Context and motivation: Service-Based Systems are highly dynamic software systems composed of several web services. In contrast to other types of systems, Service-Based Systems rely on service providers to ensure that their web services comply with the agreed Quality of Service. Delivering an adequate Quality of Service is a critical and significant challenge that requires monitoring along the different activities in the Service-Based System's lifecycle.Question/problem: Current monitoring systems are designed to support specific activities (e.g. service selection, adaptation, etc.), but do not fulfil the requirements of all the activities in the Service-Based System's lifecycle.Principal ideas/results: In this paper, we present SALMon, a QoS monitoring framework able to support the whole Service-Based System's lifecycle. SALMon is highly versatile, since it combines different strategies for its configuration (model-based and invocation-based) and for the way it gets the Quality of Service (passive monitoring and online testing). Furthermore, its architecture supports easy extensibility with new quality attributes, independence of the technology of the monitored services and interoperability with other tools. We conducted a performance evaluation over real web services using suitable estimators for response time and evaluated both its overhead and capacity.Contribution: SALMon provides infrastructure that can be used in very different scenarios, as exemplified in this paper, both in terms of the lifecycle's phase addressed and the type of system (pure Service-Oriented Architecture, cloud-based systems, etc.). This diversity of situations addressed makes SALMon a significant contribution both for practitioners that may be interested in integrating a working technology in their software solutions, and for researchers who can conduct their investigation on top of a reliable infrastructure. (C) 2015 Elsevier Ltd. All rights reserved.
Quality of an open source software ecosystem (OSS ecosystem) is key for different ecosystem actors such as contributors or adopters. In fact, the consideration of several quality aspects(e.g., activeness, visibility, interrelatedness, etc.) as a whole may provide a measure of the healthiness of OSS ecosystems. The more health a OSS ecosystem is, the more and better contributors and adopters it will gather. Some research tools have been developed to gather specific quality information from open source community data sources. However, there exist no frameworks available that can be used to evaluate their quality as a whole in order to obtain the health of an OSS ecosystem. To assess the health of these ecosystems, we propose to adopt robust principles and methods from the Service Oriented Computing field.
El modelo de computaci?on en la nube (cloud computing) ha ganado mucha popularidad en los ultimos anos, prueba de ello es la cantidad de productos que distintas empresas han lanzado para ofrecer software, capacidad de procesamiento y servicios en la nube. Para una empresa el mover sus aplicaciones a la nube, con el fin de garantizar disponibilidad y escalabilidad de las mismas y un ahorro de costes, no es una tarea facil. El principal problema es que las aplicaciones tienen que ser redisenadas porque las plataformas de computaci?on en la nube presentan restricciones que no tienen los entornos tradicionales. En este articulo presentamos CumuloNimbo, una plataforma para computacion en la nube que permite la ejecucion y migracion de manera transparente de aplicaciones multi-capa en la nube. Una de las principales caracteristicas de CumuloNimbo es la gestion de transacciones altamente escalable y coherente. El articulo describe la arquitectura del sistema, asi como una evaluaci?on de la escalabilidad del mismo.
La ciencia de los servicios es, mas que una nueva disciplina, un enfoque interdisciplinar para el estudio, diseno, e implementacion de sistemas orientado a servicios, que actua como paraguas que cubre todos los aspectos de computacion utilizados (es decir, especificacion y diseno orientado a servicios, arquitecturas orientada a servicios, servicios web, etc.), siendo actualmente una de las areas de investigacion mas activas en el ambito de la informatica. La provision de servicios y la innovacion de los mismos estan basadas sobre todo en las tecnologias de informacion. Este articulo presenta un analisis de las caracteristicas esenciales e inherentes a la orientacion a servicios. En base a este analisis se identifican carencias en los metodos y herramientas de ingenieria de requisitos actuales que dificultan su aplicacion al paradigma orientado a servicios. Estas carencias contribuyen a trazar un plan de trabajo con los retos que la Ingenieria de Requisitos Orientada a Servicios (IROS) debera enfrentar los proximos anos. Por ultimo, se ofrece un marco de IROS con el potencial para dar solucion a los retos encontrados. Este marco enfatiza, ademas, la importancia de la reutilizacion de metodos y conocimiento existentes mediante una estrategia de macromodelado, asi como la aplicabilidad de las aproximaciones de desarrollo dirigido por modelos.
In this paper, we apply the CART ,C5.0 , GP decision tree classifiers and compares with logic model and ANN model for Taiwan listed electronic companies bankruptcy prediction. Results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k-fold cross validation test in out sample. That is to say, GP decision tree model have the highest accuracy and lowest expected misclassification costs. It can provide an efficient alternative to discriminates financial distress problems in Taiwan.
This paper explores the use of evolutionary algorithms (EA) for parameter selection of image segmentation algorithms. Typically, segmentation algorithms are tuned "by hand" by the user through modifying various combinations of parameters - a time consuming and computationally expensive process. EA provide a means to explore possible parameter combinations without user trial-and-error. Herein we show the advantages of applying EA to segmentation algorithms using an example application of 3D medical image reconstruction. We also highlight future improvements to our method that will increase its efficiency and usability.
Understanding the structure of protein interaction networks is useful as a first step towards revealing the underlying principles of the large-scale organisation of the cell. In this study, we analyse the yeast (Saccharomyces cerevisiea) protein-protein interaction network with a semantic similarity measure based on functional annotations from Gene Ontology. We use this measure to assess the functional relevance of modular formations in the interactome. Our results indicate the usefulness of this measure as a tool for exploring the functional similarity of protein interactions based on Gene Ontology.
