Abstract: Embodiments are configured to provide dynamic thresholds for alerting users of anomalous resource usage of computing resources. The dynamic thresholds may be based on the historical behavior of compute metrics (or a time series obtained therefor) associated with the computing resources and a detected seasonality in that time series. The seasonality is detected based on an analysis of several, different time series combinations that are based on the original time series, which advantageously increases the probability of successful seasonality detection. Based on characteristics of the time series, a model for generating dynamic thresholds may be determined. The dynamic thresholds track the detected seasonality of the computing metrics, rather than being a static threshold. As utilization of the computing resources continues, the determined thresholds are applied to the compute metrics. If the determined thresholds are exceeded, an alert indicating an anomalous resource usage may be provided to a user.

Abstract: An information governance system comprising a plurality of classifiers which employ cutoffs for classifying at least a portion of a population of incoming documents as documents to be retained and documents to be discarded in accordance with a corresponding plurality of pre-defined retention schedules; training apparatus for training said classifiers based on relevance inputs provided by a human information governance expert regarding a training set of documents within a universe of documents to be governed; and apparatus operative to automatically cause any classified document to be retained and subsequently discarded in accordance with its pre-defined retention schedule including discarding only documents that (a) have been classified as documents to be discarded and (b) have not been classified as documents to be retained, and to automatically cause any document which could not be classified, to be retained as gray area data until further notice.

Abstract: A system including an electronic repository having a multiplicity of accesses to a respective multiplicity of electronic documents and metadata; a document rater using a processor to run a first computer algorithm on the multiplicity of electronic documents which yields a score which rates each of the multiplicity of electronic documents to an issue; and a metadata-based document discriminator to run a second computer algorithm on at least some of the metadata which yields leads, each lead having at least one metadata value for at least one metadata parameter, whose value correlates with the score of the electronic documents to the issue, typically used in combination with an electronic document analysis method receiving N electronic documents pertaining to a case encompassing a set of issues including at least one issue and establishing relevance of at least the N documents to at least one individual issue in the set of issues.