Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Knowledge discovery for moderating collaborative projects

In today's global market environment, enterprises are increasingly turning towards collaboration in projects to leverage their resources, skills and expertise, and simultaneously address the challenges posed in diverse and competitive markets. Moderators, which are knowledge based systems have successfully been used to support collaborative teams by raising awareness of problems or conflicts. However, the functioning of a moderator is limited to the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update the corresponding expert module. The architecture for the Universal Knowledge Moderator (UKM) shows how the existing moderators can be extended to support global manufacturing. A method for designing and developing the knowledge acquisition module of the Moderator for manual and semi-automatic update of knowledge is documented using the Unified Modelling Language (UML). UML has been used to explore the static structure and dynamic behaviour, and describe the system analysis, system design and system development aspects of the proposed KOATING framework. The proof of design has been presented using a case study for a collaborative project in the form of construction project supply chain. It has been shown that Moderators can "learn" by extracting various kinds of knowledge from Post Project Reports (PPRs) using different types of text mining techniques. Furthermore, it also proposed that the knowledge discovery integrated moderators can be used to support and enhance collaboration by identifying appropriate business opportunities and identifying corresponding partners for creation of a virtual organization. A case study is presented in the context of a UK based SME. Finally, this thesis concludes by summarizing the thesis, outlining its novelties and contributions, and recommending future research

Database marketing intelligence methodology supported by ontologies and knowlegde discovery in databases

