Mueen
Abdullah Mueen, Albuquerque, NM US
Patent application number | Description | Published |
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20160070709 | ONLINE REVIEW ASSESSMENT USING MULTIPLE SOURCES - Multiple sources of reviews for the same product or service (e.g. hotels, restaurants, clinics, hair saloon, etc.) are utilized to provide a trustworthiness score. Such a score can clearly identify hotels with evidence of review manipulation, omission and fakery and provide the user with a comprehensive understanding of the reviews of a product or establishment. Three types of information are used in computing the score: spatial, temporal and network or graph-based. The information is blended to produce a representative set of features that can reliably produce the trustworthiness score. The invention is self-adapting to new reviews and sites. The invention also includes a validation mechanism by crowd-sourcing and fake review generation to ensure reliability and trustworthiness of the scoring. | 03-10-2016 |
Abdullah Al Mueen, Seattle, WA US
Patent application number | Description | Published |
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20130290350 | Similarity Search Initialization - A similarity search initialization system includes a leaf selector to select a leaf of a suffix tree generated from a target string representing a target sequence. The selected leaf is associated with a prefix in the suffix tree having a longest match to a suffix of a query string representing a query. The system further includes a distance module to determine a distance between the query and a subsequence of the target sequence represented by a candidate substring of the target string. The candidate substring includes the prefix associated with the selected leaf. The determined distance is to provide an initial upper bound in a similarity search of the target sequence using the query. | 10-31-2013 |
Abdullah Al Mueen, Albuquerque, NM US
Patent application number | Description | Published |
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20150377938 | SEASONALITY DETECTION IN TIME SERIES DATA - A system that uses power spectrum analysis and auto-correlation function analysis to perform seasonality estimation of time series data. A power spectrum analyzer calculates and analyzes a power spectrum of a received time series data. An auto-correlation function analyzer calculates at least one auto-correlation function of the received time series, and generates a resulting set of one or more candidate seasonalities. A seasonality estimator estimates one or more seasonalities of the received time series using at least a portion of the analyzed result from the power spectrum analyzer and using the set of one or more candidates generated by the auto-correlation function analyzer. Accordingly, the estimation of candidate seasonality uses both auto-correlation and power spectrum analysis, thereby at least in some circumstances improving the seasonality estimation compared to auto-correlation function analysis alone or power spectrum analysis alone. | 12-31-2015 |