Seyed Iman Mirrezaei

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There are millions of web tables with geographic data that are pertinent for big data integration in a variety of domain applications, such as urban sustainability, transportation networks, policy studies, and public health. These tables, however, are heterogeneous in structure, concepts, and metadata. One of the challenges in semantically extracting(More)
One of the challenges of P2P systems is to perform load balancing efficiently. A distributed hash table (DHT) abstraction, heterogeneous nodes, and non uniform distribution of objects cause load imbalance in structured P2P overlay networks. Several solutions are suggested to solve this problem but they have some restrictions. They assume the homogeneous(More)
Discovering knowledge from textual sources and subsequently expanding the coverage of knowledge bases like DBPedia or Freebase currently requires either extensive manual work or carefully designed information extractors. Information extractors capture triples from textual sentences. Each triple consists of a subject, a predicate/property, and an object.(More)
This paper describes T<scp>riplex</scp>-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. T<scp>riplex</scp>-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples(More)
Data load balancing is a challenging task in the P2P systems. Distributed hash table (DHT) abstraction, heterogeneous nodes, and non uniform distribution of objects are the reasons to cause load imbalance in structured P2P overlay networks. Previous works solved the load balancing problem by assuming the homogeneous capabilities of nodes, unawareness of the(More)
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