Numerify, Inc.

Digital.ai Software, Inc.

Firstrain, Inc.

Gaurav Rewari

Rahul Kapoor

Sadanand Sahasrabudhe

Florian Schouten

Aravind Sridharan

Renu Chintalapati

Ravishankar Muniasamy

David Shenk

Srinivas M Vedagiri

Binay Mohanty

Rohan Aranha

Xiang Yu

Kari Clement

Approaches for a model-driven tool-agnostic reporting approach are provided. In one example, a report request is received from a business intelligence reporting application, from which a graphical template and query template are created, wherein the graphical template comprises the visual presentation of data responsive to the request and the query template specifies how the data responsive to the request is to be computed. The report created by the approaches described herein is compatible with multiple business intelligence reporting applications.

Model driven reporting

Automatically assessing the health and predicting a probability of outage of a configuration item. A system automatically assesses the health of a configuration item and determines the probability of its outage by analyzing information associated with the current conditions of the configuration item obtained from an information technology service management (ITSM) system, an application performance management (APM) system or a machine data (MD) analysis system. The system further determines a probability of outage and health degradation of a set of configuration items impacted due to a Change by matching essence key phrases of the requested Change with outage predictor list for outage probability and health degradation predictor list for health degradation probability. The system further monitors configuration items impacted by a Change by analyzing information for the associated configuration items obtained from the ITSM, APM and MD analysis systems before and after the requested Change.

Automated health assessment and outage prediction system

Approaches for integrating an operational reporting system with a warehouse based reporting system. The operational reporting system and the warehouse based reporting system may be offered and supported by different vendors, and so may be deployed in different clouds or as separate product installations in case of on-premise software, or these systems may be offered and supported by a single vendor, and so may be deployed in the same cloud or as part of a common product installation in case of on-premise software. The integrated system uses a common or consistent dimensional model to link related measures of the operational reporting system and the warehouse based reporting system, which allows for analysis of real time content with historical and cross subject perspective provided alongside in the same interface. The integration also facilitates trend analysis and predictive analytics in real time.

Warehouse based reporting and operational reporting integration

An automated input gathering system for defining, refining or validating a data warehouse star schema for a given data source. The automated data input gathering system includes a source metadata analysis module, an operational reporting environment analysis module, a usage analysis module and a data profiling module. To automatically ascertain inputs for warehouse definition, refinement or validation like i) list of entities and most relevant ones ii) entity column types and lengths iii) entity keys iv) relationships between entities v) relationships within entities vi) entity and column update frequencies vii) grouping of entity and column updates viii) specialized entities ix) workflow and correlated attributes and x) commonly used measures the source metadata analysis module programmatically analyzes source metadata documentation or API's; the operational reporting environment analysis module analyzes underlying models, reports and dashboards; the usage analysis module analyzes queries on the source database; and the data profiling module analyzes data in the source database.

Input gathering system and method for defining, refining or validating star schema for a source database

An automated system for defining a star schema for a data source. The system based on automatically gathered information from the data source such as entities and columns, entity column types and lengths, entity keys, relationships between and within entities, measures, workflow and correlated attributes, specialized entities, an update frequency of entities and columns, and grouping of entity and column updates associated with the source database automatically determines facts, dimensions, dimension hierarchies, measures, workflow specific measures (if data source has workflows) and workflow correlated attribute specific measures (if data source has temporal, priority, ownership and progress tracking attributes) to come up with a star schema for the data source.

Automated definition of data warehouse star schemas

Approaches for automatically determining of correlation for incident, problem and change items. Approaches may perform the steps of: (i) automatically extracting relevant information that includes any of (a) a time of occurrence, (b) important keywords, (c) a category, or sub-category classification, (d) related configuration items, (e) a group membership of the user who worked on, and (f) outage records associated with the incident, problem, and change items; and (ii) automatically analyzing persisted correlation maps using the essence key phrases associated with the incidents to identify essence key phrases of contributing problems, and changes; and (iii) automatically clustering the incidents, optionally with (A) the problems, and (B) the changes to identify clusters of related incident, problem and change items based on the relevant information associated with the incident, problem, and change items.

Automated incident, problem, change correlation analysis system

An automated system for validating and refining a template warehouse star schema for a customized data source. The system based on automatically gathered information from the data source such as custom entities and constituent columns, data type and length of custom columns, custom entity keys, relationships of custom entities with other entities, relationships within custom entities, contribution of custom entities to specialized entities, update frequency of custom entities and columns including grouping of updates, frequently used expressions on custom columns and contribution of custom entities and columns to workflow and correlated attributes automatically determines facts, dimensions, dimension hierarchies, measures, workflow specific measures (if data source has workflows) and workflow correlated attribute specific measures (if data source has temporal, priority, ownership and progress tracking workflow correlated attributes) to validate and refine the star schema of the template warehouse to accommodate customizations of the data source.

Automated refinement and validation of data warehouse star schemas

A source computing environment administering business intelligence (BI) processing at a target computing environment. The source computing environment storing intermediate design plans for creating dimensional models, reports, and ETL jobs for deployment in a plurality of different computing environments. At the source computing environment, target specific modules translating the one or more intermediate design plans into one or more target specific plans for dimensional models, reports, and ETL job plans which are interpretable or executable by the target computing environment. The source computing environment, in consultation with a workflow engine, instructing the target computing environment to provision data repositories, create reports, and perform ETL processing based on the one or more target specific plans.

Multi-step code generation for bi processes

Approaches for providing a customizable external data feed to a recipient. Metadata that identifies a plurality of configuration parameters that define characteristics of the external data feed are stored. A plurality of executable jobs using the configuration parameters defined in the metadata are programmatically generated. At least a first portion of executable jobs are executed to extract a data set identified by the configuration parameters from a persistent data store. At least a second portion of executable jobs are executed to format and transform the data set according to the configuration parameters. The external data feed, which comprises the formatted and transformed data set, is provided to the recipient.

Metadata driven code-generated external data feeds

Approaches for a persistent data store that may be used to service data queries originating from data warehousing applications, operational reporting applications, or data archiving and data feed applications. At periodic intervals, data may be extracted from one or more disparate data sources. The extracted data may be stored in the persistent data store in a tabular format. A backward delta data set is generated. The backward delta data set identifies what additions, deletions, and modifications need to be made to the data stored in the persistent data store to return the data to a most recent version. Backward delta data sets are stored and maintained in accordance with a policy that instructs two or more backward delta data sets, associated with a continuous interval of time, be combined into a single backward delta data set after the expiration of a specified amount of time.

Common extract store

Approaches for a user-driven warehousing approach are provided, wherein usage patterns for business intelligence applications are gathered, for example in an automated recording fashion, allowing the automated scheduling of jobs in a manner that prioritizes jobs that populate the most-used tables and scheduling those jobs in a manner to ensure that the data is up-to-date prior to when it is generally accessed. The usage pattern analysis also allows for the automated identification of more focused data marts for particular situations. The usage pattern analysis also provides for automated data warehouse/data mart creation and customization based on usage patterns that may be used as a seed, as well as for on-the-fly latitudinal analysis across prepackaged domain-specific applications.

User driven warehousing

Embodiments herein receive a set of content quality threshold values, a search string, and a content data stream at a content quality metric (CQM) apparatus. Content segments associated with the content data stream are scored and/or graded according to a set of content relevance scales. The content data stream is then filtered to include only passing content segments and intermediate calculation values used to determine whether a content segment is passing. Other embodiments are described and claimed.

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Cupertino, CA, United States of America

Gaurav Rewari