A software placement device of a software placement system includes an information acquiring unit configured to acquire a finally-found location and a finally-found time, a deployment destination determining unit configured to calculate a data presence probability indicating a probability of a search target being detected at a certain time using the finally-found location and the finally-found time, to calculate a total cost using the data presence probability calculated and a computational cost and a network cost of each of base computers, and to select a base computer and a time interval for which the calculated total cost is a minimum, and a software distributing unit configured to distribute software to the selected base computer and transmit an analysis instruction for data of the selected time interval to the selected base computer.

An error analysis device for an aerosol generating device includes: a communication interface configured to receive log data from the aerosol generating device; and a processor configured to determine at least one error that occurred in the aerosol generating device based on the log data that is received. The log data according to an embodiment may include logs corresponding to events that occurred in the aerosol generating device.

A log management system includes a first secret splitting module to split the log data generated regularly or intermittently into a plurality of split log fragments including a split log fragment for remote transmission per predetermined unit using secret splitting scheme, the split log fragment for remote transmission having a smaller size than remaining split log fragments; a first communication control module to transmit the split log fragment for remote transmission to a remote apparatus; a second communication control module to receive, in response to occurrence of an event, one or more remaining split log fragments corresponding to the log data to be partially recovered out of the remaining split log fragments accumulated by a local apparatus; and a second secret splitting module to recover the log data from the one or more remaining split log fragments and the split log fragment for remote transmission using the secret splitting scheme.

A log management system includes a first secret splitting module to split the log data generated regularly or intermittently into a plurality of split log fragments including a split log fragment for remote transmission per predetermined unit using secret splitting scheme, the split log fragment for remote transmission having a smaller size than remaining split log fragments; a first communication control module to transmit the split log fragment for remote transmission to a remote apparatus; a second communication control module to receive, in response to occurrence of an event, one or more remaining split log fragments corresponding to the log data to be partially recovered out of the remaining split log fragments accumulated by a local apparatus; and a second secret splitting module to recover the log data from the one or more remaining split log fragments and the split log fragment for remote transmission using the secret splitting scheme.

The disclosed privacy management platforms are adapted to scan any number of data sources in order to provide users with visibility into stored personal information, risk associated with storing such information and/or usage activity relating to such information. The platforms may correlate personal information findings to specific data subjects and may employ machine learning models to classify findings as corresponding to a particular personal information attribute to provide an indexed inventory across multiple data sources.

Auditing information is captured from a processing stack of an invoked application. An annotation customized for that invocation context is processed to filter and/or add additional audition information available from the processing stack. The customized auditing information is then sent to a destination based on a processing context of the invoked application when the invoked application completes processing. In an embodiment, the customized auditing information is housed in a data store and an interface is provided for customized query processing, report processing, event processing, a notification processing.

A computer-implemented method comprises receiving a plurality of sampled data points, each data point including a y value and a t value; defining a plurality of bins; defining an array of elements; dividing the sampled data points into a plurality of sections; assigning a plurality of polynomial equations, one polynomial equation to each section, each polynomial equation having a waveform that fits the data points of the associated section; determining a plurality of section bin times, one section bin time for each bin in each section, each section bin time determined using the polynomial equation and indicating an amount of time that the waveform has values in the range of one of the bins; and adding the section bin time for each bin in each section to the histogram data in the array element pointed to by the number of the bin.

A computer-implemented method comprises receiving a plurality of sampled data points, each data point including a y value and a t value; defining a plurality of bins; defining an array of elements; dividing the sampled data points into a plurality of sections; assigning a plurality of polynomial equations, one polynomial equation to each section, each polynomial equation having a waveform that fits the data points of the associated section; determining a plurality of section bin times, one section bin time for each bin in each section, each section bin time determined using the polynomial equation and indicating an amount of time that the waveform has values in the range of one of the bins; and adding the section bin time for each bin in each section to the histogram data in the array element pointed to by the number of the bin.

For each of operations each included in at least one of a plurality of flow instances generated based on an operation log of a terminal, a counting unit (104) counts the number of flow instances each including the same operation. Also, the extraction unit (105) extracts operations included commonly in the flow instances the number of which is larger than a predetermined criterion as counted by the counting unit (104), in order of occurrence time.

For each of operations each included in at least one of a plurality of flow instances generated based on an operation log of a terminal, a counting unit (104) counts the number of flow instances each including the same operation. Also, the extraction unit (105) extracts operations included commonly in the flow instances the number of which is larger than a predetermined criterion as counted by the counting unit (104), in order of occurrence time.