Abstract: The present invention extends to systems and methods for notifying entities of relevant events. An entity defines a rule formula that is triggered when one or more detected events match the rule formula including defining one or more event types and one or more locations types. A boundary associated with a selected location type is also received along with a monitoring area. The received elements are combined into the rule formula notification preferences are associated with the defined rule. One or more events are detected and then compared to the rule formula to determine if the combination of one or more event types occurred within the boundary of the one or more location types within the area.

Abstract: The present invention extends to methods, systems, and computer program products for detecting events from features derived from ingested signals. In one aspect, an event detection infrastructure determines that characteristics of multiple (and possibly private and/or non-private) signals, when considered collectively, indicate an event of interest to one or more parties. In another aspect, an evaluation module determines that characteristics of one or more (possibly private and/or non-private) signals indicate a possible event of interest to one or more parties. A validator then determines that characteristics of one or more other (possibly private and/or non-private) signals validate the possible event as an actual event of interest to the one or more parties. Signal features can be used to compute probabilities of events occurring.

Abstract: The present invention extends to methods, systems, and computer program products for ingesting streaming signals. Signal ingestion modules sample a frame from a raw streaming signal. A preliminary severity or possible event type is computed from the sampled frame. A deeper inspection request is triggered of the raw streaming signal or of another raw streaming signal. Segments of content from the raw streaming signal or the other raw streaming signal are inspected. An actual severity or actual event type is computed from the inspected content. The actual severity or actual event type can be included in a normalized signal.

Abstract: The present invention extends to methods, systems, and computer program products for normalizing ingested signals. In general, different types of raw signals including source data in different pluralities of data dimensions and including other characteristics are ingested. Per raw signal, a transdimensionality transform is applied to recode and normalize the source data into a normalized signal that includes normalized data in a common reduced plurality of dimensions including a time dimension, a location dimension, and a context dimension. Normalization can include inferring signal annotations from the source data and using the annotations and/or the other characteristics to derive time, location, and context dimensions. Derivation can include computing a probability of a real-world event and including the probability in the context dimension. An real-world event is detection from the normalized data in the time, location, and context dimensions and an entity is notified of the real-world event.

Abstract: The present invention extends to methods, systems, and computer program products for ingesting streaming signals. Signal ingestion modules sample a frame from a raw streaming signal. A preliminary severity or possible event type is computed from the sampled frame. A deeper inspection request is triggered of the raw streaming signal or of another raw streaming signal. Segments of content from the raw streaming signal or the other raw streaming signal are inspected. An actual severity or actual event type is computed from the inspected content. The actual severity or actual event type can be included in a normalized signal.

Abstract: The present invention extends to methods, systems, and computer program products for normalizing ingested signals. In general, signal ingestion modules ingest different types of raw structured and/or raw unstructured signals on an ongoing basis. The signal ingestion modules normalize raw signals into normalized signals having a Time, Location, Context (or “TLC”) dimensions. A Time (T) dimension can be a time of origin or alternatively “event time” of a signal. A Location (L) dimension can be anywhere across a geographic area, such as, a country (e.g., the United States), a State, a defined area, an impacted area, an area defined by a geo cell, an address, etc. A Context (C) dimension indicates circumstances surrounding formation/origination of a raw signal in terms that facilitate understanding and assessment of the raw signal. The Context (C) dimension of a raw signal can be derived from express as well as inferred signal features of the raw signal.

Abstract: The present invention extends to methods, systems, and computer program products for ingesting streaming signals. Signal ingestion modules sample a plurality of frames contained in a raw streaming signal. The signal ingestion modules separate the raw streaming signal into a plurality of segments. It is determined that content in the plurality of sampled frames indicates a possible event type. The signal ingestion modules replay a segment, from among the plurality of segments, in response to determining the indicated possible event. The content of the segment is inspected during replay of the segment. The possible event type is confirmed or not confirmed as an actual event type based on the inspection. When confirmed, a context dimension of the streaming signal is updated to include the event type.