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Temporal Feature
temporal attribute involved. Theoretical Foundation Expressions are a core element of queries. They are used in map, select, and join operators. Typically … (tdistance, PredictionTimes)'] },testSelect) Join When applying a join operator on two streams with PredictionTimes metadata, the operator intersectsRecommender System operators
= RECOMMENDATION_CANDIDATES(JOIN(rfr, 1:models)) predicted_candidates = PREDICT_RATING(JOIN(models, recomm_candidates)) recommendations = RECOMMEND({top_n = 8, min_rating = 3.5}, predicted_candidates) /// evaluation predicted_test_data = PREDICT_RATING(JOIN(models, 1:splitted_rating_data)) model_errors = TEST_PREDICTIONSubQuery
}/innerQuery.qry'}, in, in2) out0 = OUTPUTCONNECTOR({PORT = 0, name="Conn1"}, 0:out) out1 = OUTPUTCONNECTOR({PORT = 1, name="Conn2"}, 1:out) join = JOIN(in, out1OdysseusNet Replication
, ID='OPERATOR_ID'},INPUT_STREAM) E. g. the following query only replicates the JOIN-Operator: #PARSER PQL #CONFIG DISTRIBUTE true #NODE_PARTITION … , ID='window2'},nexmark:bid) out = JOIN({ PREDICATE='id = auction' }, windowed_auction, windowed_bid )Query Definition Language (QDL)
=[1, "SECONDS"]}; Filter filter{predicate="bid.price <= 200"}; ODLJoin join{predicate="auction.ID = bid.AUCTION", card = "ONE_MANY"}; ODLProject project{attributes=[auction.ID, auction.ITEMNAME, bid.PRICE]}; auction -> window; bid -> filter; [window, filter:1] -> joinThe Odysseus Operator Framework
algorithms for the processing of data but describes what to do with the data, e.g. in a projection it states the attributes to deliver, and in a join it gives the join predicates. A physical operator is one possible implementation of an algorithm to process the operation. So, different physical operators can be providedDEBS 2015 - Solution
}, profit_fare_plus_tip ) /// together profit_joined_empty_taxis = JOIN({ name= 'JoinCreating new Operators
will be modified (e.g. projection) NEW_ELEMENT: operator creates a new element (e.g. join) @Override public OutputMode getOutputMode() { return … operator before costly joins. The implementation is done by rewriting rules, which are implemented like transformation rules (see next section). TransformationTutorials
and Window: How to use windows and aggregations Join: How to combine information from different streamsCalcLatency operator
. Important: If you are using operators like Join, you additionally need to add #METADATA TimeInterval Parameter measurementPoint (String): If you want to use