public class MatchGraphRequest extends Object implements org.apache.avro.generic.IndexedRecord
GPUdb.matchGraph.
Matches a directed route implied by a given set of latitude/longitude points to an existing underlying road network graph using a given solution type.
IMPORTANT: It's highly recommended that you review the Graphs & Solvers concepts documentation, the Graph REST Tutorial, and/or some /match/graph examples before using this endpoint.
| Modifier and Type | Class and Description |
|---|---|
static class |
MatchGraphRequest.Options
A set of string constants for the
MatchGraphRequest parameter
options. |
static class |
MatchGraphRequest.SolveMethod
A set of string constants for the
MatchGraphRequest parameter
solveMethod. |
| Constructor and Description |
|---|
MatchGraphRequest()
Constructs a MatchGraphRequest object with default parameters.
|
MatchGraphRequest(String graphName,
List<String> samplePoints,
String solveMethod,
String solutionTable,
Map<String,String> options)
Constructs a MatchGraphRequest object with the specified parameters.
|
| Modifier and Type | Method and Description | ||
|---|---|---|---|
boolean |
equals(Object obj) |
||
Object |
get(int index)
This method supports the Avro framework and is not intended to be called
directly by the user.
|
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static org.apache.avro.Schema |
getClassSchema()
This method supports the Avro framework and is not intended to be called
directly by the user.
|
||
String |
getGraphName()
Name of the underlying geospatial graph resource to match to using
samplePoints. |
||
Map<String,String> |
getOptions()
Additional parameters.
|
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List<String> |
getSamplePoints()
Sample points used to match to an underlying geospatial graph.
|
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org.apache.avro.Schema |
getSchema()
This method supports the Avro framework and is not intended to be called
directly by the user.
|
||
String |
getSolutionTable()
The name of the table used to store the results, in
[schema_name.]table_name format, using standard
StringgetSolveMethod()
The type of solver to use for graph matching.
| ||
int |
hashCode() |
||
void |
put(int index,
Object value)
This method supports the Avro framework and is not intended to be called
directly by the user.
|
||
MatchGraphRequest |
setGraphName(String graphName)
Name of the underlying geospatial graph resource to match to using
samplePoints. |
||
MatchGraphRequest |
setOptions(Map<String,String> options)
Additional parameters.
|
||
MatchGraphRequest |
setSamplePoints(List<String> samplePoints)
Sample points used to match to an underlying geospatial graph.
|
||
MatchGraphRequest |
setSolutionTable(String solutionTable)
The name of the table used to store the results, in
[schema_name.]table_name format, using standard
MatchGraphRequestsetSolveMethod(String solveMethod)
The type of solver to use for graph matching.
| ||
String |
toString() |
public MatchGraphRequest()
public MatchGraphRequest(String graphName, List<String> samplePoints, String solveMethod, String solutionTable, Map<String,String> options)
graphName - Name of the underlying geospatial graph resource to
match to using samplePoints.samplePoints - Sample points used to match to an underlying
geospatial graph. Sample points must be specified
using identifiers; identifiers are
grouped as combinations. Identifiers can be
used with: existing column names, e.g.,
'table.column AS SAMPLE_X'; expressions, e.g.,
'ST_MAKEPOINT(table.x, table.y) AS
SAMPLE_WKTPOINT'; or constant values, e.g., '{1, 2,
10} AS SAMPLE_TRIPID'.solveMethod - The type of solver to use for graph matching.
Supported values:
MARKOV_CHAIN: Matches samplePoints
to the graph using the Hidden Markov Model
(HMM)-based method, which conducts a
range-tree closest-edge search to find the
best combinations of possible road segments
(NUM_SEGMENTS)
for each sample point to create the best
route. The route is secured one point at a
time while looking ahead CHAIN_WIDTH number of
points, so the prediction is corrected after
each point. This solution type is the most
accurate but also the most computationally
intensive. Related options: NUM_SEGMENTS and
CHAIN_WIDTH.
MATCH_OD_PAIRS: Matches samplePoints to find the most probable path
between origin and destination pairs with
cost constraints.
MATCH_SUPPLY_DEMAND: Matches samplePoints to optimize scheduling
multiple supplies (trucks) with varying
sizes to varying demand sites with varying
capacities per depot. Related options:
PARTIAL_LOADING and MAX_COMBINATIONS.
MATCH_BATCH_SOLVES: Matches samplePoints source and destination pairs
for the shortest path solves in batch mode.
MATCH_LOOPS:
Matches closed loops (Eulerian paths)
originating and ending at each graph node
within min and max hops (levels).
MATCH_CHARGING_STATIONS: Matches an optimal
path across a number of ev-charging stations
between source and target locations.
MATCH_SIMILARITY: Matches the intersection
set(s) by computing the Jaccard similarity
score between node pairs.
MATCH_PICKUP_DROPOFF: Matches the pickups
and dropoffs by optimizing the total trip
costs
MATCH_CLUSTERS: Matches the graph nodes
with a cluster index using Louvain
clustering algorithm
MATCH_PATTERN: Matches a pattern in the
graph
MATCH_EMBEDDING: Creates vector node
embeddings
MATCH_ISOCHRONE: Solves for isochrones for
a set of input sources
MARKOV_CHAIN.solutionTable - The name of the table used to store the results,
in [schema_name.]table_name format, using standard
name resolution rules and
meeting table naming criteria. This
table contains a track of geospatial points for
the matched portion of the graph, a track ID, and
a score value. Also outputs a details table
containing a trip ID (that matches the track ID),
the latitude/longitude pair, the timestamp the
point was recorded at, and an edge ID
corresponding to the matched road segment. Must
not be an existing table of the same name. The
default value is ''.options - Additional parameters.
GPS_NOISE: GPS noise
value (in meters) to remove redundant sample
points. Use -1 to disable noise reduction. The
default value accounts for 95% of point
variation (+ or -5 meters). The default value is
'5.0'.
NUM_SEGMENTS:
Maximum number of potentially matching road
segments for each sample point. For the MARKOV_CHAIN solver,
the default is 3. The default value is '3'.
SEARCH_RADIUS:
Maximum search radius used when snapping sample
points onto potentially matching surrounding
segments. The default value corresponds to
approximately 100 meters. The default value is
'0.001'.
