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 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: 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.