Abstract: An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective sample sources, with each of the biological samples containing genomic material from a plurality of distinct microorganisms within an environment of a corresponding one of the sample sources, and to perform distributed data analytics to provide surveillance functionality characterizing at least one of a disease, an infection and a contamination as involving genomic material from multiple ones of the sample source. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Abstract: An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective sample sources, with each of the biological samples containing genomic material from a plurality of distinct microorganisms within an environment of a corresponding one of the sample sources, and to perform distributed data analytics to characterize an actual or potential outbreak of at least one of a disease, an infection and a contamination that involves genomic material from multiple ones of the distinct microorganisms in one or more of the sample sources. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Abstract: An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective sample sources, with each of the biological samples containing genomic material from a plurality of distinct microorganisms within an environment of a corresponding one of the sample sources, and to perform distributed data analytics to provide surveillance functionality characterizing at least one of a disease, an infection and a contamination as involving genomic material from multiple ones of the sample source. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Abstract: A method comprises initiating a first application in a first one of a plurality of distributed processing nodes, and responsive to initiation of the first application, identifying a plurality of beacon entities to be contacted in conjunction with execution of at least a portion of the first application. The method also comprises, for each of at least a subset of the identified beacon entities, initiating an additional application in an additional one of the plurality of distributed processing nodes. The method further comprises aggregating processing results from the first and one or more additional processing nodes, and providing the aggregated processing results to a client. The plurality of distributed processing nodes may comprise a plurality of YARN clusters associated with respective data zones, with each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone.

Abstract: A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Abstract: A method comprises configuring a first metagenomics sequencing center to perform metagenomics sequencing on a first set of biological samples, and processing metagenomics sequencing results from the first metagenomics sequencing center with additional metagenomics sequencing results from one or more other metagenomics sequencing centers for respective additional sets of biological samples in conjunction with associated epidemiological data accessible to respective ones of the first and additional metagenomics sequencing centers. The processing is configured to generate global view information characterizing epidemiological relationships between the metagenomics sequencing results from the first and additional metagenomics sequencing centers. The method further includes providing the global view information to a requesting client.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The one or more processing devices are operative to configure a plurality of distributed processing nodes to communicate over a network, to abstract content locally accessible in respective data zones of respective ones of the distributed processing nodes into respective catalogs of a distributed catalog service in accordance with a layered extensible data model, and to provide in the distributed processing nodes a plurality of microservices for performing processing operations on at least one of the layered extensible data model and the catalogs. The layered extensible data model comprises a plurality of layers including a core data model layer and at least one extensions layer. The microservices may comprise at least one microservice to alter the layered extensible data model and at least one microservice to query one or more of the catalogs.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to receive results of intermediate long-tail histogram computations performed on respective ones of a plurality of datasets in respective ones of a plurality of distributed processing nodes configured to communicate over at least one network. The processing device is further configured to perform at least one global long-tail histogram computation based at least in part on the results of the intermediate long-tail histogram computations, and to utilize results of the intermediate and global long-tail histogram computations to identify patterns in the plurality of datasets. The distributed processing nodes are illustratively associated with respective distinct data zones in which the respective datasets are locally accessible to the respective distributed processing nodes.

Abstract: An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective sample sources, with each of the biological samples containing genomic material from a plurality of distinct microorganisms within an environment of a corresponding one of the sample sources, and to perform distributed data analytics to characterize an actual or potential outbreak of at least one of a disease, an infection and a contamination that involves genomic material from multiple ones of the distinct microorganisms in one or more of the sample sources. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to receive results of intermediate percentile computations performed on respective ones of a plurality of datasets in respective ones of a plurality of distributed processing nodes configured to communicate over at least one network. The processing device is further configured to perform at least one global percentile computation based at least in part on the results of the intermediate percentile computations, and to utilize results of the intermediate and global percentile computations to populate an interactive user interface associated with at least a given one of the distributed processing nodes. The distributed processing nodes are illustratively associated with respective distinct data zones in which the respective datasets are locally accessible to the respective distributed processing nodes.

