Dionisis Margaris

An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques

In this paper, we present a framework which incorporates runtime adaptation for BPEL scenarios. The adaptation is based on (a) the quality of service parameters of available services, allowing for tailoring their execution to the diverse needs of individual users and (b) collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes. The proposed framework also caters maintaining the transactional semantics that invocations to multiple services offered by the same provider may bear. We present an algorithm combining QoS and collaborative filtering for BPEL adaptation.The combination introduces collaborating filtering functionality maintaining high QoS.We exploit the sparsity of the rating matrix to tackle known issues of CF.We evaluate the approach both in terms of performance and adaptation QoS.

Query personalization using social network information and collaborative filtering techniques

Abstract Query personalization has emerged as a means to handle the issue of information volume growth, aiming to tailor query answer results to match the goals and interests of each user. Query personalization dynamically enhances queries, based on information regarding user preferences or other contextual information; typically enhancements relate to incorporation of conditions that filter out results that are deemed of low value to the user and/or ordering results so that data of high value are presented first. In the domain of personalization, social network information can prove valuable; users’ social networks profiles, including their interests, influence from social friends, etc. can be exploited to personalize queries. In this paper, we present a query personalization algorithm, which employs collaborative filtering techniques and takes into account influence factors between social network users, leading to personalized results that are better-targeted to the user.

Recommendation information diffusion in social networks considering user influence and semantics

One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users’ profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items’ general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore, online advertisement efficiency is raised.

Knowledge-Based Leisure Time Recommendations in Social Networks

We introduce a novel knowledge-based recommendation algorithm for leisure time information to be used in social networks, which enhances the state-of-the-art in this algorithm category by taking into account (a) qualitative aspects of the recommended places (restaurants, museums, tourist attractions etc.), such as price, service and atmosphere, (b) influencing factors between social network users, (c) the semantic and geographical distance between locations and (d) the semantic categorization of the places to be recommended. The combination of these features leads to more accurate and better user-targeted leisure time recommendations.

Adapting WS-BPEL scenario execution using collaborative filtering techniques

WS-BPEL has been adopted as the predominant method for composing individual web services into higher-level business processes. The designers of WS-BPEL scenarios define at development time the specific web services that will be invoked in the context of the business process they model; in the context however of the current web, where each functionality is offered by multiple service providers, under different quality of service parameters, using a fixed BPEL scenario has been recognized to be inadequate for servicing the diverse needs of business processes clients. To this end, WS-BPEL scenario execution adaptation has been proposed, mainly allowing clients to specify quality of service policies, which drive the dynamic selection of the services that will be invoked. In this paper, we present a framework extending the quality of service-based adaptation mechanisms with collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes.

Pruning and aging for user histories in collaborative filtering

In this paper, we introduce algorithms for pruning and aging user ratings in collaborative filtering systems, based on their oldness, under the rationale that aged user ratings may not accurately reflect the current state of users regarding their preferences. The aging algorithm reduces the importance of aged ratings, while the pruning algorithm removes them from the database. The algorithms are evaluated against various types of datasets. The pruning algorithm has been found to present a number of advantages, namely (1) reducing the rating database size, (2) achieving better prediction generation times and (3) improving prediction quality by cutting off predictions with high error. The algorithm can be used in all rating databases that include a timestamp and has been proved to be effective in any type of dataset, from movies and music, to videogames and books.

Exploiting Internet of Things information to enhance venues’ recommendation accuracy

In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theaters, etc.), such as the user’s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.

Improving QoS delivered by WS-BPEL scenario adaptation through service execution parallelization

WS-BPEL scenario execution adaptation has been proposed by researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we study the potential to parallelize the execution of the WS-BPEL scenario in order to minimize its response time and/or achieving higher scores in the other qualitative dimensions, such as cost, reliability, etc., at the same time. We also describe, develop and validate a parallelization algorithm for realizing the proposed enhancements.

On Replacement Service Selection in WS-BPEL Scenario Adaptation

WS-BPEL scenario execution adaptation has been proposed by numerous researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences, these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we consider a number of issues related to WS-BPEL scenario adaptation, aiming to enhance adaptation quality and improve the QoS offered to end users. More specifically, with the goal of broadening the service selection pool we (a) discuss the identification of potential services that can be used to realize a functionality used in the WS-BPEL scenario and (b) elaborate on transactional semantics that invocations to multiple services offered by the same provider may bear. We also describe and validate an architecture for realizing the proposed enhancements.

Enhancing User Rating Database Consistency Through Pruning

Recommender systems are based on information about users’ past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose.