Abstract: A platform for data analytics may be provided in a hosted environment on a multi-tenant system. The platform provider may also provide transactional processing services. Data obtained from processing the transactional services may be stored in an n-dimensional cube with which analytics may be performed. A dimension and hierarchy model may be identified based on correlations between hierarchy dimensions and levels in a dataset, or in schema and queries related to the dataset. Correlations may be further based on data received from a data stream. Priority for calculating a hierarchy may be based on data received from a data stream.

Abstract: Prediction logic analyzes previous data usage activities of a customer process running on a host machine to generate a first prediction indicating that the customer process will request a first data set at a first time. The prediction logic retrieves the first data set from long-term storage and loads the first data set into memory on the host machine in advance of the first time in order to provide the customer process with access to first data set in the memory during a period between the first time and a second time. The prediction logic further generates a second prediction indicating that the customer process will not access the first data set for a threshold period of time after the second time and stores the first data set in the long-term storage at the second time.

Abstract: A data analysis system determines characteristics of a data set such as statistical measures, analytical insights, data trends, or relationships with other data sets. The system determines a level of importance for each determined characteristic using metadata associated with the data set, and, in some cases, user preferences provided by the user. Such metadata may include descriptive names, data types, and data characteristics of the data set and of data elements within the data set.

Abstract: A data analytics system may receive query definitions from which relationships between datasets may be identified. The query definitions may be analyzed to determine estimated costs and frequencies of combining a first and second dataset. Based on the cost and frequency, a combined dataset may be generated to by joining data from the first and second datasets. The combined dataset may be stored. Queries that comprise instructions to combine the first and second datasets may be processed by instead accessing the combined dataset.

Abstract: A resource management service implements techniques for provisioning a process with computing resources sufficient to process a query. A query is received and computing system resources sufficient to process the query are provisioned. A response to the query is generated by running the process with the provisioned computing system resources.

Abstract: Processes and systems are disclosed for selecting a producer system from a number of producer systems to lease to a consumer system. A leasing agent, in response to a request from the consumer system for access to a service at a producer system, can identify a producer system to lease to the lease requestor based, at least in part, on a selection weight associated with each producer system that the leasing agent is assigned. The selection weights can be modified based on status information associated with each of the producer systems. This status information may be obtain from the producer systems and/or from a consumer system that has previously accessed the producer system. The consumer system may provide the status information to the leasing agent as part of the consumer system's lease request.

Abstract: A hosted analytics system may be integrated with transactional data systems and additional data sources such real-time systems and log files. A data processing pipeline may transform data on arrival for incorporation into an n-dimensional cube. Correlation between patterns of events in transactional data may be identified. Upon arrival, new data may be transformed and incorporated into the n-dimensional cube. Similarity between the new data and a previously identified correlation may be determined and flagged.

Abstract: An online analytical processing system may comprise an n-dimensional cube partitioned into slices, in which each slice may represent data points at the intersections of fixed and variable dimensions. Computation of data points within a slice may be deferred. A dependency graph may be initially constructed, in which the dependency graph is utilized in a subsequent computation. Calculation of data points may be prioritized based on information indicative of a chance that the data points will be accessed.

Abstract: A hosted analytics system may be integrated with transactional data systems and additional data sources such real-time systems and log files. A data processing pipeline may transform data on arrival for incorporation into an n-dimensional cube. Correlation between patterns of events in transactional data may be identified. Upon arrival, new data may be transformed and incorporated into the n-dimensional cube. Similarity between the new data and a previously identified correlation may be determined and flagged.

Abstract: An online analytical processing system may comprise an n-dimensional cube structured using slice-based partitioning in which each slice comprises one or more hierarchies of data points. A region of a hierarchy may be classified according to computational demands associated with the region. A scaling or replication mechanism may be applied to the region based on the computational demands associated with that region.

Abstract: A data analysis system determines a set of characteristics of a data set that is provided by a user. In various embodiments, individual characteristics may be statistical measures, analytical insights, data trends, or relationships with other data sets. The data analysis system selects a subset of the characteristics to be presented to the user. In an embodiment, the data analysis system determines a level of importance for each characteristic based at least in part on metadata associated with the data set, and in some embodiments, user preferences provided by the user. In an embodiment, the metadata includes descriptive names, data types, and data characteristics of the data set and of data elements within the data set.