CHAIN_WIDTH: For the
MARKOV_CHAIN
solver only. Length of the sample points
lookahead window within the Markov kernel; the
larger the number, the more accurate the
solution. The default value is '9'.
SOURCE: Optional WKT
starting point from samplePoints for the
solver. The default behavior for the endpoint is
to use time to determine the starting point. The
default value is 'POINT NULL'.
DESTINATION:
Optional WKT ending point from samplePoints for the solver. The default
behavior for the endpoint is to use time to
determine the destination point. The default
value is 'POINT NULL'.
PARTIAL_LOADING:
For the MATCH_SUPPLY_DEMAND solver only. When false
(non-default), trucks do not off-load at the
demand (store) side if the remainder is less
than the store's need.
Supported values:
TRUE: Partial
off-loading at multiple store (demand)
locations
FALSE: No partial
off-loading allowed if supply is less
than the store's demand.
TRUE.
MAX_COMBINATIONS: For the MATCH_SUPPLY_DEMAND solver only. This is the
cutoff for the number of generated combinations
for sequencing the demand locations - can
increase this up to 2M. The default value is
'10000'.
MAX_SUPPLY_COMBINATIONS: For the MATCH_SUPPLY_DEMAND solver only. This is the
cutoff for the number of generated combinations
for sequencing the supply locations if/when
'permute_supplies' is true. The default value is
'10000'.
LEFT_TURN_PENALTY: This will add an additonal
weight over the edges labelled as 'left turn' if
the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph
creation. The default value is '0.0'.
RIGHT_TURN_PENALTY: This will add an additonal
weight over the edges labelled as' right turn'
if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph
creation. The default value is '0.0'.
INTERSECTION_PENALTY: This will add an
additonal weight over the edges labelled as
'intersection' if the 'add_turn' option
parameter of the GPUdb.createGraph was invoked at graph
creation. The default value is '0.0'.
SHARP_TURN_PENALTY: This will add an additonal
weight over the edges labelled as 'sharp turn'
or 'u-turn' if the 'add_turn' option parameter
of the GPUdb.createGraph was invoked at graph
creation. The default value is '0.0'.
AGGREGATED_OUTPUT: For the MATCH_SUPPLY_DEMAND solver only. When it is
true (default), each record in the output table
shows a particular truck's scheduled cumulative
round trip path (MULTILINESTRING) and the
corresponding aggregated cost. Otherwise, each
record shows a single scheduled truck route
(LINESTRING) towards a particular demand
location (store id) with its corresponding cost.
The default value is 'true'.
OUTPUT_TRACKS: For
the MATCH_SUPPLY_DEMAND solver only. When it is
true (non-default), the output will be in tracks
format for all the round trips of each truck in
which the timestamps are populated directly from
the edge weights starting from their originating
depots. The default value is 'false'.
MAX_TRIP_COST: For
the MATCH_SUPPLY_DEMAND and MATCH_PICKUP_DROPOFF solvers only. If this
constraint is greater than zero (default) then
the trucks/rides will skip travelling from one
demand/pick location to another if the cost
between them is greater than this number
(distance or time). Zero (default) value means
no check is performed. The default value is
'0.0'.
FILTER_FOLDING_PATHS: For the MARKOV_CHAIN solver
only. When true (non-default), the paths per
sequence combination is checked for folding over
patterns and can significantly increase the
execution time depending on the chain width and
the number of gps samples.
Supported values:
The default value is FALSE.
UNIT_UNLOADING_COST: For the MATCH_SUPPLY_DEMAND solver only. The unit cost
per load amount to be delivered. If this value
is greater than zero (default) then the
additional cost of this unit load multiplied by
the total dropped load will be added over to the
trip cost to the demand location. The default
value is '0.0'.
MAX_NUM_THREADS:
For the MARKOV_CHAIN solver only. If specified (greater
than zero), the maximum number of threads will
not be greater than the specified value. It can
be lower due to the memory and the number cores
available. Default value of zero allows the
algorithm to set the maximal number of threads
within these constraints. The default value is
'0'.
SERVICE_LIMIT: For
the MATCH_SUPPLY_DEMAND solver only. If specified
(greater than zero), any supply actor's total
service cost (distance or time) will be limited
by the specified value including multiple rounds
(if set). The default value is '0.0'.
ENABLE_REUSE: For
the MATCH_SUPPLY_DEMAND solver only. If specified
(true), all supply actors can be scheduled for
second rounds from their originating depots.
Supported values:
TRUE: Allows
reusing supply actors (trucks, e.g.) for
scheduling again.
FALSE: Supply
actors are scheduled only once from
their depots.
FALSE.
MAX_STOPS: For the
MATCH_SUPPLY_DEMAND solver only. If specified
(greater than zero), a supply actor (truck) can
at most have this many stops (demand locations)
in one round trip. Otherwise, it is unlimited.
If 'enable_truck_reuse' is on, this condition
will be applied separately at each round trip
use of the same truck. The default value is '0'.
SERVICE_RADIUS:
For the MATCH_SUPPLY_DEMAND and MATCH_PICKUP_DROPOFF solvers only. If specified
(greater than zero), it filters the
demands/picks outside this radius centered
around the supply actor/ride's originating
location (distance or time). The default value
is '0.0'.
PERMUTE_SUPPLIES: For the MATCH_SUPPLY_DEMAND solver only. If specified
(true), supply side actors are permuted for the
demand combinations during msdo optimization -
note that this option increases optimization
time significantly - use of 'max_combinations'
option is recommended to prevent prohibitively
long runs.
Supported values:
TRUE: Generates
sequences over supply side permutations
if total supply is less than twice the
total demand
FALSE:
Permutations are not performed, rather a
specific order of supplies based on
capacity is computed
TRUE.
BATCH_TSM_MODE:
For the MATCH_SUPPLY_DEMAND solver only. When enabled,
it sets the number of visits on each demand
location by a single salesman at each trip is
considered to be (one) 1, otherwise there is no
bound.
Supported values:
TRUE: Sets only one
visit per demand location by a salesman
(tsm mode)
FALSE: No preset
limit (usual msdo mode)
FALSE.
ROUND_TRIP: For the
MATCH_SUPPLY_DEMAND solver only. When enabled,
the supply will have to return back to the
origination location.
