The temporal feature integrated temporal attributes into Odysseus. These can be useful to predict unknown or future values of an attribute that develops over time. The temporal feature brings a complete semantics for temporal processing and is the foundation for spatio-temporal processing in Odysseus. The temporal feature is closely related to the moving object algebra by Güting et al. The implementation is a prototype and can be buggy in some places. This article is meant to give you an overview if you want to use or extend this part of Odysseus.
Temporal Attributes
Temporal attributes are attributes that not only have one value, but one value for each point in time for a certain time interval. In other words, a temporal attribute of a certain type is a function that for each point in time returns a value of its type. Take a temporal integer over the interval [0,5) as an example. It could be the function f(t) = 5 * t. Then, it has five values, one for each point in the time interval: 0→ 0, 1→ 5, 2→ 10, 3→ 15, 4→ 20. A temporal integer can be denoted as tinteger, a temporal boolean as tboolean and so on.
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- A union merge function for the PredictionTimes is applied. This leads to a result that ignores if some elements that participate in an aggregation are not valid in the prediction time dimension at some points in time, which is probably what the user wants. An intersection could be useful in some situations, but is not the default option in the implementation.
- Most aggregation functions are incremental functions. Currently, only those are supported to work with temporal attributes. They are translated to a
TemporalIncrementalAggregationFunction, which creates a temporal output for the function.
Examples
Text
Energy Consumption
Text
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#PARSER PQL
#DOREWRITE false
#DEFINE PATH_LOCAL '/media/mydata/dev/odysseus_workspace/phd-workspace/Moving Object/Evaluation/scenarios/energy/energy_data.csv'
#ADDQUERY
input = ACCESS({
source='households',
wrapper='GenericPull',
schema=[
['id','Integer'],
['wh','Integer'],
['BaseDateTime','StartTimeStamp']
],
inputschema=['Integer','Integer','Integer'],
transport='File',
protocol='csv',
datahandler='Tuple',
metaattribute = ['TimeInterval', 'PredictionTimes'],
options=[
['filename', ${PATH_LOCAL}],
['Delimiter',','],
['TextDelimiter','"'],
['delay','1000'],
['readfirstline','false'],
['BaseTimeUnit','MINUTES']
]
}
)
/// Only use the last two measured values
time = TIMEWINDOW({
size = [20, 'minutes']
},
input
)
/// Convert the wh-attribute to a temporal double
temporalize = AGGREGATION({
aggregations = [
['function' = 'ToTemporalDouble', 'input_attributes' = 'wh', 'output_attributes' = 'temp_wh']
],
group_by = ['id'],
eval_at_outdating = false
},
time
)
predTime = PREDICTIONTIME({
addtostartvalue = [0, 'MINUTES'],
addtoendvalue = [15, 'MINUTEs']
},
temporalize
)
/// Energy consumption per household per minute in the next 15 minutes
derivative = MAP({
expressions = [
['derivative(temp_wh, PredictionTimes)','whPerMinute'],
['id','id']
]
},
predTime
)
/// Energy consumption per household per minute in the next 15 minutes
watts = MAP({
expressions = [
['whPerMinute * 60','watt'],
['id','id']
]
},
derivative
)
/// Filter out elements with a low energy consumption
highConsumption = SELECT({
predicate = 'watt > 300'
},
watts
) |