Abstract: An apparatus in one embodiment includes at least one processing device comprising a processor coupled to a memory. The processing device is configured to discover data sources associated with respective edge devices configured to communicate over at least one network, to obtain metadata characterizing the data sources, and to control performance of distributed analytics across a plurality of distributed processing nodes of the network utilizing the obtained metadata and locally accessible data provided by the corresponding data sources. In some embodiments, the edge devices comprise respective IoT gateways each associated with a set of IoT data sources. The IoT gateways are illustratively arranged in respective data zones that also encompass their respective sets of IoT data sources, and the distributed analytics is performed without requiring that locally accessible data from the IoT data sources leave the corresponding data zones.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to initiate distributed computations across a plurality of data processing clusters associated with respective data zones, and to combine local processing results of the distributed computations from respective ones of the data processing clusters. Each of the data processing clusters is configured to process data from a data source of the corresponding data zone using a local data structure and an associated computational framework of that data processing cluster. A first one of data processing clusters utilizes a first local data structure configured to support a first computational framework, and at least a second one of the data processing clusters utilizes a second local data structure different than the first local data structure and configured to support a second computational framework different than the first computational framework.

Abstract: An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective microbiomes, with each of the biological samples containing genomic material from a plurality of distinct microorganisms of its corresponding one of the microbiomes, and to perform distributed data analytics to detect a disease, infection or contamination that involves genomic material from multiple ones of the distinct microorganisms in one or more of the microbiomes. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to distribute in-memory computations across at least first and second nodes of respective distinct data processing clusters of a plurality of data processing clusters over at least one network, and to aggregate results of the distributed in-memory computations for delivery to a requesting client device. The data processing clusters are associated with respective distinct data zones, and the first and second nodes of the respective distinct data processing clusters are configured to perform corresponding portions of the distributed in-memory computations utilizing respective ones of first and second in-memory datasets locally accessible within their respective data zones. The in-memory computations in some embodiments illustratively comprise Spark computations, such as Spark Core batch computations.

Abstract: A computing device of a data zone includes a persistent storage and a processor. The persistent storage includes a data source. The processor obtains a global computation request, instantiates a global computation based on the global computation request, and instantiates an intermediate computation in a second data zone based on the instantiated global computation.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The one or more processing devices are operative to configure a plurality of distributed processing nodes to communicate over a network, to obtain metadata characterizing data locally accessible in respective data zones of respective ones of the distributed processing nodes, and to populate catalog instances of a distributed catalog service for respective ones of the data zones utilizing the obtained metadata. Distributed data analytics are performed in the distributed processing nodes utilizing the populated catalog instances of the distributed catalog service and the locally accessible data of the respective data zones. The metadata characterizing the locally accessible data is illustratively obtained in a metadata repository from at least one of a master data management platform and a governance, risk and compliance platform.

Abstract: A method comprises receiving metagenomics data, configuring a data model characterizing relationships between aspects of the metagenomics data, and processing the metagenomics data in accordance with the configured data model in order to characterize at least one of a disease, infection or contamination. The data model comprises an abundance score element that relates portions of the metagenomics data comprising reads of biological samples to one or more genomic sequences of an ecogenome, and a comparative score element that relates portions of the metagenomics data comprising characteristics of multiple patients to one another with respect to the disease, infection or contamination. The data model further relates the abundance score element to the comparative score element via one or more additional elements of the data model corresponding to respective other aspects of the metagenomics data.

Abstract: An analytics platform is configured to receive a request to execute a distributed analytics workflow specification comprising multiple distributed computations to be performed across a plurality of data processing clusters associated with respective data zones. For each of a plurality of analytic services to be executed in accordance with the distributed analytics workflow specification, the platform determines if execution of the analytic service is authorized for the request, and for each of a plurality of datasets to be operated on by one or more of the analytic services in accordance with the distributed analytics workflow specification, the platform determines if execution of the one or more analytic services on the dataset is authorized by its corresponding data zone for the request.

Abstract: An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to receive results of intermediate statistical computations performed on respective ones of a plurality of datasets in respective ones of a plurality of distributed processing nodes configured to communicate over at least one network. The processing device is further configured to perform at least one global statistical computation based at least in part on the results of the intermediate statistical computations, and to utilize a result of the global statistical computation to perform one or more benchmarking operations for specified parameters relating to the plurality of datasets. The distributed processing nodes are associated with respective distinct data zones in which the respective datasets are locally accessible to the respective distributed processing nodes.

Abstract: An apparatus is configured to initiate distributed computations across a plurality of data processing clusters associated with respective data zones, to utilize local processing results of at least a subset of the distributed computations from respective ones of the data processing clusters to generate global processing results, and to update at least one distributed ledger maintained by one or more of the plurality of data processing clusters to incorporate one or more blocks each characterizing at least a portion of the distributed computations. Each of at least a subset of the data processing clusters is configured to process data from a data source of the corresponding data zone using one or more local computations of that data processing cluster to generate at least a portion of the local processing results.