Supported values:
TRUE: The
optimization is done for trips in round
trip manner always returning to
originating locations
FALSE: Supplies do
not have to come back to their
originating locations in their routes.
The routes are considered finished at
the final dropoff.
TRUE.
NUM_CYCLES: For the
MATCH_CLUSTERS solver only. Terminates the
cluster exchange iterations across 2-step-cycles
(outer loop) when quality does not improve
during iterations. The default value is '10'.
NUM_LOOPS_PER_CYCLE: For the MATCH_CLUSTERS and
MATCH_EMBEDDING solvers only. Terminates the
cluster exchanges within the first step
iterations of a cycle (inner loop) unless
convergence is reached. The default value is
'10'.
NUM_OUTPUT_CLUSTERS: For the MATCH_CLUSTERS
solver only. Limits the output to the top
'num_output_clusters' clusters based on density.
Default value of zero outputs all clusters. The
default value is '0'.
MAX_NUM_CLUSTERS: For the MATCH_CLUSTERS and
MATCH_EMBEDDING solvers only. If set (value
greater than zero), it terminates when the
number of clusters goes below than this number.
For embedding solver the default is 8. The
default value is '0'.
CLUSTER_QUALITY_METRIC: For the MATCH_CLUSTERS
solver only. The quality metric for Louvain
modularity optimization solver.
Supported values:
GIRVAN: Uses the
Newman Girvan quality metric for cluster
solver
SPECTRAL:
Applies recursive spectral bisection
(RSB) partitioning solver
GIRVAN.
RESTRICTED_TYPE:
For the MATCH_SUPPLY_DEMAND solver only. Optimization
is performed by restricting routes labeled by
'MSDO_ODDEVEN_RESTRICTED' only for this supply
actor (truck) type.
Supported values:
ODD: Applies
odd/even rule restrictions to odd tagged
vehicles.
EVEN: Applies
odd/even rule restrictions to even
tagged vehicles.
NONE: Does not
apply odd/even rule restrictions to any
vehicles.
NONE.
SERVER_ID: Indicates
which graph server(s) to send the request to.
Default is to send to the server, amongst those
containing the corresponding graph, that has the
most computational bandwidth. The default value
is ''.
INVERSE_SOLVE: For
the MATCH_BATCH_SOLVES solver only. Solves
source-destination pairs using inverse shortest
path solver.
Supported values:
The default value is FALSE.
MIN_LOOP_LEVEL:
For the MATCH_LOOPS solver only. Finds closed loops
around each node deducible not less than this
minimal hop (level) deep. The default value is
'0'.
MAX_LOOP_LEVEL:
For the MATCH_LOOPS solver only. Finds closed loops
around each node deducible not more than this
maximal hop (level) deep. The default value is
'5'.
SEARCH_LIMIT: For
the MATCH_LOOPS
solver only. Searches within this limit of nodes
per vertex to detect loops. The value zero means
there is no limit. The default value is '10000'.
OUTPUT_BATCH_SIZE: For the MATCH_LOOPS solver
only. Uses this value as the batch size of the
number of loops in flushing(inserting) to the
output table. The default value is '1000'.
CHARGING_CAPACITY: For the MATCH_CHARGING_STATIONS solver only. This is
the maximum ev-charging capacity of a vehicle
(distance in meters or time in seconds depending
on the unit of the graph weights). The default
value is '300000.0'.
CHARGING_CANDIDATES: For the MATCH_CHARGING_STATIONS solver only. Solver
searches for this many number of stations
closest around each base charging location found
by capacity. The default value is '10'.
CHARGING_PENALTY: For the MATCH_CHARGING_STATIONS solver only. This is
the penalty for full charging. The default value
is '30000.0'.
MAX_HOPS: For the
MATCH_SIMILARITY and MATCH_EMBEDDING
solvers only. Searches within this maximum hops
for source and target node pairs to compute the
Jaccard scores. The default value is '3'.
TRAVERSAL_NODE_LIMIT: For the MATCH_SIMILARITY
solver only. Limits the traversal depth if it
reaches this many number of nodes. The default
value is '1000'.
PAIRED_SIMILARITY: For the MATCH_SIMILARITY
solver only. If true, it computes Jaccard score
between each pair, otherwise it will compute
Jaccard from the intersection set between the
source and target nodes.
Supported values:
The default value is TRUE.
FORCE_UNDIRECTED: For the MATCH_PATTERN and
MATCH_EMBEDDING solvers only. Pattern matching
will be using both pattern and graph as
undirected if set to true.
Supported values:
The default value is FALSE.
MAX_VECTOR_DIMENSION: For the MATCH_EMBEDDING
solver only. Limits the number of dimensions in
node vector embeddings. The default value is
'1000'.
OPTIMIZE_EMBEDDING_WEIGHTS: For the MATCH_EMBEDDING
solvers only. Solves to find the optimal weights
per sub feature in vector emdeddings.
Supported values:
The default value is FALSE.
EMBEDDING_WEIGHTS: For the MATCH_EMBEDDING
solver only. User specified weights per sub
feature in vector embeddings. The string
contains the comma separated float values for
each sub-feature in the vector space. These
values will ONLY be used if
'optimize_embedding_weights' is false. The
default value is '1.0,1.0,1.0,1.0'.
OPTIMIZATION_SAMPLING_SIZE: For the MATCH_EMBEDDING
solver only. Sets the number of random nodes
from the graph for solving the weights using
stochastic gradient descent. The default value
is '1000'.
OPTIMIZATION_MAX_ITERATIONS: For the MATCH_EMBEDDING
solver only. When the iterations (epochs) for
the convergence of the stochastic gradient
descent algorithm reaches this number it bails
out unless relative error between consecutive
iterations is below the
'optimization_error_tolerance' option. The
default value is '1000'.
OPTIMIZATION_ERROR_TOLERANCE: For the MATCH_EMBEDDING
solver only. When the relative error between all
of the weights' consecutive iterations falls
below this threshold the optimization cycle is
interrupted unless the number of iterations
reaches the limit set by the option
'max_optimization_iterations'. The default value
is '0.001'.
