Daniele Dell'Aglio

Social Listening of City Scale Events Using the Streaming Linked Data Framework

City-scale events may easily attract half a million of visitors in hundreds of venues over just a few days. Which are the most attended venues? What do visitors think about them? How do they feel before, during and after the event? These are few of the questions a city-scale event manger would like to see answered in real-time. In this paper, we report on our experience in social listening of two city-scale events (London Olympic Games 2012, and Milano Design Week 2013) using the Streaming Linked Data Framework.

BOTTARI: An augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams

In 2011, an average of three million tweets per day was posted in Seoul. Hundreds of thousands of tweets carry the live opinion of some tens of thousands of users about restaurants, bars, cafes, and many other semi-public points of interest (POIs) in the city. Trusting this collective opinion to be a solid base for novel commercial and social services, we conceived BOTTARI: an augmented reality application that offers personalized and localized recommendation of POIs based on the temporally weighted opinions of the social media community. In this paper, we present the design of BOTTARI, the potentialities of semantic technologies such as inductive and deductive stream reasoning, and the lessons learnt in experimentally deploying BOTTARI in Insadong-a popular tourist area in Seoul-for which we have been collecting tweets for three years to rate the hundreds of restaurants in the district. The results of our study demonstrate the feasibility of BOTTARI and encourage its commercial spread.

On Correctness in RDF Stream Processor Benchmarking

Two complementary benchmarks have been proposed so far for the evaluation and continuous improvement of RDF stream processors: SRBench and LSBench. They put a special focus on different features of the evaluated systems, including coverage of the streaming extensions of SPARQL supported by each processor, query processing throughput, and an early analysis of query evaluation correctness, based on comparing the results obtained by different processors for a set of queries. However, none of them has analysed the operational semantics of these processors in order to assess the correctness of query evaluation results. In this paper, we propose a characterization of the operational semantics of RDF stream processors, adapting well-known models used in the stream processing engine community: CQL and SECRET. Through this formalization, we address correctness in RDF stream processor benchmarks, allowing to determine the multiple answers that systems should provide. Finally, we present CSRBench, an extension of SRBench to address query result correctness verification using an automatic method.

Urbanopoly -- A Social and Location-Based Game with a Purpose to Crowdsource Your Urban Data

Checking-in various venues in our surrounding environment via location-based apps like foursquare is becoming more and more popular; this behaviour makes people share some “bits” of their location with their friends. Exploiting this trend in a Human Computation fashion to collect information about urban environments is the aim of the Urbanopoly Android app - a social, mobile and location-based Game with a Purpose designed around the idea of the “monopoly” board game. In this paper, we illustrate the main design choices for Urbanopoly - including the use of social media like Facebook in the context of a Human Computation approach - and we explain the gameplay.

Large knowledge collider: a service-oriented platform for large-scale semantic reasoning

Recent advances in the Semantic Web community have yielded a variety of reasoning methods used to process and exploit semantically annotated data. However, most of those methods have only been approved for small, closed, trustworthy, consistent, and static domains. Still, there is a deep mismatch between the requirements for reasoning on a Web scale and the existing efficient reasoning algorithms over restricted subsets. This paper describes the pilot implementation of LarKC -- the Large Knowledge Collider, a platform, which focuses on supporting large-scale reasoning over billions of structured data in heterogeneous data sets. The architecture of LarKC allows for an effective combination of techniques coming from different Semantic Web domains by following a service-oriented approach, supplied by sustainable infrastructure solutions.

RSP-QL Semantics: A Unifying Query Model to Explain Heterogeneity of RDF Stream Processing Systems

RDF and SPARQL are established standards for data interchange and querying on the Web. While they have been shown to be useful and applicable in many scenarios, they are not sufficiently adequate for dealing with streams of data and their intrinsic continuous nature. In the last years data and query languages have been proposed to extend both RDF and SPARQL for streams and continuous processing, under the name of RDF Stream Processing-RSP. These efforts resulted in several models and implementations that, at a first look, appear to propose alternative syntaxes but equivalent semantics. However, when asked to continuously answer the same queries on the same data streams, they provide different answers at disparate moments due to the heterogeneity of their operational semantics. These discrepancies render the process of understanding and comparing continuous query results complex and misleading. In this work, the authors propose RSP-QL, a comprehensive model that formally defines the semantics of an RSP system. RSP-QL makes explicit the hidden assumptions of currently available RSP systems, allows defining a formal notion of correctness for RSP query results and, thus, explains why available implementations provide different answers at disparate moments.

Linking smart cities datasets with human computation: the case of urbanmatch

To realize the Smart Cities vision, applications can leverage the large availability of open datasets related to urban environments. Those datasets need to be integrated, but it is often hard to automatically achieve a high-quality interlinkage. Human Computation approaches can be employed to solve such a task where machines are ineffective. We argue that in this case not only peoples background knowledge is useful to solve the task, but also peoples physical presence and direct experience can be successfully exploited. In this paper we present UrbanMatch, a Game with a Purpose for players in mobility aimed at validating links between points of interest and their photos; we discuss the design choices and we show the high throughput and accuracy achieved in the interlinking task.

The Experience of Realizing a Semantic Web Urban Computing Application

Urban Computing is a branch of Pervasive Computing that investigates urban settings and everyday lifestyles. A large quantity of information to develop pervasive applications for urban environments is often already available, even if scattered and not integrated: maps, points of interest, user locations, traffic, pollution, and events are just a few examples of the digitalized information which we can access on the Web. Applications for mobile users that leverage such information are rapidly growing. In this article, we report our experience in addressing practical computational issues influencing the use of Geographic Information Systems and geospatial data from the standpoint of semantics and pervasive computing. We refer to the early achievements of the LarKC project, in which we developed an Urban Computing demonstrator. We highlight the positive sides of our experience and we discuss open issues and possible advances.

Semantic Traffic-Aware Routing Using the LarKC Platform

The popularity of location-based services and automotive navigation systems calls for a new generation of intelligent solutions to support users in mobility. This article presents a traffic-aware semantic routing service for mobile users based on the Large Knowledge Collider (LarKC) Semantic Web pluggable platform. It proposes a technique for integrating conceptual query answering with statistical learning and operations research algorithms. The presented prototype of a traffic-aware semantic routing service works efficiently with large, heterogeneous information sources and delivers value-added services to mobile users.

Approximate Continuous Query Answering over Streams and Dynamic Linked Data Sets

To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static background data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases and applications become unresponsive. In this paper, we address the problem of optimizing the evaluation of these queries by leveraging local views on background data. Local views enhance performance, but require maintenance processes, because changes in the background data sources are not automatically reflected in the application. We propose a two-step query-driven maintenance process to maintain the local view: it exploits information from the query e.g.,i?źthe sliding window definition and the current window content to maintain the local view based on user-defined Quality of Service constraints. Experimental evaluation show the effectiveness of the approach.