| 1. |
- Basit, K. A., et al.
(author)
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GUMO inspired ontology to support user experience based Citywide Mobile Learning
- 2011
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In: Proc. - Int. Conf. User Sci. Eng., i-USEr. - 9781457716553 ; , s. 195-200
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Conference paper (peer-reviewed)abstract
- User experience has been extensively discussed in literature, yet the idea of applying it to explain and comprehend the conceptualization of Mobile Learning (ML) is relatively new. Consequently much of the existing works are mainly theoretical and they concentrate to establish and explain the relationship between ML and experience. Little has been done to apply or adopt it into practice. In contrast to the currently existing approaches, this paper presents an ontology to support Citywide Mobile Learning (CML). The ontology presented in this paper addresses three fundamental aspects of CML, namely User Model, User Experience and Places/Spaces which exist in the city. The ontology presented here not only attempts to model and translate the theoretical concepts such as user experience and Place/Spaces for citywide context for Mobile Learning, but also apply them into practice. The discussed ontology is used in our system to support Place/Space based CML.
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| 2. |
- Bunea, Ramona, et al.
(author)
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Exploiting dynamic privacy in socially regularized recommenders
- 2012
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In: Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on. - : IEEE. - 9780769549255 ; , s. 539-546
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Conference paper (peer-reviewed)abstract
- In this paper we introduce a privacy-aware collaborative filtering recommender framework which aims to address the privacy concern of profile owners in the context of social trust sparsity. While sparsity in social trust is mitigated by similarity driven trust using a probabilistic matrix factorization technique, the privacy issue is addressed by employing a dynamic privacy inference model. The privacy inference model exploits the underlying inter-entity trust information to obtain a personalized privacy view for each individual in the social network. We evaluate the proposed framework by employing an off-the-shelf collaborative filtering recommender method to make predictions using this personalized view. Experimental results show that our method offers better performance than similar non-privacy aware approaches, while at the same time meeting user privacy concerns.
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| 3. |
- Cena, Federica, et al.
(author)
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Forging Trust and Privacy with User Modeling Frameworks : An Ontological Analysis
- 2011
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In: The First International Conference on Social Eco-Informatics. - : IARIA. - 9781612081632 ; , s. 43-48
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Conference paper (peer-reviewed)abstract
- With the ever increasing importance of social net- working sites and services, socially intelligent agents who are responsible for gathering, managing and maintaining knowledge surrounding individual users are of increasing interest to both computing research communities as well as industries. For these agents to be able to fully capture and manage the knowledge about a user’s interaction with these social sites and services, a social user model needs to be introduced. A social user model is defined as a generic user model (model capable of capturing generic information related to a user), plus social dimensions of users (models capturing social aspects of user such as activities and social contexts). While existing models capture a proportion of such information, they fail to model and present ones of the most important dimensions of social connectivity: trust and privacy. To this end, in this paper, we introduce an ontological model of social user, composed by a generic user model component, which imports existing well-known user model structures, a social model, which contains social dimensions, and trust, reputation and privacy become the pivotal concepts gluing the whole ontological knowledge models together.
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| 4. |
- Claycomb, W., et al.
(author)
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Message from the Workshop Chairs - Part III
- 2015
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In: Proceedings - International Computer Software and Applications Conference. - : IEEE Communications Society.
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Conference paper (peer-reviewed)
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| 5. |
- Corodescu, Andrei-Alin, et al.
(author)
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Big Data Workflows : Locality-Aware Orchestration Using Software Containers
- 2021
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In: Sensors. - : MDPI AG. - 1424-8220. ; 21:24
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Journal article (peer-reviewed)abstract
- The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.
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| 6. |
- Corodescu, A. -A, et al.