OPTIMIZATION_ITERATION_RATE: For the MATCH_EMBEDDING
solver only. It is otherwise known as the
learning rate, which is the proportionality
constant in fornt of the gradient term in
successive iterations. The default value is
'0.3'.
MAX_RADIUS: For the
MATCH_ISOCHRONE solver only. Sets the maximal
reachability limmit for computing isochrones.
Zero means no limit. The default value is '0.0'.
Map.public static org.apache.avro.Schema getClassSchema()
public String getGraphName()
samplePoints.graphName.public MatchGraphRequest setGraphName(String graphName)
samplePoints.graphName - The new value for graphName.this to mimic the builder pattern.public List<String> getSamplePoints()
samplePoints.public MatchGraphRequest setSamplePoints(List<String> samplePoints)
samplePoints - The new value for samplePoints.this to mimic the builder pattern.public String getSolveMethod()
MARKOV_CHAIN: Matches samplePoints to the graph using the Hidden
Markov Model (HMM)-based method, which conducts a range-tree
closest-edge search to find the best combinations of possible
road segments (NUM_SEGMENTS) for
each sample point to create the best route. The route is secured
one point at a time while looking ahead CHAIN_WIDTH number of points, so the
prediction is corrected after each point. This solution type is
the most accurate but also the most computationally intensive.
Related options: NUM_SEGMENTS and
CHAIN_WIDTH.
MATCH_OD_PAIRS: Matches
samplePoints to find the most
probable path between origin and destination pairs with cost
constraints.
MATCH_SUPPLY_DEMAND:
Matches samplePoints to optimize
scheduling multiple supplies (trucks) with varying sizes to
varying demand sites with varying capacities per depot. Related
options: PARTIAL_LOADING and
MAX_COMBINATIONS.
MATCH_BATCH_SOLVES:
Matches samplePoints source and
destination pairs for the shortest path solves in batch mode.
MATCH_LOOPS: Matches closed
loops (Eulerian paths) originating and ending at each graph node
within min and max hops (levels).
MATCH_CHARGING_STATIONS: Matches an optimal path across a
number of ev-charging stations between source and target
locations.
MATCH_SIMILARITY: Matches
the intersection set(s) by computing the Jaccard similarity
score between node pairs.
MATCH_PICKUP_DROPOFF:
Matches the pickups and dropoffs by optimizing the total trip
costs
MATCH_CLUSTERS: Matches the
graph nodes with a cluster index using Louvain clustering
algorithm
MATCH_PATTERN: Matches a
pattern in the graph
MATCH_EMBEDDING: Creates
vector node embeddings
MATCH_ISOCHRONE: Solves for
isochrones for a set of input sources
MARKOV_CHAIN.solveMethod.public MatchGraphRequest setSolveMethod(String solveMethod)
MARKOV_CHAIN: Matches samplePoints to the graph using the Hidden
Markov Model (HMM)-based method, which conducts a range-tree
closest-edge search to find the best combinations of possible
road segments (NUM_SEGMENTS) for
each sample point to create the best route. The route is secured
one point at a time while looking ahead CHAIN_WIDTH number of points, so the
prediction is corrected after each point. This solution type is
the most accurate but also the most computationally intensive.
Related options: NUM_SEGMENTS and
CHAIN_WIDTH.
MATCH_OD_PAIRS: Matches
samplePoints to find the most
probable path between origin and destination pairs with cost
constraints.
MATCH_SUPPLY_DEMAND:
Matches samplePoints to optimize
scheduling multiple supplies (trucks) with varying sizes to
varying demand sites with varying capacities per depot. Related
options: PARTIAL_LOADING and
MAX_COMBINATIONS.
MATCH_BATCH_SOLVES:
Matches samplePoints source and
destination pairs for the shortest path solves in batch mode.
MATCH_LOOPS: Matches closed
loops (Eulerian paths) originating and ending at each graph node
within min and max hops (levels).
MATCH_CHARGING_STATIONS: Matches an optimal path across a
number of ev-charging stations between source and target
locations.
MATCH_SIMILARITY: Matches
the intersection set(s) by computing the Jaccard similarity
score between node pairs.
MATCH_PICKUP_DROPOFF:
Matches the pickups and dropoffs by optimizing the total trip
costs
MATCH_CLUSTERS: Matches the
graph nodes with a cluster index using Louvain clustering
algorithm
MATCH_PATTERN: Matches a
pattern in the graph
MATCH_EMBEDDING: Creates
vector node embeddings
MATCH_ISOCHRONE: Solves for
isochrones for a set of input sources
MARKOV_CHAIN.solveMethod - The new value for solveMethod.this to mimic the builder pattern.public String getSolutionTable()
solutionTable.public MatchGraphRequest setSolutionTable(String solutionTable)
solutionTable - The new value for solutionTable.this to mimic the builder pattern.public Map<String,String> getOptions()
GPS_NOISE: GPS noise value (in meters)
to remove redundant sample points. Use -1 to disable noise
reduction. The default value accounts for 95% of point variation
(+ or -5 meters). The default value is '5.0'.
NUM_SEGMENTS: Maximum number of
potentially matching road segments for each sample point. For
the MARKOV_CHAIN solver, the
default is 3. The default value is '3'.
SEARCH_RADIUS: Maximum search
radius used when snapping sample points onto potentially
matching surrounding segments. The default value corresponds to
approximately 100 meters. The default value is '0.001'.
CHAIN_WIDTH: For the MARKOV_CHAIN solver only. Length of
the sample points lookahead window within the Markov kernel; the
larger the number, the more accurate the solution. The default
value is '9'.
SOURCE: Optional WKT starting point from
samplePoints for the solver. The
default behavior for the endpoint is to use time to determine
the starting point. The default value is 'POINT NULL'.
DESTINATION: Optional WKT ending
point from samplePoints for the
solver. The default behavior for the endpoint is to use time to
determine the destination point. The default value is 'POINT
NULL'.
PARTIAL_LOADING: For the MATCH_SUPPLY_DEMAND solver
only. When false (non-default), trucks do not off-load at the
demand (store) side if the remainder is less than the store's
need.
Supported values:
TRUE: Partial off-loading at
multiple store (demand) locations
FALSE: No partial off-loading
allowed if supply is less than the store's demand.
TRUE.
