Hamid Haidarian Shahri

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Data cleaning deals with the detection and removal of errors and inconsistencies in data, gathered from distributed sources. This process is essential for drawing correct conclusions from data in decision support systems. Eliminating fuzzy duplicate records is a fundamental part of the data cleaning process. The vagueness and uncertainty involved in(More)
Vehicle tracking has a wide variety of applications from law enforcement to traffic planning and public safety. However, the image resolution of the videos available from most traffic camera systems, make it difficult to track vehicles based on unique identifiers like license plates. In many cases, vehicles with similar attributes are indistinguishable from(More)
D ata cleaning is an inevitable problem when integrating data from distributed operational databases, 1 because no unified set of standards spans all the distributed sources. One of the most challenging phases of data cleaning is removing fuzzy duplicate records. Approximate or fuzzy duplicates pertain to two or more tuples that describe the same real-world(More)
What commonsense knowledge do intelligent systems need, in order to recover from failures or deal with unexpected situations? It is impractical to represent predetermined solutions to deal with every unanticipated situation or provide predetermined fixes for all the different ways in which systems may fail. We contend that intelligent systems require only a(More)
Currently, our interactions with devices are constrained, as we need to program/configure devices, primarily through some artificial interface, instead of interacting through a dialog agent. This limitation in human-device interactions is a major obstacle to the integration of devices (e.g. PDA, GPS) in our daily activities. Considering the numerous(More)
In this paper, when we use the term ontology, we are primarily referring to linked data in the form of RDF(S). The problem of ontology mapping has attracted considerable attention over the last few years, as the deployment of ontologies is increasing with the advent of the Web of Data. We identify two sharply distinct goals for ontology mapping, based on(More)
The problem of ontology mapping has attracted considerable attention over the last few years, as the usage of ontologies is increasing. In this paper, we revisit the fundamental assumptions that drive the mapping process. Based on real-world use cases, we identify two distinct goals for mapping, which are: (i) ontology development and (ii) facilitating(More)
AbstrAct Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and warehousing, where data are gathered from distributed and inconsistent sources. Learnable string similarity measures are an active area of research in the entity resolution problem. Our proposed framework(More)