JWE Abstracts 

Vol.11 No.1 March 1, 2012

Research Articles: 

A Framework for Interactively Helpful Web Forms (pp001-022)
Morten Bohoj, Niels Olof Bouvin, and Henrik Gammelmark
AdapForms is a framework for adaptive forms, consisting of a form definition language designating structure and constraints upon acceptable input, and a software architecture that continuously validates and adapts the form presented to the user. The validation is performed server-side, which enables the use of complex business logic without duplicate code. Thus, the state of the form is kept persistently at the server, and the system ensures that all submitted forms are valid and type safe.

Domain Specific Language for the Generation of Learning Management Systems Modules (pp023-050)
Carlos E. Montenegro-Marin, Juan M. Cueva-Lovelle, Oscar Sanjuan-Martinez and
 Vicente García-Diaz
Nowadays there are many research projects conducted in the areas of Learning Management Systems (LMS) and Model-Driven Engineering (MDE). These research projects have shown that there are LMS platforms with different architectures and inoperative to each other. The most significant contribution of MDE has been the creation of a common meta-metamodel. This meta-metamodel allows transformations between different models. This research work presents a LMS metamodel. The metamodel created is based on the study of five LMS platforms. The LMS metamodel is a global model that makes a bridge for the transformation of modules between the model and different LMS platforms, and it also presents the development of a Domain Specific Language (DSL) tool to validate the metamodel, the transformation process of the model with our DSL Tool to modules deployed in Moodle, Claroline and Atutor, and finally testing and validation of creating modules with LMS platforms VS creating modules with our DSL Tool.

A Feature-Opinion Extraction Approach to Opinion Mining (pp051-063)
Bolanle A. Ojokoh and Olumide Kayode
With the rapid expansion of the web and e-commerce in recent times, increasingly numerous products are bought and sold on the Web. A lot of product reviews which would be very useful for potential customers to make better decisions are generated by web users. It is highly essential to produce a correct and quick summary of these reviews. In this paper, we propose a method that extracts feature and opinion pairs from online reviews, determines the polarity and strength of these opinions with the aim of summarizing and determining the recommendation status of the customers’ reviews. The evaluation results on opinion extraction from the reviews of digital camera demonstrate the effectiveness of the proposed technique.

Prediction Algorithms for Prefetching in the Current Web (pp064-078)
Josep Domenech, Julio Sahuquillo, Jose A. Gil, and Ana Pont
This paper reviews a representative subset of the prediction algorithms used for Web prefetching
classifying them according to the information gathered. Then, the DDG algorithm is described. The main novelty of this algorithm lies in the fact that, unlike previous algorithms, it creates a prediction model according to the structure of the current web. To this end, the algorithm distinguishes between container objects and embedded objects. Its performance is compared against important existing algorithms, and results show that, for the same amount of extra requests to the server, DDG always outperforms those algorithms by reducing the perceived latency up to 70% more without increasing the complexity order.

Predictive Self-Healing of Web Services Using Health Score (pp079-092)
Mohsen Sharifi, Somayeh Bakhtiari Ramezani, and Amin Amirlatifi
Existing self-healing mechanisms for Web services constantly monitor services and their computational environment, analyze system state, determine failure occurrences, and execute built-in recovery plans (MAPE loop). We propose a more pro-active self healing mechanism that uses a multi-layer perceptron ANN and a health score mechanism to learn about the occurrences of failures or quality of service degradation in advance, without requiring modifications to the framework of services used by applications. Highest score is assigned to the system upon start and is degraded during system execution whenever a service fails to operate or the time-to-leave (TTL) of the client side requests increases. Application of the proposed mechanism to a set of vehicle tracking Web services decreased the probability of out of service occurrences by 70% and increased system quality of service by 13%. The overhead of the mechanism was nearly 3% and negligible, whilst TTL for a request from the client side decreased by 20%.

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