MAX_COMBINATIONS: For the
MATCH_SUPPLY_DEMAND
solver only. This is the cutoff for the number of generated
combinations for sequencing the demand locations - can increase
this up to 2M. The default value is '10000'.
MAX_SUPPLY_COMBINATIONS:
For the MATCH_SUPPLY_DEMAND solver only. This is the cutoff for the
number of generated combinations for sequencing the supply
locations if/when 'permute_supplies' is true. The default value
is '10000'.
LEFT_TURN_PENALTY: This will
add an additonal weight over the edges labelled as 'left turn'
if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
RIGHT_TURN_PENALTY: This will
add an additonal weight over the edges labelled as' right turn'
if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
INTERSECTION_PENALTY: This
will add an additonal weight over the edges labelled as
'intersection' if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
SHARP_TURN_PENALTY: This will
add an additonal weight over the edges labelled as 'sharp turn'
or 'u-turn' if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
AGGREGATED_OUTPUT: For the
MATCH_SUPPLY_DEMAND
solver only. When it is true (default), each record in the
output table shows a particular truck's scheduled cumulative
round trip path (MULTILINESTRING) and the corresponding
aggregated cost. Otherwise, each record shows a single scheduled
truck route (LINESTRING) towards a particular demand location
(store id) with its corresponding cost. The default value is
'true'.
OUTPUT_TRACKS: For the MATCH_SUPPLY_DEMAND solver
only. When it is true (non-default), the output will be in
tracks format for all the round trips of each truck in which the
timestamps are populated directly from the edge weights starting
from their originating depots. The default value is 'false'.
MAX_TRIP_COST: For the MATCH_SUPPLY_DEMAND and MATCH_PICKUP_DROPOFF solvers
only. If this constraint is greater than zero (default) then the
trucks/rides will skip travelling from one demand/pick location
to another if the cost between them is greater than this number
(distance or time). Zero (default) value means no check is
performed. The default value is '0.0'.
FILTER_FOLDING_PATHS: For
the MARKOV_CHAIN solver only.
When true (non-default), the paths per sequence combination is
checked for folding over patterns and can significantly increase
the execution time depending on the chain width and the number
of gps samples.
Supported values:
The default value is FALSE.
UNIT_UNLOADING_COST: For the
MATCH_SUPPLY_DEMAND
solver only. The unit cost per load amount to be delivered. If
this value is greater than zero (default) then the additional
cost of this unit load multiplied by the total dropped load will
be added over to the trip cost to the demand location. The
default value is '0.0'.
MAX_NUM_THREADS: For the MARKOV_CHAIN solver only. If specified
(greater than zero), the maximum number of threads will not be
greater than the specified value. It can be lower due to the
memory and the number cores available. Default value of zero
allows the algorithm to set the maximal number of threads within
these constraints. The default value is '0'.
SERVICE_LIMIT: For the MATCH_SUPPLY_DEMAND solver
only. If specified (greater than zero), any supply actor's total
service cost (distance or time) will be limited by the specified
value including multiple rounds (if set). The default value is
'0.0'.
ENABLE_REUSE: For the MATCH_SUPPLY_DEMAND solver
only. If specified (true), all supply actors can be scheduled
for second rounds from their originating depots.
Supported values:
TRUE: Allows reusing supply actors
(trucks, e.g.) for scheduling again.
FALSE: Supply actors are scheduled
only once from their depots.
FALSE.
MAX_STOPS: For the MATCH_SUPPLY_DEMAND solver
only. If specified (greater than zero), a supply actor (truck)
can at most have this many stops (demand locations) in one round
trip. Otherwise, it is unlimited. If 'enable_truck_reuse' is on,
this condition will be applied separately at each round trip use
of the same truck. The default value is '0'.
SERVICE_RADIUS: For the MATCH_SUPPLY_DEMAND and MATCH_PICKUP_DROPOFF solvers
only. If specified (greater than zero), it filters the
demands/picks outside this radius centered around the supply
actor/ride's originating location (distance or time). The
default value is '0.0'.
PERMUTE_SUPPLIES: For the
MATCH_SUPPLY_DEMAND
solver only. If specified (true), supply side actors are
permuted for the demand combinations during msdo optimization -
note that this option increases optimization time significantly
- use of 'max_combinations' option is recommended to prevent
prohibitively long runs.
Supported values:
TRUE: Generates sequences over
supply side permutations if total supply is less than
twice the total demand
FALSE: Permutations are not
performed, rather a specific order of supplies based on
capacity is computed
TRUE.
BATCH_TSM_MODE: For the MATCH_SUPPLY_DEMAND solver
only. When enabled, it sets the number of visits on each demand
location by a single salesman at each trip is considered to be
(one) 1, otherwise there is no bound.
Supported values:
TRUE: Sets only one visit per
demand location by a salesman (tsm mode)
FALSE: No preset limit (usual msdo
mode)
FALSE.
ROUND_TRIP: For the MATCH_SUPPLY_DEMAND solver
only. When enabled, the supply will have to return back to the
origination location.
Supported values:
TRUE: The optimization is done for
trips in round trip manner always returning to
originating locations
FALSE: Supplies do not have to
come back to their originating locations in their
routes. The routes are considered finished at the final
dropoff.
TRUE.
NUM_CYCLES: For the MATCH_CLUSTERS solver only.
Terminates the cluster exchange iterations across 2-step-cycles
(outer loop) when quality does not improve during iterations.
The default value is '10'.
NUM_LOOPS_PER_CYCLE: For the
MATCH_CLUSTERS and MATCH_EMBEDDING solvers only.
Terminates the cluster exchanges within the first step
iterations of a cycle (inner loop) unless convergence is
reached. The default value is '10'.
NUM_OUTPUT_CLUSTERS: For the
MATCH_CLUSTERS solver only.
Limits the output to the top 'num_output_clusters' clusters
based on density. Default value of zero outputs all clusters.
The default value is '0'.
MAX_NUM_CLUSTERS: For the
MATCH_CLUSTERS and MATCH_EMBEDDING solvers only. If
set (value greater than zero), it terminates when the number of
clusters goes below than this number. For embedding solver the
default is 8. The default value is '0'.
CLUSTER_QUALITY_METRIC:
For the MATCH_CLUSTERS solver
only. The quality metric for Louvain modularity optimization
solver.
