Abstract: Software for a distributed streaming platform receives an application. The application is structured as a directed acyclic graph (DAG) with instances of operators as nodes and streams as edges between nodes. Multiple instances of an operator access a shared database. The software receives a pre-defined hint associated with the application. The pre-defined hint sets a maximum period of time for local caching of a result from a query of the database by each of the multiple instances. The software launches the application by assigning the instances of operators to one or more containers provided by the streaming platform and initiating the streams. Each container is associated with a local cache. The software then receives a request from the application to make a dynamic adjustment that increases the maximum period of time for local caching of a result from a query of the database by each of the multiple instances.

Abstract: Software for a distributed streaming platform receives an application. The application is structured as a directed acyclic graph (DAG) with instances of operators as nodes and streams as edges between nodes. Multiple instances of an operator access a shared database. The software receives a pre-defined hint associated with the application. The pre-defined hint sets a maximum period of time for local caching of a result from a query of the database by each of the multiple instances. The software launches the application by assigning the instances of operators to one or more containers provided by the streaming platform and initiating the streams. Each container is associated with a local cache. The software then receives a request from the application to make a dynamic adjustment that increases the maximum period of time for local caching of a result from a query of the database by each of the multiple instances.

Abstract: Software for a distributed streaming platform receives an application that runs on a streaming platform. The application is structured as a directed acyclic graph (DAG) with operators as nodes and streams as edges between nodes. The application includes an operator that receives a plurality of values, counts the values that are unique, and emits the unique values in a stream. The software partitions the operator into at least two partitioned instances. Then the software assigns a unique value emitting from a partitioned instance to one of a plurality of unifiers according to a pre-defined scheme. Each unifier creates a count of the unique values received by the unifier. Then the software transmits the counts from each of the unifiers to a downstream instance of an operator that aggregates the counts into a sum. And the software displays the sum in a graphical user interface (GUI).

Abstract: Software for a distributed streaming platform launches an application that runs on a streaming platform, in real time or near real time rather than offline. The application is structured as a directed acyclic graph (DAG) with instances of operators as nodes and streams as edges between nodes. The software receives an indication that a first instance of an operator is I/O bound. A stream connects the first instance of an operator to a second instance of another operator in a single container provided by the streaming platform. The software then transmits the indication to the application and receives a request to combine the first instance with the second instance into a single third instance of an operator. The software creates the third instance and re-initiates the stream using a recovery policy.

Abstract: Software for a distributed streaming platform receives an application that runs on a streaming platform. The application is structured as a directed acyclic graph (DAG) with instances of operators as nodes and streams as edges between nodes. The application is associated with a pre-defined hint that is a key-value pair. The software launches the application by assigning the instances of operators to containers provided by the streaming platform and initiating the streams. Then the software reads a value for the pre-defined hint and transmits the value to the application through an application programming interface (API) exposed by the streaming platform. The software receives a request from the application through the API to make a dynamic adjustment. And the software makes the dynamic adjustment and re-launches the application using a recovery policy.

Abstract: A method for sampling of streaming data for distribution on a network (e.g., the Internet) includes receiving the steaming data (which includes periodically updated information items) from a streaming data source. The periodically updated information items of the streaming data are subsequently sampled using a sampling process based on a total update rate of the steaming data (Ua) and a capacity of the network (C), thereby creating a sampled data stream. Alternatively, the periodically updated information items of the streaming data can be sampled using a sampling process that is also based on the importance (Kqmin) of each of the periodically updated information items and the update rate (Uq) of each of the periodically updated information items to create the sampled data stream. The sampled data stream is then distributed on the network (e.g., the Internet or a private area network).

Abstract: A method of filtering real-time financial data to provide accurate data to users is provided. The method includes comparing the trading price of a currently reported stock trade with the trading price of a previously reported stock trade and identifies the currently reported stock trade as a potential bad trade if the percentage difference between the two is greater than a reference percentage. The real-time spike filter confirms the currently reported stock trade as a bad trade based on: (i) a comparison of the trading prices of the currently reported stock trade and a next reported stock trade; and (ii) a comparison of the trading prices of the previously reported stock trade and the next reported stock trade.

Abstract: A system and method may allow an advertiser to sign a conditional contract with an advertising network so that the advertiser's advertisement may be displayed on a web page only when the network traffic thereto exceeds a threshold level. The system may have a network traffic monitoring server which may obtain information about actual network traffic level to the web page, compare the actual network traffic level to the web page with the threshold level, and output an enabling signal when the actual network traffic level exceeds the threshold level. An advertising network server may store the contract between the advertiser and the advertising network, send the threshold level to the network traffic monitoring server, and be configured to display the advertiser's advertisement on the web page only when it receives the enabling signal from the network traffic monitoring server.

Abstract: A method for sampling of streaming data for distribution on a network (e.g., the Internet) includes receiving the steaming data (which includes periodically updated information items) from a streaming data source. The periodically updated information items of the streaming data are subsequently sampled using a sampling process based on a total update rate of the steaming data (Ua) and a capacity of the network (C), thereby creating a sampled data stream. Alternatively, the periodically updated information items of the streaming data can be sampled using a sampling process that is also based on the importance (Kqmin) of each of the periodically updated information items and the update rate (Uq) of each of the periodically updated information items to create the sampled data stream. The sampled data stream is then distributed on the network (e.g., the Internet or a private area network).

Abstract: A method for sampling of streaming data for distribution on a network (e.g., the Internet) includes receiving the streaming data (which includes periodically updated information items) from a streaming data source. The periodically updated information items of the streaming data are subsequently sampled using a sampling process based on a total update rate of the streaming data (Ua) and a capacity of the network (C), thereby creating a sampled data stream. Alternatively, the periodically updated information items of the streaming data can be sampled using a sampling process that is also based on the importance (Kqmin) of each of the periodically updated information items and the update rate (Uq) of each of the periodically updated information items to create the sampled data stream. The sampled data stream is then distributed on the network (e.g., the Internet or a private area network).

Abstract: A method for sampling of streaming data for distribution on a network (e.g., the Internet) includes receiving the streaming data (which includes periodically updated information items) from a streaming data source. The periodically updated information items of the streaming data are subsequently sampled using a sampling process based on a total update rate of the streaming data (Ua) and a capacity of the network (C), thereby creating a sampled data stream. Alternatively, the periodically updated information items of the streaming data can be sampled using a sampling process that is also based on the importance (Kqmin) of each of the periodically updated information items and the update rate (Uq) of each of the periodically updated information items to create the sampled data stream. The sampled data stream is then distributed on the network (e.g., the Internet or a private area network).