Features for Killer Apps from a Semantic Web Perspective

There are certain features that that distinguish killer apps from other ordinary applications. This chapter examines those features in the context of the semantic web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing semantic web applications. Killer apps are highly tranformative technologies that create new e-commerce venues and widespread patterns of behaviour. Information technology, generally, and the Web, in particular, have benefited from killer apps to create new networks of users and increase its value. The semantic web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. The authors hope that this chapter will help to highlight some of the common ingredients of killer apps in e-commerce, and discuss how such applications might emerge in the semantic web

Privacy-Preserving Reengineering of Model-View-Controller Application Architectures Using Linked Data

When a legacy system’s software architecture cannot be redesigned, implementing additional privacy requirements is often complex, unreliable and costly to maintain. This paper presents a privacy-by-design approach to reengineer web applications as linked data-enabled and implement access control and privacy preservation properties. The method is based on the knowledge of the application architecture, which for the Web of data is commonly designed on the basis of a model-view-controller pattern. Whereas wrapping techniques commonly used to link data of web applications duplicate the security source code, the new approach allows for the controlled disclosure of an application’s data, while preserving non-functional properties such as privacy preservation. The solution has been implemented and compared with existing linked data frameworks in terms of reliability, maintainability and complexity

Tracking Federated Queries in the Linked Data

Federated query engines allow data consumers to execute queries over the federation of Linked Data (LD). However, as federated queries are decomposed into potentially thousands of subqueries distributed among SPARQL endpoints, data providers do not know federated queries, they only know subqueries they process. Consequently, unlike warehousing approaches, LD data providers have no access to secondary data. In this paper, we propose FETA (FEderated query TrAcking), a query tracking algorithm that infers Basic Graph Patterns (BGPs) processed by a federation from a shared log maintained by data providers. Concurrent execution of thousand subqueries generated by multiple federated query engines makes the query tracking process challenging and uncertain. Experiments with Anapsid show that FETA is able to extract BGPs which, even in a worst case scenario, contain BGPs of original queries

Personal Linked Data: A Solution to Manage User's Privacy on the Web

National audienceWhile using modern applications, personal digital data is spread over hundreds of servers all around the world and users have very poor control over these data. To tackle this issue, based on the semantic Web, we are developing a framework, named Privacy-Lookout, to allow people to be on the lookout for transgressions of their personal data privacy. Concretely, we propose to construct a personal linked data view of individuals to organize and semantically enrich the meta information of their personal data existing in the Web. The mean idea is to allow users to know if the information the Web posses about them respects their privacy principles. This paper introduces the first ideas of such approach

Semantic technologies to support the user-centric analysis of activity data

There is currently a trend in giving access to users of on-line services to their own data. In this paper, we consider in particular the data which is generated from the interaction between a user and an organisation online: activity data as held in websites and Web applications logs. We show how we use semantic technologies including RDF integration of log data, SPARQL and lightweight ontology reasoning to aggregate, integrate and analyse activity data from a user-centric point of view

Semantics, sensors, and the social web: The live social semantics experiments

The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment

A framework for feeding Linked Data to Complex Event Processing engines

A huge volume of Linked Data has been published on the Web, yet is not processable by Complex Event Processing (CEP) or Event Stream Processing (ESP) engines. This paper presents a frame-work to bridge this gap, under which Linked Data are first translated into events conforming to a lightweight ontology, and then fed to CEP engines. The event processing results will also be published back onto the Web of Data. In this way, CEP engines are connected to the Web of Data, and the ontological reasoning is integrated with event processing. Finally, the implementation method and a case study of the framework are presented

Linked data generation for adaptive learning analytics systems

The presentation for the paper "Linked Data Generation for Adaptive Learning Analytics Systems" given at the LILE2018 – Learning & Education with Web Data workshop at the WebSci conference 2018 in Amsterdam. The associated paper: https://biblio.ugent.be/publication/856697