Microsoft Technology Licensing, LLC

Capillary Technologies International Pte Ltd

Krishna Kumar Mehra

Trishul A Chilimbi

Sumit Gulwani

Kapil Vaswani

Sriram Kottarakurichi Rajamani

Prasun Kumar

Aditya Nori

Benjamin Robert Liblit

Aravinda P Sistla

Pravanjan Choudhury

Sumit K Jha

Abhilash L Lakkur

Sudipta Samanta

An ETL system for extracting transactional data from a plurality of source machines is provided. The ETL system includes a data query module configured to provide an interface to enable a data analyst to define an input query to extract transactional data, which is present on a plurality of source machines. The source machines form a multi-tenant system and each source machine is identified by a unique tenant identifier. The system also includes a query parsing module coupled to the data query module and configured to deconstruct the input query to create a tree structure. The tree structure comprises a plurality of distinct queries. The system further includes a data extraction module coupled to the query parsing module and configured to extract transactional data based on the distinct queries and copy the transactional data to a destination system wherein the transactional data comprises a plurality of data types.

Extract, transform and load (ETL) system and method

Bounding resource consumption of code that processes recursive data structures and collections includes making use of quantitative functions (based on user input) that are associated with a tuple of data-structures and whose semantics is specified by describing the effect of various data-structure methods on the relevant quantitative functions. Counter variables are incorporated into source code to count loop iterations (and number of recursive procedure call invocations). Relevant quantitative functions are incorporated into the source code to allow computation of invariants (and hence bounds) on the incorporated counter variables in terms of the quantitative functions.

Calculating resource bounds of programs manipulating recursive data structures and collections

Bounding resource consumption of code using abstract interpretation includes a static analysis to estimate a code's resource consumption in terms of units of resources utilized at any point during execution, expressed as a function of its scalar inputs. An instrumentation mechanism and an abstract interpretation mechanism are employed to compute bounds on the code resource consumption. The instrumentation mechanism includes incorporating one or more counter variables in the source code to count the number of loop iterations and recursive procedure call invocations. The abstract interpretation mechanism includes computing invariants on the instrumented counter variables and scalar program variables to obtain bounds on the number of loop iterations and recursive procedure call invocations, which are then composed together to obtain resource bounds for the entire program.

Bounding resource consumption using abstract interpretation

The method executes the application and if there are no errors from the execution of the application, the method ends. If errors exist, the errors are collected from the execution of the application in an error report. Labeled application paths are created by adding a unique label to individual application paths where the application paths are individual loops and individual functions in the application. An analysis is created of the labeled application paths by executing the application with the labeled paths, reviewing the error report for data related to the labels and if an error is sufficiently related to application paths with labels, storing the path that created the errors in a report. If an error is not sufficient related to the application path with labels, the method is repeated by the creating the analysis again by substituting additional application paths for the application paths.

Statistical debugging using paths and adaptive profiling

An object relational map verification system is described. In some embodiments, the object relational map verification system can verify object relational maps and identify counterexamples when an object relational map cannot be verified. The object relational map verification system can verify an object relational map by (1) receiving objects, database schemas, query views, and update views; (2) generating first order logic formulae corresponding to the received objects, database schemas, query views, and update views; and (3) proving theorems indicated by the generated first order logic formulae. When the theorems are proved, the object relational map is verified. In some embodiments, the object relational map verification system can also generate models illustrating counterexamples when the theorem cannot be proved. The counterexamples provide data that the object relational map does not consistently store and then retrieve.

Object relational map verification system

Growing community of inventors

Bengaluru, India

Krishna Kumar Mehra