Abstract: This disclosure describes technologies for pattern matching in data streams. Given one or more patterns of data, and one or more data streams, e.g., streams of measurements made by one or more sensors, this disclosure provides apparatus and techniques for identifying occurrences of the patterns in the data streams. Techniques according to this disclosure generally make use of distribution strategies, as disclosed herein, to distribute the work of pattern identification among multiple processors. Data streams may be divided into multiple segments, and patterns for identification as well as data stream segments may be distributed among the multiple processors.

Abstract: Bots are detected real-time by correlating activity between users using a lag-sensitive hashing technique that captures warping-invariant correlation. Correlated users groups in social media may be found that represent bot behavior with thousands of bot accounts detected in a couple of hours.

Abstract: 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.

Abstract: Bots are detected real-time by correlating activity between users using a lag-sensitive hashing technique that captures warping-invariant correlation. Correlated users groups in social media may be found that represent bot behavior with thousands of bot accounts detected in a couple of hours.

Abstract: 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.

Abstract: A lowest common ancestor of a first data sequence and a second data sequence is determined. Based on the lowest common ancestor, symbols that differ between the first data sequence and the second data sequence are identified. A distance between the first data sequence and the second data sequence is determined based on the symbols.