(author)
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Locality-aware workflow orchestration for big data
- 2021
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In: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery. ; , s. 62-70
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Conference paper (peer-reviewed)abstract
- The development of the Edge computing paradigm shifts data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructure. Such a paradigm requires data processing solutions that consider data locality in order to reduce the performance penalties from data transfers between remote (in network terms) data centres. However, existing Big Data processing solutions have limited support for handling data locality and are inefficient in processing small and frequent events specific to Edge environments. This paper proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. Our solution considers any available data locality information by default, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare our system with Argo workflow and show significant performance improvements in terms of speed of execution for processing units of data using our data locality aware Big Data workflow approach.
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| 7. |
- Dautaras, Justas, et al.
(author)
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Mobile Crowdsensing with Imagery Tasks
- 2021
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In: Proceedings 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 54-61
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Conference paper (peer-reviewed)abstract
- The amount of gadgets connected to the internet has grown rapidly in the recent years. These human owned devices can potentially be used to gather sensor data without active involvement of their owners. One of the types of platforms that contribute to the utilisation of these devices are mobile crowdsensing systems. These systems can be used for different tasks including different types of community support. While these systems are quite widely used, yet little research has been done for integration of imagery data into them which require also human involvement. This paper considers a mobile crowdsensing system where gathering data from sensors is supported by crowdsourcing human intelligence for providing both textual and visual information. We also explore the best settings for such a system. Imagery processing is integrated into an already existing mobile crowdsensing platform CrowdS. The solution was evaluated both by a limited number of real life users and by conducting simulations. The simulations represent complex scenarios with multi-level variables. The results of simulation allow suggest an efficient configuration for the parameters and characteristics of the environment used in imagery integration.
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| 8. |
- Dessalk, Yared Dejene, et al.
(author)
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Scalable Execution of Big Data Workflows using Software Containers
- 2020
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In: Proceedings of the 12th International Conference on Management of Digital EcoSystems, MEDES 2020. - New York, NY, USA : Association for Computing Machinery, Inc. ; , s. 76-83
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Conference paper (peer-reviewed)abstract
- Big Data processing involves handling large and complex data sets, incorporating different tools and frameworks as well as other processes that help organisations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, require taking advantage of the elasticity of cloud infrastructures for scalability. In this paper, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies and message-oriented middleware (MOM) to enable highly scalable workflow execution. The approach is demonstrated in a use case together with a set of experiments that demonstrate the practical applicability of the proposed approach for the scalable execution of Big Data workflows. Furthermore, we present a scalability comparison of our proposed approach with that of Argo Workflows-one of the most prominent tools in the area of Big Data workflows.
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| 9. |
- Dokoohaki, Nima, et al.
(author)
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Achieving Optimal Privacy in Trust-Aware Social Recommender Systems
- 2010
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In: SOCIAL INFORMATICS. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642165665 ; , s. 62-79
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Conference paper (peer-reviewed)abstract
- Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommenders accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.
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| 10. |
- Dokoohaki, Nima, et al.
(author)
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An Adaptive Framework for Discovery andMining of User Profiles from Social Web-based Interest Communities
- 2013
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In: The Influence of Technology on Social Network Analysis and Mining. - Wien : Springer. - 9783709113455 ; , s. 497-519
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Book chapter (peer-reviewed)abstract
- Abstract Within this paper we introduce an adaptive framework for semi- tofully-automatic discovery, acquisition and mining of topic style interest profilesfrom openly accessible social web communities. To do such, we build an adaptivetaxonomy search tree from target domain (domain towards which we are gatheringand processing profiles for), starting with generic concepts at root moving down tospecific-level instances at leaves, then we utilize one of proposed Quest schemesto read the concept labels from the tree and crawl the source social networkrepositories for profiles containing matching and related topics. Using machinelearning techniques, cached profiles are then mined in two consecutive steps,utilizing a clusterer and a classifier in order to assign and predict correct profilesto their corresponding clustered corpus, which are retrieved later on by an ontology-based recommender to suggest and recommend the community members with theitems of their similar interest. Focusing on increasingly important digital culturalheritage context, using a set of profiles acquired from an openly accessible socialnetwork, we test the accuracy and adaptivity of framework. We will show that a tradeoff between schemes proposed can lead to adaptive discovery of highly relevant profiles.
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