JWE Abstracts 

Vol.15 No.3&4 July 1, 2016

Review Article:

Enhancing Keyword Suggestion of Web Search by Leveraging Microblog Data (pp181-202)
Lin Li, Lu Qi, Fang Deng, Shengwu Xiong, and Jingling Yuan
Query suggestion of Web search is an effective approach to help users quickly express their information need and accurately get the information they need. Most of popular web-search engines provide possible query suggestions based on their query log data, which is a kind of implicit relevance based approach. However, it is difficult to give suggestions to search queries that have no or few historical evidences in query logs. To solve this problem, traditional pseudo relevance based approaches directly extract additional keywords from the top-listed search results of a given search query as suggestions. However, for hot topic or event related search queries, users more like to browse the latest and newly appeared contents. In this paper, we follow the direction of pseudo relevance based suggestion approaches by mining microblog data that is inherent in fast information propagation and dissemination. Our graph based rank aggregation approach combines a frequency based ranking with considering words themself and a LDA (Latent Dirichlet Allocation) based ranking by mining hidden topics behinds words. A dataset is crawled from the posts of fourteen micro-topics of Sina microblog platform. The experimental results clearly demonstrate our proposed approach is more effective than traditional pseudo relevance based methods. Moreover, the suggested keywords extracted from the posts published by \textit{authenticated users} are more effective than two traditional pseudo relevance based approaches, i.e., the posts submitted by \textit{all users} and the top returned posts returned by Sina search engine. In addition, applying LDA on microbog posts alone is far from satisfactory, but the combination of the frequency based ranking and the LDA based ranking show much better performance.

Privacy-Preserving Collaborative Web Services QoS Prediction via Yao’s Garbled Circuits and Homomorphic Encryption (pp203-225)
Lu Li, An Liu, Qing Li, Liusheng Huang, Wei Yang, and Guanfeng Liu
Collaborative Web services QoS prediction has become an important tool for the generation of accurate personalized QoS which is a cornerstone of most QoS-based approaches for Web services selection and composition. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We combine Yao's garbled circuit and additively homomorphic encryption via additively secret sharing to address non-linear computations required in the process of QoS prediction. We implement the proposed framework based on FasterGC, an open source implementation of Yao's garbled circuit, and conduct extensive simulations to study its performance. Simulation results, together with theoretical security and complexity analysis, show that privacy-preserving QoS prediction can be efficiently achieved in our framework.

Outbreak Power Measurement for Evolution Course of Web Events (pp226-248)
Xingzhi Wang, Xiangfeng Luo, Hui Zhang, and Huimin Liu
Nowadays, emergencies have a great impact on people’s daily lives. Web makes it possible to study emergencies from web information due to its real-time, open, and dynamic features. Measuring temporal features in evolution course of web events can help people timely get knowledge and understand emergent events, which contribute to reducing harms to our society caused by emergencies. In this paper, we propose an outbreak power measuring algorithm for the evolution of web events, in order to provide guidance for automatic detection and prediction of emergencies. An iterative algorithm is firstly introduced to calculate outbreak power of web events through increased web pages of events, increased attributes of events, and distribution of attributes in web pages and the relationships of attributes. Secondly, definition of web events types is proposed. From studying each type of web events, we dig out feature patterns and find laws of each type events, with hot event having the highest outbreak power while general event have the lowest outbreak power, and general event fluctuating most while urgent event fluctuating least, which can be prior knowledge of web events we study. And then, a fuzzy based algorithm is presented to discriminate the type of web events. By means of prior knowledge, membership grade of web events belong to each type can be calculated, and then the type of web events can be discriminated. Experiments on real data set demonstrate the proposed algorithm is both efficient and effective, and it is capable of providing accurate results of discrimination.

Searching for Relevant Tweets Based on Topic-Related User Activities (pp249-276)
Tomoya Noro and Takehiro Tokuda
Twitter is one of the largest social media. Although it can be used to get information on a topic of interest, it is not easy for us to find tweets relevant to the topic due to a massive amount of tweets and the small size of each tweet. Some relevant tweets may not include any terms explicitly related to the topic, and general content-based keyword search techniques and query expansion techniques are not effective for finding such relevant tweets. To solve this problem, we present a method for finding tweets on a topic of interest based on the Twitter user activities related to the topic such as tweet, retweet, and reply. The method consists of two phases: the preparation phase and the main phase. In the preparation phase, we create a user-tweet reference graph representing the relation between users and tweets based on the past user activities related to the topic, calculate the influence of each user and tweet in the topic, then define two types of each user's power, called ``Voice'' and ``Impact'', indicating ``how much voice the user has on the topic'' and ``how much impact the user has on the other users' tweets on the topic''. In the main phase, we calculate the relevance of newly-arrived tweets to the topic according to the Voice and the Impact score of the users who posted, retweeted, or replied to each of the tweets, then rank the tweets by the relevance score. The two phases are processed independently. Once the preparation phase is completed, the main phase can return the final result any time. Experimental results show that ``who retweeted or replied to the tweet'' is more effective for judging the relevance of each tweet to the topic than ``who posted the tweet'', and our method can find relevant tweets which do not include any terms explicitly related to the topic. We compare our method with an indegree-based method and a PageRank-based method, and show that our method outperforms the methods compared.

A Description-Based Hybrid Composition Method of Mashup Applications for Mobile Devices (pp277-309)
Korawit Prutsachainimmit and Takehiro Tokuda
Mashup application composition methods have been proposed for quick development of new mobile applications from existing resources. The existing methods have succeeded in developing data-flow mashup applications. However, they have limited capability to create event-driven mashup applications. A full treatment of data-flow and event-driven mashup composition is not yet achieved. This paper presents a new methodology for developing data-flow and event-driven mashup applications for mobile devices. Our hybrid composition method allows integration of mobile applications and REST Web services in a data-flow and event-driven manner. Description-based techniques and application generator tools are applied to reduce development cost. A mashup development system is implemented in Android mobile environment as the first experimental platform. The evaluation results show that our method is expressive and efficient in composing mobile mashup applications.

A Semantic Approach for Dynamically Determining Complex Composed Service Behaviour (pp310-338)
Carla Vairetti, Rosa Alarcon, and Jesus Bellido
Dynamic Web services composition aims to generate a composition plan at run-time. Semantic-based techniques rely on annotating services to facilitate the discovery of the service components that satisfy a user need (matchmaking). The matchmaking process places most attention on service selection rather than on the behaviour of the composed service, and the service components are arranged considering simple control-flow patterns (mainly sequence). In real life scenarios, however, composed service behaviour follows complex control-flow patterns that satisfy the needs of business processes, which are generally defined through manual service composition. In this paper we present a technique to derive complex composed service behaviour semantics, such semantics make possible to dynamically and automatically discover complex services compositions. We have implemented and tested our technique with a known dataset with better performance when compared to simple service composition strategies.

Multimedia Data Retrieving based on SOA Architecture (pp339-360)
Sid Ahmed Djallal Midouni, Youssef Amghar & Azeddine Chikh
In our past research we have already defined a full service approach to compose MaaS services for multimedia data retrieving. This approach is based on a four phases process: description; filtering; clustering; and restitution. In this work, we are especially interested in the description and filtering phases of this process. Our contribution is two-fold. First, we propose to extend for the MaaS description the W3C recommendation on semantics for web services (SAWSDL). To do so, we use two types of ontologies: a Domain Ontology encompassing concepts that define semantics of the related business domain and a Multimedia Ontology encompassing concepts that define a set of annotation properties of the multimedia content. Second, we show how this extension allows addressing the problem of matching between MaaS services and user needs.

Back to JWE Online Front Page