Supported values:
GIRVAN: Uses the Newman Girvan
quality metric for cluster solver
SPECTRAL: Applies recursive
spectral bisection (RSB) partitioning solver
GIRVAN.
RESTRICTED_TYPE: For the MATCH_SUPPLY_DEMAND solver
only. Optimization is performed by restricting routes labeled by
'MSDO_ODDEVEN_RESTRICTED' only for this supply actor (truck)
type.
Supported values:
ODD: Applies odd/even rule
restrictions to odd tagged vehicles.
EVEN: Applies odd/even rule
restrictions to even tagged vehicles.
NONE: Does not apply odd/even rule
restrictions to any vehicles.
NONE.
SERVER_ID: Indicates which graph
server(s) to send the request to. Default is to send to the
server, amongst those containing the corresponding graph, that
has the most computational bandwidth. The default value is ''.
INVERSE_SOLVE: For the MATCH_BATCH_SOLVES solver only.
Solves source-destination pairs using inverse shortest path
solver.
Supported values:
The default value is FALSE.
MIN_LOOP_LEVEL: For the MATCH_LOOPS solver only. Finds closed
loops around each node deducible not less than this minimal hop
(level) deep. The default value is '0'.
MAX_LOOP_LEVEL: For the MATCH_LOOPS solver only. Finds closed
loops around each node deducible not more than this maximal hop
(level) deep. The default value is '5'.
SEARCH_LIMIT: For the MATCH_LOOPS solver only. Searches
within this limit of nodes per vertex to detect loops. The value
zero means there is no limit. The default value is '10000'.
OUTPUT_BATCH_SIZE: For the
MATCH_LOOPS solver only. Uses
this value as the batch size of the number of loops in
flushing(inserting) to the output table. The default value is
'1000'.
CHARGING_CAPACITY: For the
MATCH_CHARGING_STATIONS solver only. This is the maximum
ev-charging capacity of a vehicle (distance in meters or time in
seconds depending on the unit of the graph weights). The default
value is '300000.0'.
CHARGING_CANDIDATES: For the
MATCH_CHARGING_STATIONS solver only. Solver searches for this
many number of stations closest around each base charging
location found by capacity. The default value is '10'.
CHARGING_PENALTY: For the
MATCH_CHARGING_STATIONS solver only. This is the penalty for
full charging. The default value is '30000.0'.
MAX_HOPS: For the MATCH_SIMILARITY and MATCH_EMBEDDING solvers only.
Searches within this maximum hops for source and target node
pairs to compute the Jaccard scores. The default value is '3'.
TRAVERSAL_NODE_LIMIT: For
the MATCH_SIMILARITY solver
only. Limits the traversal depth if it reaches this many number
of nodes. The default value is '1000'.
PAIRED_SIMILARITY: For the
MATCH_SIMILARITY solver
only. If true, it computes Jaccard score between each pair,
otherwise it will compute Jaccard from the intersection set
between the source and target nodes.
Supported values:
The default value is TRUE.
FORCE_UNDIRECTED: For the
MATCH_PATTERN and MATCH_EMBEDDING solvers only.
Pattern matching will be using both pattern and graph as
undirected if set to true.
Supported values:
The default value is FALSE.
MAX_VECTOR_DIMENSION: For
the MATCH_EMBEDDING solver
only. Limits the number of dimensions in node vector embeddings.
The default value is '1000'.
OPTIMIZE_EMBEDDING_WEIGHTS: For the MATCH_EMBEDDING solvers only.
Solves to find the optimal weights per sub feature in vector
emdeddings.
Supported values:
The default value is FALSE.
EMBEDDING_WEIGHTS: For the
MATCH_EMBEDDING solver only.
User specified weights per sub feature in vector embeddings.
The string contains the comma separated float values for each
sub-feature in the vector space. These values will ONLY be used
if 'optimize_embedding_weights' is false. The default value is
'1.0,1.0,1.0,1.0'.
OPTIMIZATION_SAMPLING_SIZE: For the MATCH_EMBEDDING solver only. Sets
the number of random nodes from the graph for solving the
weights using stochastic gradient descent. The default value is
'1000'.
OPTIMIZATION_MAX_ITERATIONS: For the MATCH_EMBEDDING solver only. When
the iterations (epochs) for the convergence of the stochastic
gradient descent algorithm reaches this number it bails out
unless relative error between consecutive iterations is below
the 'optimization_error_tolerance' option. The default value is
'1000'.
OPTIMIZATION_ERROR_TOLERANCE: For the MATCH_EMBEDDING solver only. When
the relative error between all of the weights' consecutive
iterations falls below this threshold the optimization cycle is
interrupted unless the number of iterations reaches the limit
set by the option 'max_optimization_iterations'. The default
value is '0.001'.
OPTIMIZATION_ITERATION_RATE: For the MATCH_EMBEDDING solver only. It is
otherwise known as the learning rate, which is the
proportionality constant in fornt of the gradient term in
successive iterations. The default value is '0.3'.
MAX_RADIUS: For the MATCH_ISOCHRONE solver only. Sets
the maximal reachability limmit for computing isochrones. Zero
means no limit. The default value is '0.0'.
Map.options.public MatchGraphRequest setOptions(Map<String,String> options)
GPS_NOISE: GPS noise value (in meters)
to remove redundant sample points. Use -1 to disable noise
reduction. The default value accounts for 95% of point variation
(+ or -5 meters). The default value is '5.0'.
NUM_SEGMENTS: Maximum number of
potentially matching road segments for each sample point. For
the MARKOV_CHAIN solver, the
default is 3. The default value is '3'.
SEARCH_RADIUS: Maximum search
radius used when snapping sample points onto potentially
matching surrounding segments. The default value corresponds to
approximately 100 meters. The default value is '0.001'.
CHAIN_WIDTH: For the MARKOV_CHAIN solver only. Length of
the sample points lookahead window within the Markov kernel; the
larger the number, the more accurate the solution. The default
value is '9'.
SOURCE: Optional WKT starting point from
samplePoints for the solver. The
default behavior for the endpoint is to use time to determine
the starting point. The default value is 'POINT NULL'.
DESTINATION: Optional WKT ending
point from samplePoints for the
solver. The default behavior for the endpoint is to use time to
determine the destination point. The default value is 'POINT
NULL'.