Tese de doutoramento em Tecnologias e Sistemas de InformaçãoActualmente as organizações actuam em ambientes caracterizados pela inconstância, elevada competitividade e pressão no desenvolvimento de novas abordagens ao mercado e aos clientes. Nesse contexto, o acesso à informação, o suporte à tomada de decisão e a partilha de conhecimento tornam-se essenciais para o desempenho organizativo. No domínio do marketing têm surgido diversas abordagens para a exploração do conteúdo das suas bases de dados. Uma das abordagens, utilizadas com maior sucesso, tem sido o processo para a descoberta de conhecimento em bases de dados. Por outro lado, a necessidade de representação e partilha de conhecimento tem contribuído para um crescente desenvolvimento das ontologias em áreas diversas como sejam medicina, aviação ou segurança. O presente trabalho cruza diversas áreas: tecnologias e sistemas de informação (em particular a descoberta de conhecimento), o marketing (especificamente o database marketing) e as ontologias. O objectivo principal desta investigação foca o papel das ontologias em termos de suporte e assistência ao processo de descoberta de conhecimento em bases de dados num contexto de database marketing. Através de abordagens distintas foram formuladas duas ontologias: ontologia para o processo de descoberta de conhecimento em bases de dados e, a ontologia para o processo database marketing suportado na extracção de conhecimento em bases de dados (com reutilização da ontologia anterior). O processo para licitação e validação de conhecimento, baseou-se no método de Delphi (ontologia de database marketing) e no processo de investigação baseada na revisão de literatura (ontologia de descoberta de conhecimento). A concretização das ontologias suportou-se em duas metodologias: metodologia methontology, para a ontologia de descoberta de conhecimento e metodologia 101 para a ontologia de database marketing. A última, evidencia a reutilização de ontologias, viabilizando assim a reutilização da ontologia de descoberta de conhecimento na ontologia de database marketing. Ambas ontologias foram desenvolvidas sobre a ferramenta Protege-OWL permitindo não só a criação de toda a hierarquia de classes, propriedades e relações, como também, a realização de métodos de inferência através de linguagens baseadas em regras de Web semântica. Posteriormente, procedeu-se à experimentação da ontologia em casos práticos de extracção de conhecimento a partir de bases de dados de marketing. O emprego das ontologias neste contexto de investigação, representa uma abordagem pioneira e inovadora, uma vez que são propostas para assistirem em cada uma das fases do processo de extracção de conhecimento em bases de dados através de métodos de inferência. È assim possível assistir o utilizador em cada fase do processo de database marketing em acções tais como de selecção de actividades de marketing em função dos objectivos de marketing (e.g., perfil de cliente), em acções de selecção dados (e.g., tipos de dados a utilizar em função da actividade a desenvolver) ou mesmo no processo de selecção de algoritmos (e.g. inferir sobre o tipo de algoritmo a usar em função do objectivo definido). A integração das duas ontologias num contexto mais lato permite, propor uma metodologia com vista ao efectivo suporte do processo de database marketing baseado no processo de descoberta de conhecimento em bases de dados, denominado nesta dissertação como: Database Marketing Intelligence. Para a demonstração da viabilidade da metodologia proposta foi seguido o método action-research com o qual se observou e testou o papel das ontologias no suporte à descoberta de conhecimento em bases de dados (através de um caso prático) num contexto de database marketing. O trabalho de aplicação prática decorreu sobre uma base de dados real relativa a um cartão de fidelização de uma companhia petrolífera a operar em Portugal. Os resultados obtidos serviram para demonstrar em duas vertente o sucesso da abordagem proposta: por um lado foi possível formalizar e acompanhar todo o processo de descoberta de conhecimento em bases de dados; por outro lado, foi possível perspectivar uma metodologia para um domínio concreto suportado por ontologias (suporte á decisão na selecção de métodos e tarefas) e na descoberta de conhecimento em bases de dados.Nowadays, the environment in which companies work is turbulent, very competitive and pressure in the development of new approaches to the market and clients. In this context, the access to information, the decision support and knowledge sharing become essential for the organization performance. In the marketing domain several approaches for the exploration of database exploration have emerged. One of the most successfully used approaches has been the knowledge discovery process in databases. On the other hand, the necessity of knowledge representation and sharing and contributed to a growing development of ontologies in several areas such as in the medical, the aviation or safety areas. This work crosses several areas: technology and information systems (specifically knowledge discovery in databases), marketing (specifically database marketing) and ontologies in general. The main goal of this investigation is to focus on the role of ontologies in terms of support and aid to the knowledge discovery process in databases in a database marketing context. Through distinct approaches two ontologies were created: ontology for the knowledge discovery process in databases, and the ontology for the database marketing process supported on the knowledge extraction in databases (reusing the former ontology). The elicitation and validation of knowledge process was based on the Delphi method (database marketing ontology) and the investigation process was based on literature review (knowledge discovery ontology). The carrying out of both ontologies was based on two methodologies: methontology methodology, for the knowledge discovery process and 101 methodology for the database marketing ontology. The former methodology, stresses the reusing of ontologies, allowing the reusing of the knowledge discovery ontology in the database marketing ontology. Both ontologies were developed with the Protege-OWL tool. This tool allows not only the creation of all the hierarchic classes, properties and relationships, but also the carrying out of inference methods through web semantics based languages. Then, the ontology was tested in practical cases of knowledge extraction from marketing databases. The application of ontologies in this investigation represents a pioneer and innovative approach, once they are proposed to aid and execute an effective support in each phase of the knowledge extraction from databases in the database marketing context process. Through inference processes on the knowledge base created it was possible to assist the user in each phase of the database marketing process such as, in marketing activity selection actions according to the marketing objectives (e.g., client profile) or in data selection actions (e.g., type of data to use according to the activity to be preformed. In relation to aid in the knowledge discovery process in databases, it was also possible to infer on the type of algorithm to use according to the defined objective or even according to the type of data pre-processing activities to develop regarding the type of data and type of attribute information. The integration of both ontologies in a more general context allows proposing a methodology aiming to the effective support of the database marketing process based on the knowledge discovery process in databases, named in this dissertation as: Database Marketing Intelligence. To demonstrate the viability of the proposed methodology the action-research method was followed with which the role of ontologies in assisting knowledge discovery in databases (through a practical case) in the database marketing context was observed and tested. For the practical application work a real database about a customer loyalty card from a Portuguese oil company was used. The results achieved demonstrated the success of the proposed approach in two ways: on one hand, it was possible to formalize and follow the whole knowledge discovery in databases process; on the other hand, it was possible to perceive a methodology for a concrete domain supported by ontologies (support of the decision in the selection of methods and tasks) and in the knowledge discovery in databases.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/36541/200

How you move reveals who you are: understanding human behavior by analyzing trajectory data

The widespread use of mobile devices is producing a huge amount of trajectory data, making the discovery of movement patterns possible, which are crucial for understanding human behavior. Significant advances have been made with regard to knowledge discovery, but the process now needs to be extended bearing in mind the emerging field of behavior informatics. This paper describes the formalization of a semantic-enriched KDD process for supporting meaningful pattern interpretations of human behavior. Our approach is based on the integration of inductive reasoning (movement pattern discovery) and deductive reasoning (human behavior inference). We describe the implemented Athena system, which supports such a process, along with the experimental results on two different application domains related to traffic and recreation management