Amadeus S.a.s.

Philippe François Georges Gentric

Yannick Guiraud Devaux

Charles-Antoine Robelin

Matthieu Delost

Gurvan Nicolas Huiban

Stephane Pierre-Henri Janel

Herve Fresnel Eblohoue

Carlos Lo-Iacono Garcia

Stéphane Pierre-Henri Janel

Hervé Fresnel Eblohoue

Data is handled in a distributed computing environment comprising at least one server and a plurality of clients comprising at least a first client and a second client. The first client sends a first request for data to the second client, receives a first response from the second client as a response to the first quest, determines a probability of validity of the data requested based on a validity indication included in the first response indicating a probability that the data requested from the second client is invalid. The first client determines that the data requested by the first request and available from the second client is likely invalid, sends a second request to the server to obtain the data from the server and receives the data from the server.

Distributed data processing

Data is processed in a distributed computing environment with at least one server and a plurality of clients comprising at least a first client and a second client. The first client sends a first request to the server to obtain result data, receives raw data from the server as a response to the first request, processes the raw data to obtain the result data and stores the result data, and sends the result data to the second client in response to receiving a third request to obtain the result data from the second client.

The present invention relates to a method and a system for maintaining consistency of data between data-sets stored in a master database of a master computing node and corresponding data-sets stored in a replication database of at least one replication computing node. Each time an updated version of a stored data set is received, the master computing node is configured for updating a corresponding data-set stored in the master database and transmitting replication data relating to the updated data-set version to the at least one replication computing node for replication. The replication data comprising a bit-vector storing data values extracted from data fields of the data-set classified as volatile data fields. The data values stored in the bit-vector representing the changes in the data-set to be replicated in the at least one replication node.

Maintaining consistency of data between computing nodes of a distributed computer architecture

Incremental data processing at a computerized device includes determining a number of data sets from a plurality of data sets, each comprising values in at least two dimensions. The device accesses priority lists for a subset of the data sets. The priority lists specify data values for an ordered number of dimension value sets. Each priority list is sequentially processed to determine the specified data values for combinations of dimension values that apply to device requirements. Processing is aborted when a data value is determined for each combination of the dimension values that apply to the device requirements. A data value is selected among the determined data values. A number of data sets is determined based on the selected data values. A network route from a source device to a target device can be determined in this manner.

Incremental data processing

Database queries are processed by a first client storing cached data. The first client and a second client host a probabilistic model yielding validity values associating with the cached data indicating a probability that the cached data stored coincides with corresponding original data. The first client randomly selects queries at the time the respective query is received from one of the plurality of clients. For each of the queries randomly selected, a first piece of the cached data stored at the first client and matching the randomly selected query is retrieved, and a first piece of the original data matching the randomly selected query from the at least one original data source is retrieved. The probabilistic model is adapted using a machine learning algorithm based on the retrieved first piece of the cached data and the retrieved first piece of the original data and made available to the second client.

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La-Colle-sur-Loup, France

Philippe François Georges Gentric