PARTIAL_LOADING: For the MATCH_SUPPLY_DEMAND solver
only. When false (non-default), trucks do not off-load at the
demand (store) side if the remainder is less than the store's
need.
Supported values:
TRUE: Partial off-loading at
multiple store (demand) locations
FALSE: No partial off-loading
allowed if supply is less than the store's demand.
TRUE.
MAX_COMBINATIONS: For the
MATCH_SUPPLY_DEMAND
solver only. This is the cutoff for the number of generated
combinations for sequencing the demand locations - can increase
this up to 2M. The default value is '10000'.
MAX_SUPPLY_COMBINATIONS:
For the MATCH_SUPPLY_DEMAND solver only. This is the cutoff for the
number of generated combinations for sequencing the supply
locations if/when 'permute_supplies' is true. The default value
is '10000'.
LEFT_TURN_PENALTY: This will
add an additonal weight over the edges labelled as 'left turn'
if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
RIGHT_TURN_PENALTY: This will
add an additonal weight over the edges labelled as' right turn'
if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
INTERSECTION_PENALTY: This
will add an additonal weight over the edges labelled as
'intersection' if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
SHARP_TURN_PENALTY: This will
add an additonal weight over the edges labelled as 'sharp turn'
or 'u-turn' if the 'add_turn' option parameter of the GPUdb.createGraph was invoked at graph creation. The default
value is '0.0'.
AGGREGATED_OUTPUT: For the
MATCH_SUPPLY_DEMAND
solver only. When it is true (default), each record in the
output table shows a particular truck's scheduled cumulative
round trip path (MULTILINESTRING) and the corresponding
aggregated cost. Otherwise, each record shows a single scheduled
truck route (LINESTRING) towards a particular demand location
(store id) with its corresponding cost. The default value is
'true'.
OUTPUT_TRACKS: For the MATCH_SUPPLY_DEMAND solver
only. When it is true (non-default), the output will be in
tracks format for all the round trips of each truck in which the
timestamps are populated directly from the edge weights starting
from their originating depots. The default value is 'false'.
MAX_TRIP_COST: For the MATCH_SUPPLY_DEMAND and MATCH_PICKUP_DROPOFF solvers
only. If this constraint is greater than zero (default) then the
trucks/rides will skip travelling from one demand/pick location
to another if the cost between them is greater than this number
(distance or time). Zero (default) value means no check is
performed. The default value is '0.0'.
FILTER_FOLDING_PATHS: For
the MARKOV_CHAIN solver only.
When true (non-default), the paths per sequence combination is
checked for folding over patterns and can significantly increase
the execution time depending on the chain width and the number
of gps samples.
Supported values:
The default value is FALSE.
UNIT_UNLOADING_COST: For the
MATCH_SUPPLY_DEMAND
solver only. The unit cost per load amount to be delivered. If
this value is greater than zero (default) then the additional
cost of this unit load multiplied by the total dropped load will
be added over to the trip cost to the demand location. The
default value is '0.0'.
MAX_NUM_THREADS: For the MARKOV_CHAIN solver only. If specified
(greater than zero), the maximum number of threads will not be
greater than the specified value. It can be lower due to the
memory and the number cores available. Default value of zero
allows the algorithm to set the maximal number of threads within
these constraints. The default value is '0'.
SERVICE_LIMIT: For the MATCH_SUPPLY_DEMAND solver
only. If specified (greater than zero), any supply actor's total
service cost (distance or time) will be limited by the specified
value including multiple rounds (if set). The default value is
'0.0'.
ENABLE_REUSE: For the MATCH_SUPPLY_DEMAND solver
only. If specified (true), all supply actors can be scheduled
for second rounds from their originating depots.
Supported values:
TRUE: Allows reusing supply actors
(trucks, e.g.) for scheduling again.
FALSE: Supply actors are scheduled
only once from their depots.
FALSE.
MAX_STOPS: For the MATCH_SUPPLY_DEMAND solver
only. If specified (greater than zero), a supply actor (truck)
can at most have this many stops (demand locations) in one round
trip. Otherwise, it is unlimited. If 'enable_truck_reuse' is on,
this condition will be applied separately at each round trip use
of the same truck. The default value is '0'.
SERVICE_RADIUS: For the MATCH_SUPPLY_DEMAND and MATCH_PICKUP_DROPOFF solvers
only. If specified (greater than zero), it filters the
demands/picks outside this radius centered around the supply
actor/ride's originating location (distance or time). The
default value is '0.0'.
PERMUTE_SUPPLIES: For the
MATCH_SUPPLY_DEMAND
solver only. If specified (true), supply side actors are
permuted for the demand combinations during msdo optimization -
note that this option increases optimization time significantly
- use of 'max_combinations' option is recommended to prevent
prohibitively long runs.
Supported values:
TRUE: Generates sequences over
supply side permutations if total supply is less than
twice the total demand
FALSE: Permutations are not
performed, rather a specific order of supplies based on
capacity is computed
TRUE.
BATCH_TSM_MODE: For the MATCH_SUPPLY_DEMAND solver
only. When enabled, it sets the number of visits on each demand
location by a single salesman at each trip is considered to be
(one) 1, otherwise there is no bound.
Supported values:
TRUE: Sets only one visit per
demand location by a salesman (tsm mode)
FALSE: No preset limit (usual msdo
mode)
FALSE.
ROUND_TRIP: For the MATCH_SUPPLY_DEMAND solver
only. When enabled, the supply will have to return back to the
origination location.
Supported values:
TRUE: The optimization is done for
trips in round trip manner always returning to
originating locations
FALSE: Supplies do not have to
come back to their originating locations in their
routes. The routes are considered finished at the final
dropoff.
TRUE.
NUM_CYCLES: For the MATCH_CLUSTERS solver only.
Terminates the cluster exchange iterations across 2-step-cycles
(outer loop) when quality does not improve during iterations.
The default value is '10'.
NUM_LOOPS_PER_CYCLE: For the
MATCH_CLUSTERS and MATCH_EMBEDDING solvers only.
Terminates the cluster exchanges within the first step
iterations of a cycle (inner loop) unless convergence is
reached. The default value is '10'.