Abstract: A data analysis system provides data analytics to a user via a natural language interface. In various embodiments, the data analysis system identifies statistical measures, analytical insights, data trends, or relationships with other data sets, based at least in part on a natural language query provided by a user. In an embodiment, the data analysis system interprets the natural language query to produce a result, and the result is converted into a natural language result which is provided to the user. In an embodiment, the data analysis system acquires an audio stream of a conversation. In an embodiment, the data analysis system identifies the parties to the conversation, and further identifies datasets of the parties. In an embodiment, the data analysis system identifies a characteristic of the datasets that is relevant to the conversation, and provides the characteristic to the parties.

Abstract: An analytics module may be embedded into an application developed, published, or used by an entity in addition to the owner of the data under analysis. An access token may be submitted by the analytics module to a provider of hosted services. The access token may correspond to an n-dimensional cube containing data at a level of granularity permitted to the application. The access token may incorporate additional policies controlling access to the corresponding n-dimensional cube.

Abstract: Data may be stored using hybrid multidimensional and column-centric storage techniques. A hierarchy of regions of a multidimensional space may be maintained on one or more storage devices. Range information for the hierarchy may be maintained in a column-centric storage. Leaf nodes of the hierarchy may comprise tuple data stored in a column-centric storage. Tuples may be located by identifying candidate regions encompassing the tuple and scanning column-centric stores at the leaf level. Region splitting may be deferred to favor column-centric search characteristics.

Abstract: Processes and systems are disclosed for selecting a producer system from a number of producer systems to lease to a consumer system. A leasing agent, in response to a request from the consumer system for access to a service at a producer system, can identify a producer system to lease to the lease requestor based, at least in part, on a selection weight associated with each producer system that the leasing agent is assigned. The selection weights can be modified based on status information associated with each of the producer systems. This status information may be obtain from the producer systems and/or from a consumer system that has previously accessed the producer system. The consumer system may provide the status information to the leasing agent as part of the consumer system's lease request.

Abstract: A hosted analytics system may be integrated with transactional data systems and additional data sources such real-time systems and log files. A data processing pipeline may transform data on arrival for incorporation into an n-dimensional cube. Correlation between patterns of events in transactional data may be identified. Upon arrival, new data may be transformed and incorporated into the n-dimensional cube. Similarity between the new data and a previously identified correlation may be determined and flagged.

Abstract: Processes and systems are disclosed for selecting a producer system from a number of producer systems to lease to a consumer system. A leasing agent, in response to a request from the consumer system for access to a service at a producer system, can identify a producer system to lease to the lease requestor based, at least in part, on a selection weight associated with each producer system that the leasing agent is assigned. The selection weights can be modified based on status information associated with each of the producer systems. This status information may be obtain from the producer systems and/or from a consumer system that has previously accessed the producer system. The consumer system may provide the status information to the leasing agent as part of the consumer system's lease request.

Abstract: A multi-tenant system for providing hosted analytic services may be dynamically configured in response to a request from a user. A request for analytic services may comprise an indication of at least one data source to be incorporated into an n-dimensional cube. A data source connector and transformation pipeline may transform data received from the data source to a format compatible with a dimension and hierarchy model of the n-dimensional cube.

Abstract: Systems and methods aggregate data and use caching techniques. In one implementation, a computer-implemented method aggregates data. A server receives a request for data that includes one or more identifiers. Based on at least one of the one or more identifiers, at least one template defining a view of the data is retrieved. One or more modules are identified based on the at least one template and one or more services are invoked to obtain the one or more modules. The one or more modules are received and processing by applying one or more business logic rules. At least a portion of the one or more processed modules are stored in a data store. The view of the data is generated based on the processed one or more modules and a response is transmitted to a client that includes the view of the data.

Abstract: Processes and systems are disclosed for a remote messaging protocol that combines application data and reliability information into a three-packet handshake exchange. Each packet may comprise message information indicating an initial packet, or an acknowledgement packet, along with a unique identifier for identifying responses to the initial message. Time-to-live and retransmission timers may be used in order to increase reliability of the protocol.