Synthetic Geo-referenced database generator

Geo-referenced transactional database

Geo-referenced temporal database

Geo-referenced utility database

Geo-referenced uncertain transactional database

Geo-referenced uncertain temporal database

Synthetic Geo-referenced transactional database generator

#import the algorithm
from PAMI.extras.syntheticDataGenerator import georeferencedTransactionalDatabase as gtdb

#specify the parameters
databaseSize = 100000
numberOfItems = 2000
averageLengthOfTransaction = 10

#bounding box
minimumXvalue = 0
minimumYvalue = 0
maximumXvalue = 100
maximumYvalue = 100
#The above four lines ensure that the spatial information of every item in the database lie within (0, 0) and (100, 100).

#initialize the algorithm
alg = gtdb.georeferencedTransactionalDatabase(databaseSize, numberOfItems, averageLengthOfTransaction)

#execute the algorithm
alg.generate()

#save the generated data in a file
alg.save(fileName)
alg.saveLocationData(fileName2)

#Get the generated data as a dataframe (Optional feature)
dataFrame = alg.getData()
locationDataFrame = alg.getLocationData()

Synthetic Geo-referenced temporal database generator

The code to generate a geo-referenced temporal database is as follows:

#import the algorithm
from PAMI.extras.syntheticDataGenerator import georeferencedTemporalDatabase as gtempdb

#specify the parameters
databaseSize = 100000
numberOfItems = 2000
averageLengthOfTransaction = 10

probabilityToMissNextTimestamp = 0
#  0 (or 0%)     - regular temporal database with uniform time gap between the transactions
# <1 (or <100%)  - irregular temporal database with non-uniform time gaps between the transactions

probabilityOfRecurrenceOfTimestampInNextTransaction = 20
# 0 (or 0%)  - every transaction will have a different timestamp
# 1 (or 100%) - every transaction will have the same timestamp


#bounding box
minimumXvalue = 0
minimumYvalue = 0
maximumXvalue = 100
maximumYvalue = 100
#The above four lines ensure that the spatial information of every item in the database lie within (0, 0) and (100, 100).


#initialize the algorithm
alg = gtempdb.georeferencedTemporalDatabase(databaseSize, numberOfItems, \
                        averageLengthOfTransaction, \
                        probabilityToMissNextTimestamp,\
                        probabilityToMissNextTimestamp, \
                        minimumXvalue, minimumYvalue,\
                        maximumXvalue,maximumYvalue)

#execute the algorithm
alg.generate()

#save the generated data in a file
alg.save(fileName)
alg.saveLocationData(fileName2) #optional

#Get the generated data as a dataframe (Optional features)
dataFrame = alg.getData()
locationDataFrame = alg.getLocationData()

Synthetic Geo-referenced utility database generator

#import the algorithm
from PAMI.extras.syntheticDataGenerator import georeferencedUtilityDatabase as gudb

#specify the parameters
databaseSize = 100000
numberOfItems = 2000
averageLengthOfTransaction = 10

minimumInternalUtilityValue = 1
maximumInternalUtilityValue = 100

minimumExternalUtilityValue = 1
maximumExternalUtilityValue = 10


#initialize the algorithm
alg = udb.utilityDatabase(databaseSize, numberOfItems, \
                        averageLengthOfTransaction,\
                        minimumInternalUtilityValue,\
                        maximumInternalUtilityValue,\
                        minimumExternalUtilityValue,\
                        maximumExternalUtilityValue)

#execute the algorithm
alg.generate()

#save the generated data in a file
alg.save(fileName1)
alg.saveItemsInternalUtilityValues(fileName2)
alg.saveItemsExternalUtilityValues(fileName3)

#Get the generated data as a dataframe (Optional feature)
utilityDF = alg.getUtilityData()
internalUDF = alg.getInternalUtilityData()
externalUDF = alg.getExternalUtilityData()