simulators, from hardware-in-the-loop simulators to mission planning tools, trajectory simulations are used throughout the life cycle of a weapon system. A trajectory simulation can be defined as a computational tool to calculate the flight path and flight parameters of munitions. There is a wide span of trajectory simulations differing widely with respect to their performance and fidelity characteristics, from simple point-mass simulations to six-seven degrees of freedom hardware-in-the-loop missile simulations. From our observations, it is a common practice in the industry that developments of these simulations are carried out as isolated projects although they rely on the same body of knowledge. We envision an ontology that will capture the common knowledge in trajectory simulation domain and make domain knowledge available for reuse. Trajectory simulation ontology, dubbed TSONT, is being developed to realize this vision

From extended feature models to constraint logic programming

Abstract Since feature models for realistic product families may be quite complicated, the automated analysis of feature models is desirable. Although several approaches reported in the literature address this issue, complex cross-tree relationships involving attributes in extended feature models have not been handled. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate into constraint logic programs; basic, cardinality-based and extended feature models, which can include complex cross-tree relationships involving attributes. This translation enables the use of off-the-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also present the performance results of some well-known analysis operations on an example translated model.

A Metamodel For The HLA Object Model

The High Level Architecture (HLA), IEEE Std. 15162000, provides a general framework for distributed simulation applications, called federations. An HLA object model, be it a simulation object model (SOM), a federation object model (FOM) or the management object model (MOM), describes the data that can be transferred during some federation execution. This paper introduces a metamodel for the HLA Object Model, fully accounting for IEEE Std. 1516.2. The metamodel (“paradigm”) is constructed with GME (Generic Modeling Environment), a meta-programmable tool for domain-specific modeling, developed by the Institute for Software Integrated Systems at Vanderbilt University. The paper also describes an application based on the paradigm. This work aims at bringing model-integrated computing to bear on HLA-based distributed simulation.

Multilevel graph partitioning: an evolutionary approach

The graph partitioning problem is defined as that of dividing the vertices of an undirected graph into a set of balanced parts through the removal of a set of edges, whose size is to be minimized. A number of researchers have investigated multilevel schemes, which coarsen the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partitioning of the original graph. In this paper, a genetic algorithm for the coarsening phase of a multilevel scheme for graph partitioning is presented. The proposed approach has been demonstrated to improve the solution quality at the expense of running time.