Storage resource usage amount(s) are determined. For instance, storage resource usage data in a historical period related to a user is acquired. The pattern information of the storage resource usage data is determined according to a time series of the storage resource usage data, the time series being a series of observed values of the storage resource usage data in the historical period. In addition, the storage resource usage amount for a target period of the user can be determined based on the pattern information and the storage resource usage data. The pattern information at least includes at least one of a trend pattern, a cycle pattern, or an irregular pattern. Beneficially, a storage resource usage amount of a user can be more accurately determined in a future period, thereby providing the user with valuable reference information.

Storage resource usage amount(s) are determined. For instance, storage resource usage data in a historical period related to a user is acquired. The pattern information of the storage resource usage data is determined according to a time series of the storage resource usage data, the time series being a series of observed values of the storage resource usage data in the historical period. In addition, the storage resource usage amount for a target period of the user can be determined based on the pattern information and the storage resource usage data. The pattern information at least includes at least one of a trend pattern, a cycle pattern, or an irregular pattern. Beneficially, a storage resource usage amount of a user can be more accurately determined in a future period, thereby providing the user with valuable reference information.

Artificial neural network systems involve the receipt by a computing device of input data that defines a pattern to be recognized (such as faces, handwriting, and voices). The computing device may then decompose the input data into a first subband and a second subband, wherein the first and second subbands include different characterizing features of the pattern in the input data. The first and second subbands may then be fed into first and second neural networks being trained to recognize the pattern. Reductions in power expenditure, memory usage, and time taken, for example, allow resource-limited computing devices to perform functions they otherwise could not.

A prediction device f includes an acquisition unit and a prediction unit. The acquisition unit is configured to acquire attribute data pertaining to the plurality of exhibits and the number of visitors to each of the plurality of exhibits during a first period in the display area in which the plurality of exhibition articles are exhibited. The prediction unit is configured to predict a future flow of persons to the plurality of exhibits by a prediction model. The prediction model is generated using attribute data for a second period that is a period previous to the first period, graph time-series data pertaining to a change over time in a movement pattern for each of the plurality of visitors to the plurality of exhibits during the second period, the number of visitors to the exhibits, and the number of visitors to each of the plurality of exhibits.

The present disclosure relates to computer-implemented methods, software, and systems for identifying cyclic patterns in data observations collected as time series with irregular time spacing between each other. Distribution of the time occurrences associated with the data observations is analyzed to identify a cyclic pattern. A list of time gaps between each of the data observations is defined. Time gaps are defined according to a common time measure. The time gaps of the list of time gaps are evaluated using two reader operators that separately browse through the list of time gaps. A cyclic pattern is identified in the list of time gaps based on the iteratively evaluating. The identifying comprises identifying (i) a length of cycle within the cycle patterns and (ii) an index element of a time gap of the list of time gaps.

The present disclosure relates to computer-implemented methods, software, and systems for identifying cyclic patterns in data observations collected as time series with irregular time spacing between each other. Distribution of the time occurrences associated with the data observations is analyzed to identify a cyclic pattern. A list of time gaps between each of the data observations is defined. Time gaps are defined according to a common time measure. The time gaps of the list of time gaps are evaluated using two reader operators that separately browse through the list of time gaps. A cyclic pattern is identified in the list of time gaps based on the iteratively evaluating. The identifying comprises identifying (i) a length of cycle within the cycle patterns and (ii) an index element of a time gap of the list of time gaps.

A device is provided. The device includes processing circuitry configured to detect an audio signal associated with an alarm event, generate a spectrogram using a plurality of input samples of the audio signal where the spectrogram includes a plurality of spectral bins, select one of the plurality of spectral bins associated with a predefined frequency range, perform edge detection on the selected one of the plurality of spectral bins, perform pattern detection based on the edge detection, determine the audio signal corresponds to an audio alarm signal based on the pattern detection, and trigger an action in response to determining that the audio signal corresponds to an audio alarm signal.

A device is provided. The device includes processing circuitry configured to detect an audio signal associated with an alarm event, generate a spectrogram using a plurality of input samples of the audio signal where the spectrogram includes a plurality of spectral bins, select one of the plurality of spectral bins associated with a predefined frequency range, perform edge detection on the selected one of the plurality of spectral bins, perform pattern detection based on the edge detection, determine the audio signal corresponds to an audio alarm signal based on the pattern detection, and trigger an action in response to determining that the audio signal corresponds to an audio alarm signal.

One example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.

One example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.