Abstract: A method and a system for automatically detecting anomalies present within a dataset is provided. An embodiment performs analysis on detected anomalies to calculate severity and detects root cause of the anomalies. The dataset may contain sales, revenue, and profit data. The anomalies are detected may include a historical data deviation anomaly, a latest data deviation anomaly, a data add anomaly, or a data loss anomaly. Severity of the detected anomalies is calculated by a severity calculation scheme. The severity calculation uses several parameters in calculating the severity rank of the anomalies. A root cause of the detected anomalies is identified by lookup to detect probable reasoning behind the detected anomalies. Further, the dataset may be used to create a dashboard to visualize business insights.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.