NUM_OUTPUT_CLUSTERS: For the
MATCH_CLUSTERS solver only.
Limits the output to the top 'num_output_clusters' clusters
based on density. Default value of zero outputs all clusters.
The default value is '0'.
MAX_NUM_CLUSTERS: For the
MATCH_CLUSTERS and MATCH_EMBEDDING solvers only. If
set (value greater than zero), it terminates when the number of
clusters goes below than this number. For embedding solver the
default is 8. The default value is '0'.
CLUSTER_QUALITY_METRIC:
For the MATCH_CLUSTERS solver
only. The quality metric for Louvain modularity optimization
solver.
Supported values:
GIRVAN: Uses the Newman Girvan
quality metric for cluster solver
SPECTRAL: Applies recursive
spectral bisection (RSB) partitioning solver
GIRVAN.
RESTRICTED_TYPE: For the MATCH_SUPPLY_DEMAND solver
only. Optimization is performed by restricting routes labeled by
'MSDO_ODDEVEN_RESTRICTED' only for this supply actor (truck)
type.
Supported values:
ODD: Applies odd/even rule
restrictions to odd tagged vehicles.
EVEN: Applies odd/even rule
restrictions to even tagged vehicles.
NONE: Does not apply odd/even rule
restrictions to any vehicles.
NONE.
SERVER_ID: Indicates which graph
server(s) to send the request to. Default is to send to the
server, amongst those containing the corresponding graph, that
has the most computational bandwidth. The default value is ''.
INVERSE_SOLVE: For the MATCH_BATCH_SOLVES solver only.
Solves source-destination pairs using inverse shortest path
solver.
Supported values:
The default value is FALSE.
MIN_LOOP_LEVEL: For the MATCH_LOOPS solver only. Finds closed
loops around each node deducible not less than this minimal hop
(level) deep. The default value is '0'.
MAX_LOOP_LEVEL: For the MATCH_LOOPS solver only. Finds closed
loops around each node deducible not more than this maximal hop
(level) deep. The default value is '5'.
SEARCH_LIMIT: For the MATCH_LOOPS solver only. Searches
within this limit of nodes per vertex to detect loops. The value
zero means there is no limit. The default value is '10000'.
OUTPUT_BATCH_SIZE: For the
MATCH_LOOPS solver only. Uses
this value as the batch size of the number of loops in
flushing(inserting) to the output table. The default value is
'1000'.
CHARGING_CAPACITY: For the
MATCH_CHARGING_STATIONS solver only. This is the maximum
ev-charging capacity of a vehicle (distance in meters or time in
seconds depending on the unit of the graph weights). The default
value is '300000.0'.
CHARGING_CANDIDATES: For the
MATCH_CHARGING_STATIONS solver only. Solver searches for this
many number of stations closest around each base charging
location found by capacity. The default value is '10'.
CHARGING_PENALTY: For the
MATCH_CHARGING_STATIONS solver only. This is the penalty for
full charging. The default value is '30000.0'.
MAX_HOPS: For the MATCH_SIMILARITY and MATCH_EMBEDDING solvers only.
Searches within this maximum hops for source and target node
pairs to compute the Jaccard scores. The default value is '3'.
TRAVERSAL_NODE_LIMIT: For
the MATCH_SIMILARITY solver
only. Limits the traversal depth if it reaches this many number
of nodes. The default value is '1000'.
PAIRED_SIMILARITY: For the
MATCH_SIMILARITY solver
only. If true, it computes Jaccard score between each pair,
otherwise it will compute Jaccard from the intersection set
between the source and target nodes.
Supported values:
The default value is TRUE.
FORCE_UNDIRECTED: For the
MATCH_PATTERN and MATCH_EMBEDDING solvers only.
Pattern matching will be using both pattern and graph as
undirected if set to true.
Supported values:
The default value is FALSE.
MAX_VECTOR_DIMENSION: For
the MATCH_EMBEDDING solver
only. Limits the number of dimensions in node vector embeddings.
The default value is '1000'.
OPTIMIZE_EMBEDDING_WEIGHTS: For the MATCH_EMBEDDING solvers only.
Solves to find the optimal weights per sub feature in vector
emdeddings.
Supported values:
The default value is FALSE.
EMBEDDING_WEIGHTS: For the
MATCH_EMBEDDING solver only.
User specified weights per sub feature in vector embeddings.
The string contains the comma separated float values for each
sub-feature in the vector space. These values will ONLY be used
if 'optimize_embedding_weights' is false. The default value is
'1.0,1.0,1.0,1.0'.
OPTIMIZATION_SAMPLING_SIZE: For the MATCH_EMBEDDING solver only. Sets
the number of random nodes from the graph for solving the
weights using stochastic gradient descent. The default value is
'1000'.
OPTIMIZATION_MAX_ITERATIONS: For the MATCH_EMBEDDING solver only. When
the iterations (epochs) for the convergence of the stochastic
gradient descent algorithm reaches this number it bails out
unless relative error between consecutive iterations is below
the 'optimization_error_tolerance' option. The default value is
'1000'.
OPTIMIZATION_ERROR_TOLERANCE: For the MATCH_EMBEDDING solver only. When
the relative error between all of the weights' consecutive
iterations falls below this threshold the optimization cycle is
interrupted unless the number of iterations reaches the limit
set by the option 'max_optimization_iterations'. The default
value is '0.001'.
OPTIMIZATION_ITERATION_RATE: For the MATCH_EMBEDDING solver only. It is
otherwise known as the learning rate, which is the
proportionality constant in fornt of the gradient term in
successive iterations. The default value is '0.3'.
MAX_RADIUS: For the MATCH_ISOCHRONE solver only. Sets
the maximal reachability limmit for computing isochrones. Zero
means no limit. The default value is '0.0'.
Map.options - The new value for options.this to mimic the builder pattern.public org.apache.avro.Schema getSchema()
getSchema in interface org.apache.avro.generic.GenericContainerpublic Object get(int index)
get in interface org.apache.avro.generic.IndexedRecordindex - the position of the field to getIndexOutOfBoundsExceptionpublic void put(int index,
Object value)
put in interface org.apache.avro.generic.IndexedRecordindex - the position of the field to setvalue - the value to setIndexOutOfBoundsExceptionCopyright © 2025. All rights reserved.