Buckley and Qu proposed a method to solve systems of linear fuzzy equations. Basically, in their method the solutions of all systems of linear crisp equations formed by the α-levels are calculated. We propose a new method for solving systems of linear fuzzy equations based on a practical algorithm using parametric functions in which the variables are given by the fuzzy coefficients of the system. By observing the monotonicity of the parametric functions in each variable, i.e. each fuzzy coefficient in the system, we improve the algorithm by calculating less parametric functions and less evaluations of these parametric functions. We show that our algorithm is much more efficient than the method of Buckley and Qu.
This paper proposes an agent-based computational model of a lottery market based on an expected-utility paradigm, in which agents' decisions regarding lottery participation are based on their own subjective beliefs, and those beliefs are evolving over time with genetic algorithms. The simulation results are then compared with another agent-based lottery market with different agent engineering. It is found that almost all emergent properties, such as the Laffer curve, the halo effect (lottomania), conscious-selection behavior, and the interdependent preference (regretting effect) are qualitatively robust with these two different designs of agents
In this paper, a non-deterministic (portfolio-based) finite-state automaton is proposed to generalize the current financial trading applications of genetic programming from single risky asset to multi risky assets. The GP-evolved trading rules are tested under various settings with respect to search intensity, genetic portfolios, and validating parameters. The rules are compared with performance of a buy-and-hold strategy in a context of international capital flow using data from Taiwan, the U.S., Hong Kong, Japan and the U.K. The GP are evaluated by using both the mean rule and the majority rule. However, by and large, it is found that GP was outperformed by the buy-and-hold strategy in both cases.
Web services are becoming the prominent paradigm for distributed computing and electronic business. Web service design and composition is a distributed programming activity. It requires software engineering principles and technology support for Web service extension and composition. Although a Web service provides the possibility of offering new services by reuse and extension instead of designing them from scratch, to this data there is little research initiative in the context. In this paper we concentrate on the vital aspect of Web service reuse and extension-extension consistency of Web services in the presence of invariants within Web service extension and composition. We formally formulate requirements for Web service's extensions that guarantee consistency of the extended system in the presence of explicit invariants. The contribution of this paper lays down a solid foundation for our further research about the framework of Web service composition.
We. design arid validate methods for implementing a software-based digital set-top box for handling concurrent live video streams. Unlike conventional video servers that, handle only read requests to retrieve prerecorded video titles, a set-top box must handle write requests to store concurrent live video streams for later viewing. Moreover, it must react to user interactive functions such as pausing a live video stream while recording the video stream in the background or switching to another live video stream in real-time. The functional requirements of set-top boxes thus are vastly different from that of conventional video servers. We model and identify minimum resource requirements for the set-top box to function respectably based on our design and analyze design trade-offs for achieving certain performance goals.
In this paper, we introduce a new notion of fuzzy ideals of a ring, and get some fundamental results. Particularly, We have this notion is an extension of existing two concepts; the set of fuzzy ideals is an ordered set.
Most content-based retrieval (CBR) techniques such as shape and color comparison among objects are for image and video. The mechanisms are designed based on 2-D information. We propose a naive shape similarity function, based on 3-D information extracted from a VRML scene database. The method is the first step of our project, which aims to incorporate future extraction of virtual reality objects, such as chairs, car, and others in 3-D space. House interior designers can use the proposed system. The user can select proper scenes and furniture in order to meet the requirement of potential customers.
Recently integration of OLAP and data mining has drawn much attention, but there is still a lack of concrete result. In this paper we explore one aspect of such integration, called influential association rule mining. We first introduce the basic idea of association rule mining, and the two approaches, called IARM (Influential Association Rule Mining) and its improvement, IARMBM (Influential Association Rule Mining with BitMap) are briefly described. In addition, related experiments and comparisons are also reported.
This paper proposes Clea, a framework for the coordination of applications and networks. Clea conveys requests from applications to networks and enables information on network characteristics, status, and functions to be used by applications. This makes it possible for an application to adapt flexibly to dynamic changes in network status and to utilize network resources effectively. Clea also enables coordination of applications and networks to be described in a uniform and concise manner.
Mining association rules in transaction databases has received much attention in the field of data mining. Although progress has been made on techniques of mining association rules, the results often only indicate the mutual correlative relationships among the frequent items, paying no attention to the directional, or causal relations. For example, when a data set indicates an association between items A and B, it is often not clear whether the access of A caused the access of B, or the converse. In real world applications, however, knowing such causal relations is extremely useful for decision support. People would not only be interested in the facts that A and B are related, but also in the possible sequences and directions among the items. Mining transaction databases for this kind of knowledge offers the potential for deep analysis of business situations and finding strategies of operation. In this paper, we employ a Bayesian approach to mining causal relations from frequent itemsets. The results of our research include two algorithms based on Bayesian statistics model: a serial and diverging connection discovery algorithm (SDCD) and a converging connection discovery algorithm (CCD). Experimental results indicate that the performance of the algorithms is scalable.
Effective notes taking is an important link to the learning chain. In this paper, we present an Online Notes-taker intended to design and deliver personalized course notes for individual students. The notes contents can be organized based on the user profiles such as learning styles, study plan and assessment result. The characteristics of this approach are to provide better supports to the concept mapping, learning styles, content-based, and interactive learning environment. The system is running on the Internet with XML and SMIL, following an agent-based approach.
One of the few algorithms that can evaluate features in very large feature sets is Relief [1, 2]. This paper documents a bias in Relief against non-monotonic features, including Gaussian features, and proposes a modification to Relief that removes the bias.
