Freddy Lécué

Westland row why so slow?: fusing social media and linked data sources for understanding real-time traffic conditions

The advent of real-time traffic streaming offers users the opportunity to visualise current traffic conditions and congestion information. However, real-time information highlighting the underlying reason for tail-backs remains largely unexplored. Broken traffic lights, an accident, a large concert, or road-works reveal important information for citizens and traffic operators alike. Providing such information in real-time requires intelligent mechanisms and user interfaces in order to (i) harness heterogeneous data sources (volume, velocity, variety, veracity) and (ii) make derived knowledge consumable so users can visualize traffic conditions and congestion information making better routing decisions while travelling. This work focuses on surfacing relevant information and explaining the underlying reasons behind traffic conditions. To this end, static data from event providers, planned road works together with dynamically emerging events such as a traffic accidents, localized weather conditions or unplanned obstructions are captured through social media to provide users real-time feedback to highlight the causes of traffic congestion.

Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semantic Web technologies. In particular we present a system which integrates numerous sensors (exposing heterogenous, exogenous and raw data streams such as weather information, road works, city events or incidents) to improve accuracy and consistency of traffic congestion prediction. Our prototype of semantics-aware prediction, being used and experimented currently by traffic controllers in Dublin City Ireland, works efficiently with real, live and heterogeneous stream data. The experiments have shown accurate and consistent prediction of road traffic conditions, main benefits of the semantic encoding.

Applying semantic web technologies for diagnosing road traffic congestions

Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time.

Smart traffic analytics in the semantic web with STAR-CITY

This paper gives a high-level presentation of STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. Our prototype of semantics-aware traffic analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales efficiently with real, historical together with live and heterogeneous stream data. This paper highlights the lessons learned from deploying and using a system in Dublin City based on Semantic Web technologies.

Real-Time Urban Monitoring in Dublin Using Semantic and Stream Technologies

Several sources of information, from people, systems, things, are already available in most modern cities. Processing these continuous flows of information and capturing insight poses unique technical challenges that span from response time constraints to data heterogeneity, in terms of format and throughput. To tackle these problems, we focus on a novel prototype to ease real-time monitoring and decision-making processes for the City of Dublin with three main original technical aspects: (i) an extension to SPARQL to support efficient querying of heterogeneous streams; (ii) a query execution framework and runtime environment based on IBM InfoSphere Streams, a high-performance, industrial strength, stream processing engine; (iii) a hybrid RDFS reasoner, optimized for our stream processing execution framework. Our approach has been validated with real data collected on the field, as shown in our Dublin City video demonstration. Results indicate that real-time processing of city information streams based on semantic technologies is indeed not only possible, but also efficient, scalable and low-latency.

STAR-CITY: semantic traffic analytics and reasoning for CITY

This paper presents STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. We present how semantic diagnosis and predictive reasoning, both using and interpreting semantics of data to deliver useful, accurate and consistent inferences, have been exploited and adapted systematized in an intelligent user interface. Our prototype of semantics-aware traffic analytics and reasoning, experimented in Dublin City Ireland, works and scales efficiently with historical together with real live and heterogeneous stream data.

Adapting Semantic Sensor Networks for Smart Building Diagnosis

The Internet of Things is one of the next big changes in which devices, objects, and sensors are getting linked to the semantic web. However, the increasing availability of generated data leads to new integration problems. In this paper we present an architecture and approach that illustrates how semantic sensor networks, semantic web technologies, and reasoning can help in real-world applications to automatically derive complex models for analytics tasks such as prediction and diagnostics. We demonstrate our approach for buildings and their numerous connected sensors and show how our semantic framework allows us to detect and diagnose abnormal building behavior. This can lead to not only an increase of occupant well-being but also to a reduction of energy use. Given that buildings consume 40% of the worlds energy use we therefore also make a contribution towards global sustainability. The experimental evaluation shows the benefits of our approach for buildings at IBMs Technology Campus in Dublin.

Semantic content-based recommendation of software services using context

The current proliferation of software services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional services, yet their direct application to software services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune software services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

Semantic Web Service Search: A Brief Survey

Scalable means for the search of relevant web services are essential for the development of intelligent service-based applications in the future Internet. Key idea of semantic web services is to enable such applications to perform a high-precision search and automated composition of services based on formal ontology-based representations of service semantics. In this paper, we briefly survey the state of the art of semantic web service search.

Elastic Stream Processing for Distributed Environments

The current development towards the Internet of Things introduces the need for more flexibility in stream processing. To counter these challenges, the authors propose elastic stream processing for distributed environments, building on top of cloud computing and allowing a scalable and more flexible solution compared to traditional approaches.