Vol.12 No.1&2 February 1, 2013

A Survey and Analysis of Current CAPTCHA Approaches (pp001-040)
        Narges Roshanbin and James Miller
Faceted Search is an exploratory search mechanism, which provides an iterative way to refine search results by a faceted taxonomy. With the benefit of search results diversification, no need for a priori knowledge, and never leading to zero result, it can significantly reduce information overload. Faceted Search has witnessed a booming interest in the last ten years. In this paper, we first analyze the representative facet search models. Next, we present a general faceted search framework, and survey the related methods and techniques, including facet term extraction, hierarchy construction, compound term generation and facet ranking. Then we discuss the metrics for faceted search evaluation, and also highlight the main characteristics of a number of existing faceted search systems. Some directions for future research are finally presented.

A Survey of Faceted Search (pp041-064)
        Bifan Wei, Jun Liu, Qinghua Zheng, Wei Zhang, Xiaoyu Fu, and Boqin Fen
Faceted Search is an exploratory search mechanism, which provides an iterative way to refine search results by a faceted taxonomy. With the benefit of search results diversification, no need for a priori knowledge, and never leading to zero result, it can significantly reduce information overload. Faceted Search has witnessed a booming interest in the last ten years. In this paper, we first analyze the representative facet search models. Next, we present a general faceted search framework, and survey the related methods and techniques, including facet term extraction, hierarchy construction, compound term generation and facet ranking. Then we discuss the metrics for faceted search evaluation, and also highlight the main characteristics of a number of existing faceted search systems. Some directions for future research are finally presented.

Personalizing Search Using Socially Enhanced Interest Model Built from the Stream of User’s Activity (pp065-092)
        Tomas Kramar, Michal Barla, and Maria Bielikova
Older studies have proved that when searching information on the Web, users tend to write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are very ambiguous, search engines tend to find the most popular meaning -- someone who does not know anything about cascading stylesheets might search for a music band called css and be very surprised about the results. In this paper we propose a method which can infer additional keywords for a search query by leveraging a social network context and a method to build this network from the stream of user's activity on the Web. The approach was evaluated on real users using a personalized proxy server platform. The query expansion method was integrated into Google search engine and where possible, the original query was expanded and additional search results were retrieved and displayed. 70\% of the expanded results were clicked and we observed a significant increase of time that the users spent on the expanded results when compared to the time spent on standard results.

A Description-Based Composition Method for Mobile and Tethered Mashup Applications (pp093-130)
        Prach Chaisatien and Takehiro Tokuda
This paper presents a description-based composition method for rapid development of mashup applications for mobile devices. We designed and evaluated a generator system which allows an automatic generation of the declarative descriptions to mobile mashups. The generator system is based on a mobile mashup composition language called Mobile Application Interface Description Language (MAIDL). The language focused the reuse of mobile applications, Web services and Web applications as mashup components and allows composers to lay out the connection of component data flow of the mashup application. In technical aspect, our generator provides an automated mechanism that can reduce the mashup execution time. In usability aspect, the evaluation shows that our composition method could assist novice composers in interpreting and planning mobile mashup applications. We found no significant difference in composition time and correctness between novice and expert composers. From the evaluation result, we are able to indicate the expressivity, the major patterns, and common composition mistakes in our mobile mashup composition method. The further requirements lead to a new composition approach for single and multiple devices mashups via the use of tethered mashup applications.

An Approach for Web Service Discoverability Anti-Patterns Detection (pp131-158)
        Juan Manuel Rodriguez, Marco Crasso, and Alejandro Zunino
The Service Oriented Computing paradigm and its most popular implementation, namely Web Services, are at the crossing of distributed computing and loosely coupled systems. Web Services can be discovered and reused dynamically using non-proprietary mechanisms, but when Web Services are poorly described, they become difficult to be discovered, understood, and then reused. This paper presents novel algorithms and heuristics for automatically detecting common pitfalls that should be avoided when creating Web Services descriptions. To assess the accuracy of the proposed algorithms and heuristics, we compared their results with the results of manually analyzing a data-set of 400 publicly available services. In addition, we analyzed the correlation between the algorithms and heuristics results and other well-known quality metrics, which were presented by Al-Masri and Mahmoud. The average detection accuracy was 93.14% , and the false positive and false negative rates of 4.06% and 9.91% , respectively. Additionally, the Al-Masri and Mahmoud's quality metrics related to Web Services descriptions had a direct correlation with most of the automatic detecting results. The proposed algorithms and heuristics for automatically detecting common pitfalls are powerful tools for both improving existent Web Services and developing new Web Services that can be easily discovered, understood and reused.

Scalable RDF Graph Querying Using Cloud Computing (pp159-180)
        Ren Li, Dan Yang, Haibo Hu, Juan Xie, and Li Fu
With the explosion of the semantic web technologies, conventional SPARQL processing tools do not scale well for large amounts of RDF data because they are designed for use on a single-machine context. Several optimization solutions combined with cloud computing technologies have been proposed to overcome these drawbacks. However, these approaches only consider the SPARQL Basic Graph Pattern processing, and their file system-based schema can barely modify large-scale RDF data randomly. This paper presents a scalable SPARQL Group Graph Pattern (GGP) processing framework for large RDF graphs. We design a novel storage schema on HBase to store RDF data. Furthermore, a query plan generation algorithm is proposed to determine jobs based on a greedy selection strategy. Several query algorithms are also presented to answer SPARQL GGP queries in the MapReduce paradigm. An experiment on a simulation cloud computing environment shows that our framework is more scalable and efficient than traditional approaches when storing and retrieving large volumes of RDF data.

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