Abstract: In one embodiment, a system is provided. The system includes a memory device for storing templates; and a processing device, operatively coupled to the memory device, to select a template in view of user input. The template includes one or more embedded data fields associated with a study. A query comprises a tree of operators in view of the embedded data fields is derived. The set of operators being adapted to evaluate events data stored in a longitudinal database. Results corresponding to the query are generated by applying at least one operator of the tree of operators to the events data. The results including a time series of outcomes related to the events data. Thereafter, the template is populated with a least a portion of the results for presentation in connection with the study.

Abstract: In one embodiment, a method is provided. The method comprises receiving a query comprising a time period associated with events data stored in a longitudinal database. Using a processing system, a variable data structure is identified in view of the query. The variable data structure is used to identify attributes associated with the events data from the longitudinal database. A tree of operators is generated to evaluate the events data in view of the variable data structure and the time period. Thereupon, results corresponding to the query are provided by applying at least one operator of the tree of operators to the variable data structure. The results include a time series of outcomes related to the events data.

Abstract: In one embodiment, a method of assembling a longitudinal database is provided. The method comprises receiving, using a processor of a data storage unit, medical data streams from a plurality of sources. The medical data streams include patient data related to a plurality of medical events. A first date range that indicates a time span associated with the patient is identified in view of the patient data. The medical events are normalized into a plurality of linking keys based on the first date range. The keys are adapted to longitudinally link each medical event in a sequence of occurrence. Each key of comprises an offset from a start date of the first date range for a corresponding medical event. The medical events are compressed in view of the keys. The compressing generates a location index for each medical event associated with the keys based on a corresponding offset.

Abstract: A database system performs analytics on longitudinal data, such as medical histories with events occurring to patients over time. Input data is processed into streams of events. A set of indexes of event characteristics is generated. A set of patient event histories, partitioned by patient, is generated. Several copies of event data are stored, each copy being structured to support a specific analytical task. Data is partitioned and distributed over several hardware nodes to allow parallel queries. Definitions of sets of candidate patients are translated into sets of filters applied to the set of indexes. Data for these candidates are input to analytical modules. Reports from analysis are automatically generated to be compatible with standard guidelines for reporting. Workflows support one task or a set of closely related tasks by offering the user a defined sequence of query options and analytic choices specifically arranged for the task.

Abstract: A database system performs analytics on longitudinal data, such as medical histories with events occurring to patients over time. Input data is processed into streams of events. A set of indexes of event characteristics is generated. A set of patient event histories, partitioned by patient, is generated. Several copies of event data are stored, each copy being structured to support a specific analytical task. Data is partitioned and distributed over several hardware nodes to allow parallel queries. Definitions of sets of candidate patients are translated into sets of filters applied to the set of indexes. Data for these candidates are input to analytical modules. Reports from analysis are automatically generated to be compatible with standard guidelines for reporting. Workflows support one task or a set of closely related tasks by offering the user a defined sequence of query options and analytic choices specifically arranged for the task.

Abstract: A method for automatically defining a query interface for a datamart is described. The datamart includes fact and dimension tables. The method comprises accessing a schema description and a query interface description for the datamart. The schema description specifies a schema, which in turn, defines the relationships between the fact tables and dimension tables of the datamart. The query interface description specifies the fields, related to the schema description, that can be used in a query and the way in which results are to be presented to the user. The fields correspond to columns and rows in the fact tables. The schema description is used to create a first set of commands to create and populate the fact and dimension tables. Additionally, a second set of commands to create the query interface is created. Some commands of the first set of commands are executed causing the creation and population of the tables.

Abstract: A method for automatically defining aggregates for use in a datamart is described. The datamart includes fact and dimension tables. The method comprises accessing a schema description and an aggregates description for the datamart. The schema description specifies a schema, which in turn, defines the relationships between the fact tables and dimension tables of the datamart. The aggregates description specifies the aggregates, which define, from the schema definition, which aggregate tables are to be created from the fact tables and dimension tables in the datamart. The data in the aggregates correspond to the pre-computed results of specific types of queries. In response to a query, the aggregates can be searched to determine an appropriate aggregate to use in response to that query. The schema description is used to create a first set of commands to create and populate the fact and dimension tables.