Abstract:
Embodiments of the present invention are directed to a computer-implemented method for proactive rich text format management. Aspects include obtaining a content item to be displayed to a user and obtaining a preferred rich text format template for the user. Aspects also include applying the preferred rich text format template to the content item to create a customized view of the content item and displaying the customized view of the content item to the user.
Abstract:
Predicting power usage of a chip may include receiving placement data describing a placement, within the chip, of a plurality of logical components of the chip; providing the placement data as an input to a neural network; and determining, by the neural network, based on the placement data, a predicted power usage of the chip.
Abstract:
The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.
Abstract:
The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.
Abstract:
A method for processing a time series includes dividing, with a processing device, the time series into a plurality of windows by time; extracting at least one group of similar subsequences from a current window among the plurality of windows; and updating a candidate list on the basis of comparison between similar subsequences in each group of the at least one group with k characteristic subsequences in the candidate list; wherein the k characteristic subsequences are k characteristic subsequences with a greatest number of occurrences in at least processed parts of the time series.
Abstract:
Predicting power usage of a chip may include receiving placement data describing a placement, within the chip, of a plurality of logical components of the chip; providing the placement data as an input to a neural network; and determining, by the neural network, based on the placement data, a predicted power usage of the chip.