Extracting Assumptions from Incomplete Data

Extracting Assumptions from Missing Data

Information integration is the task of aggregating data from multiple heterogeneous data sources. The understandings of context knowledge of data sources are often the keys to challenging problems in information integration such as handling missing and inconsistent data. Context logic provides a unified framework for the modeling of data sources; nevertheless, the acquisition of large amounts o...

Image Reconstruction from Incomplete Data

پیش فرض ها و ارزش های فرهنگ سازمانی اسلامی: پژوهشی در چارچوب مدل فرهنگ سازمانی شاین

Edgar Schein has introduced a model in organizational culture. He claims that the origin of every culture is its assumptions which are bases for that culture’s values. Culture symbols, then, will be unfolded based on those values which have derived from culture’s assumptions. In this paper, in order to approach “Complete Organization” type, based on Schein’s model, the assumptions and values of...

Extracting reliable data from the fetal MCG

Extracting reliable information from a fetal MCG measured before the 24 week of gestation is hampered due to the poor signal-to-noise ratio. Thence, the recorded signals need to be processed in order to separate noise and signal, and need to be displayed in such a way that a reliable diagnosis can be made. No signal processing can be performed without making assumptions on either noise or signa...

Extracting regular mobility patterns from sparse CDR data without a priori assumptions

In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fac...