Ruben Costa

Twitter mining for traffic events detection

Nowadays with the proliferation of smartphones and tablets on the market, almost everyone has access to mobile devices that offer better processing capabilities and access to new information and services. The Web is undoubtedly the best tool for sharing content, especially through social networks. One of the most useful information that can be extracted is the geographical one. Current navigation systems lack in several ways to satisfy the need to process and reason upon such volumes of data, namely, to accurately provide information about urban traffic in real-time and the possibility to personalize the information used by such systems. This paper describes an approach to integrate and fuse tweet messages from traffic agencies in UK, with the objective of detecting the geographical focus of traffic events. Tweet messages are considered in this work given its uniqueness, the real time nature of tweets which may be used to quickly detect a traffic event and its simplicity; it only cost 140 characters to generate a message (called “tweet”) for any user. The approach presented here is composed by several steps: tweet classification, event type classification, name entity recognition, geolocation and event tracking. Finally, we do an experimental study on a real dataset composed by traffic related tweet messages to access the accuracy of proposed approach. We present some inaccuracies ranging from lack of geographical information, imprecise and ambiguous toponyms, overlaps and repetitions as well as visualization to our data set in UK. We finally give an outlook into potential corrections. The work presented here is still part of on-going work. Results achieved so far do not address the final conclusions but form the basis for the formalization of a domain knowledge along with the services.

Discovering semantic relations from unstructured data for ontology enrichment: Asssociation rules based approach

Ontologies have been used in information system development as one of the main knowledge representation tool. Ontologies are composed by concepts, a hierarchy, arbitrary relations between concepts, and possibly other axioms. However, ontology building is a time-consuming process, and it should be supported by automatic finding of interesting, possible relationships among concepts. This paper describes how an analysis of co-occurrences of concepts in unstructured sources of information can be used to provide interesting relationships for enriching ontological structures. We apply association rule theory to construct ontological concept relations and evaluate the importance of such relations for supporting the building process of a domain ontology. Preliminary results were collected using scientific published papers from the building and construction sector, which were used as an input for applying our method. A knowledge browsing environment was developed in order to support the analysis process.

Integrated solution to support enterprise interoperability at the business process level on e-Procurement

Major trend in the global market is the increasing collaboration among enterprises during the entire product life-cycle, motivated by business drivers like cost reduction, efficiency, flexibility and the focus on core competencies. New solutions in e-Procurement of raw and semi-finished materials are likely to be developed in the coming years, as stakeholders realize the significant benefits of a seamless collaboration supported on interoperable ICT systems.

End-to-end manufacturing in factories of the future

In the last decades, manufacturing companies have increasingly outsourced many of their functions in order to focus on their core skills and competencies. Moreover, manufacturers often have to join together in dynamic manufacturing networks in order to respond to business demands or new opportunities. Given the heterogeneity of enterprises systems, knowledge representation methods and semantics, the management of such networks is not trivial. The lack of standard tools, methodologies, and a common semantic makes the creation and monitoring of such networks difficult. Therefore, the IMAGINE project defined a framework for an interoperable end-to-end manufacturing that potentially will become essential in factories of the future. As a consequence the same project implemented a platform based on such framework guidelines which provides a central repository and standardised search, configuration and simulation capabilities to enable an efficient creation, monitoring and management of dynamic manufacturing networks. The paper presents an extension to the IMAGINE platform to enable any manufacturer to integrate its systems with such platform, in a way that guarantees seamless interoperability, thus ensuring proper communication and data exchange between all the partners in a manufacturing network throughout the entire manufacturing life cycle, from supplier search to manufacturing execution and monitoring. The use of the proposed platform extension is exemplified through a use case scenario in the furniture manufacturing sector.

Document Clustering Using an Ontology-Based Vector Space Model

This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships semantic associations modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this work are the following: i conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; and ii implementation of a proof-of-concept, named SENSE Semantic Enrichment knowledge Sources.

An Architecture to Support Semantic Enrichment of Knowledge Sources in Collaborative Engineering Projects

This work brings a contribution focused on collaborative engineering projects where knowledge plays a key role in the process, aiming to support collaborative work carried out by project teams, through an ontology-based platform and a set of knowledge-enabled services. We introduce the conceptual approach, the technical architectural (and its respective implementation) supporting a modular set of semantic services based on individual collaboration in a project-based environment (for Building & Construction sector). The approach presented here enables the semantic enrichment of knowledge sources, based on project context. The main elements defined by the architecture are an ontology (to encapsulate human knowledge), a set of web services to support the management of the ontology and adequate handling of knowledge providing search/indexing capabilities (through statistical/semantically calculus), providing a systematic procedure for formally documenting and updating organizational knowledge. Results achieved so far and future goals pursued here are also presented.

An architecture for big data processing on intelligent transportation systems. An application scenario on highway traffic flows

The transportation sector, and in particularly intelligent transportation systems, generate large volumes of real-time data that needs to be managed, communicated, interpreted, aggregated, and analyzed. To this end, innovative big data processing and mining as well as optimization techniques, need to be developed and applied in order to support real-time decision-making capabilities. Towards this end, this paper presents an ETL (extract, transform and load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented here, is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. The proposed architecture relies on the adoption of “big data” technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators. The proposed architecture is capable of handling real-time and historical data using big data technologies such as Spark on Hadoop and MongoDB. The DATEX-II data model is adopted, in order to harmonize traffic data provided by the highway operators. The work presented here, is still part of ongoing work currently addressed under the EU H2020 OPTIMUM project. Preliminary results achieved so far do not address the final conclusions of the project, but enabled us to demonstrate considerable gains in performance, when compared to other traditional ETL approaches, and also form the basis for pointing out and discuss future work directions and opportunities in the area of the development of big data processing and mining methods under the ITS domain.

Personalized Intelligent Mobility Platform: An Enrichment Approach Using Social Media

This chapter aims to present a technical approach for developing a personalized mobility knowledge base supported by mechanisms for extracting and processing tweets related with traffic events, in order to support highly specific assistance and recommendations to urban commuters. In order to address a personalized mobility knowledge base, a step-wise approach is presented with the purpose of construction and enriching a knowledge model from heterogeneous data sources providing real-time information via Personal Digital Assistants (PDAs). The approach presented is decomposed into several steps, starting from data collection and knowledge base formalization targeting the development of a personalized intelligent route planner, enabling a more efficient decision support to urban commuters. The work presented here, is still part of ongoing work currently addressed under the EU FP7 MobiS project. Results achieved so far do not address the final conclusions of the project but form the basis for the formalization of the domain knowledge do be acquired.

A Conceptual Model of Farm Management Information System for Decision Support

In a today economy, it is crucial to have systems able to handle information with precision. In addition, it is also important to apply technological innovations in the various domains, with the objective to modernize and transform them to become more competitive. In this paper, a conceptual framework for a Farm Management Information System (FMIS) is presented. It is focused in the different ways of using the information coming from various sources as sensors to assist farmers in decision making of agriculture business.

e-Proc a TO BE Scenario for Business Interoperability

The ATHENA IP (www.athena-ip.org) and INTEROP NoE (www.interop-noe.org) have been analyzing the actual level of e-Business implementation in the furniture sector, a major SME-based industrial sector in Europe. The intent of this study is to put forward and validate a referential framework for a TO BE scenario suitable to improve the interoperability of data and services during e-Procurement activities. This paper, after describe a referential e-Procurement AS IS scenario, it presents the results of the study proposing an innovative TO BE scenario to facilitate the interoperability among e-Business services in SME-based industrial scenarios.