Storing RDF as a Graph

RDF is the first W3C standard for enriching information resources of the Web with detailed meta data. The semantics of RDF data is defined using a RDF schema. The most expressive language for querying RDF is RQL, which enables querying of semantics. In order to support RQL, a RDF storage system has to map the RDF graph model onto its storage structure. Several storage systems for RDF data have been developed, which store the RDF data as triples in a relational database. To evaluate an RQL query on those triple structures, the graph model has to be rebuilt from the triples. In this paper, we presented a new approach to store RDF data as a graph in a object-oriented database. Our approach avoids the costly rebuilding of the graph and efficiently queries the storage structure directly. The advantages of our approach have been shown by performance test on our prototype implementation OO-Store

Building Occupancy Simulation and Data Assimilation Using a Graph Based Agent Oriented Model

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates the real time spatial distribution of occupants in a building. It can benefit various applications like conserving energy, smart assist, building construction, crowd management, and emergency evacuation. Building occupancy simulation and estimation needs a simulation model and a data assimilation algorithm that assimilates real-time sensor data into the simulation model. Existing build occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating a large number occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of the existing models, in this dissertation, we combine the benefits of agent and graph based modeling and develop a new graph based agent oriented model which can efficiently simulate a large number of occupants in various building structures. To support real-time occupancy dynamics estimation, we developed a data assimilation framework based on Sequential Monte Carol Methods, and apply it to the graph-based agent oriented model to assimilate real time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this dissertation work include 1) it provides an efficient model for building occupancy simulation which can accommodate thousands of occupants; 2) it provides an effective data assimilation framework for real-time estimation of building occupancy

Default contagion risks in Russian interbank market

Systemic risks of default contagion in the Russian interbank market are investigated. The analysis is based on considering the bow-tie structure of the weighted oriented graph describing the structure of the interbank loans. A probabilistic model of interbank contagion explicitly taking into account the empirical bow-tie structure reflecting functionality of the corresponding nodes (borrowers, lenders, borrowers and lenders simultaneously), degree distributions and disassortativity of the interbank network under consideration based on empirical data is developed. The characteristics of contagion-related systemic risk calculated with this model are shown to be in agreement with those of explicit stress tests.Comment: Final version, to appear in Physica

How can graph databases and reasoning be combined and integrated?

Nowadays the graph data model has been accepted as one of the most suitable data models to formalize relationships among entities of many domains. Deductive databases based on the Datalog language have been used to deduce new information from large amounts of data. Most of the attempts to combine logic and graph databases are based on translating knowledge in graph databases into Datalog and then use its inference engine. We aim to open the discussion about combining graph databases and a graph-oriented logic to define «native» deductive graph databases. This is, graph databases equipped with an inference mechanism based on graph based logic. To be concrete, we plan to use the recently introduced graph navigational logic.Peer ReviewedPostprint (published version

Bases de dados em grafos: Contextualização e estudo exploratório

After several decades of great success and good services to organizations, relational database technology has been challenged by a new class of database technologies usually called NoSQL (Not only SQL). The recent developments in the area called Big Data contributed decisively to this situation, in which the traditional relational model began to present difficulties, due to the complexity and large volumes of data. Within this new class of databases, different proposals, with several origins and application areas, appeared in four groups, according to their data model: column oriented, document oriented, key-value and Graphs oriented. In particular, graph databases provide a set of characteristics to represent relationships between data that no other model can represent so well. As we live in a world where information is all connected, this database model has what it takes to be successful. In this way, some examples of graph database applications will be discussed as well as demonstrations of the facility to construct queries, which would be extremely complex if they were developed in SQL over relational databases.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013