Controlling services in a mobile context-aware infrastructure

Context-aware application behaviors can be described as logic rules following the Event-Control-Action (ECA) pattern. In this pattern, an Event models an occurrence of interest (e.g., a change in context); Control specifies a condition that must hold prior to the execution of the action; and an Action represents the invocation of arbitrary services. We have defined a Controlling service aiming at facilitating the dynamic configuration of ECA rule specifications by means of a mobile rule engine and a mechanism that distributes context reasoning activities to a network of context processing nodes. In this paper we present a novel context modeling approach that provides application developers and users with more appropriate means to define context information and ECA rules. Our approach makes use of ontologies to model context information and has been developed on top of web services technology

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

A JSON Token-Based Authentication and Access Management Schema for Cloud SaaS Applications

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies, but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.Comment: 6 Page

An Approach for Assessment of Electronic Offers

Internet and mobile technology enable businesses to invent new business models by applying new forms of organization or offering new products and services. In order to assess these new business models there has to be a methodology that allows identifying advantages that are caused by electronic and mobile commerce. The proposed approach builds upon the theory of informational added values that provides a classification of gains produced by information work. This theory is extended by the definition of categories of technology inherent added values that result in informational added values. These informational added values can be perceived by users of information products and services and therefore be used to assess electronic offers. The relationship between technology inherent and informational added values will be clarified with examples of real business models. Furthermore, a classification of basic business model types will be provided.

Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview

This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented

Effectiveness of Data Enrichment on Categorization: Two Case Studies on Short Texts and User Movements

The widespread diffusion of mobile devices, e.g., smartphones and tablets, has made possible a huge increment in data generation by users. Nowadays, about a billion users daily interact on online social media, where they share information and discuss about a wide variety of topics, sometimes including the places they visit. Furthermore, the use of mobile devices makes available a large amount of data tracked by integrated sensors, which monitor several users’ activities, again including their position. The content produced by users are composed of few elements, such as only some words in a social post, or a simple GPS position, therefore a poor source of information to analyze. On this basis, a data enrichment process may provide additional knowledge by exploiting other related sources to extract additional data. The aim of this dissertation is to analyze the effectiveness of data enrichment for categorization, in particular on two domains, short texts and user movements. We de- scribe the concept behind our experimental design where users’ content are represented as abstract objects in a geometric space, with distances representing relatedness and similarity values, and contexts representing regions close to the each object where it is possibile to find other related objects, and therefore suitable as data enrichment source. Regarding short texts our research involves a novel approach on short text enrichment and categorization, and an extensive study on the properties of data used as enrich- ment. We analyze the temporal context and a set of properties which characterize data from an external source in order to properly select and extract additional knowledge related to textual content that users produce. We use Twitter as short texts source to build datasets for all experiments. Regarding user movements we address the problem of places categorization recognizing important locations that users visit frequently and intensively. We propose a novel approach on places categorization based on a feature space which models the users’ movement habits. We analyze both temporal and spa- tial context to find additional information to use as data enrichment and improve the importance recognition process. We use an in-house built dataset of GPS logs and the GeoLife public dataset for our experiments. Experimental evaluations on both our stud- ies highlight how the enrichment phase has a considerable impact on each process, and the results demonstrate its effectiveness. In particular, the short texts analysis shows how news articles are documents particularly suitable to be used as enrichment source, and their freshness is an important property to consider. User Movements analysis demonstrates how the context with additional data helps, even with user trajectories difficult to analyze. Finally, we provide an early stage study on user modeling. We exploit the data extracted with enrichment on the short texts to build a richer user profile. The enrichment phase, combined with a network-based approach, improves the profiling process providing higher scores in similarity computation where expectedCo-supervisore: Ivan ScagnettoopenDottorato di ricerca in Informaticaope

Characterizing Location-based Mobile Tracking in Mobile Ad Networks

Mobile apps nowadays are often packaged with third-party ad libraries to monetize user data