Emc IP Holding Company LLC

Vitor Silva Sousa

Rômulo Teixeira De Abreu Pinho

Adriana Bechara Prado

Roberto Nery Stelling Neto

Rodrigo Rios Almeida De Souza

Jonas Furtado Dias

Paulo De Figueiredo Pires

Fábio André Machado Porto

Wagner Dos Santos Vieira

Altobelli De Brito Mantuan

Rodolpho Rosa Da Silva

Marcia Lucas Pesce

Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.

Facilitating detection of anomalies in data center telemetry

Techniques are provided for provenance-based software script reuse. One method comprises extracting provenance data from source code including, for example, source code fragments, wherein the extracted provenance data indicates a control flow and a data flow of the source code; encapsulating source code fragments from the source code that satisfy one or more similarity criteria as a reusable source code fragment; and providing a repository of encapsulated reusable source code fragments for reuse during a development of new software scripts. The repository of encapsulated reusable source code fragments optionally comprises a searchable database further including, for example, the provenance data, data annotations, input parameters and generated results for the corresponding source code fragment.

Provenance-based reuse of software code

Techniques are provided for data discovery and data integration in a data lake. One method comprises obtaining data files from a data lake, wherein each data file comprises multiple records having multiple fields; selecting multiple candidate fields from a data file based on a record type; determining a relevance score for each candidate field from the data file based on multiple features extracted from the data file; and clustering the scored candidate fields into clusters of similar domains using a hashing algorithm, wherein a given cluster comprises candidate fields, wherein multiple data files can be integrated based on a domain of the candidate fields in the given cluster. The relevance score for each candidate field is based on multiple features comprising, for example, features that take into account a morphological or semantic similarity between file name, file metadata and/or file records and features that consider statistics of candidate fields in a data file.

Automatic indexing of relevant domains in a data lake for data discovery and integration

Growing community of inventors

Niterói, Brazil

Vitor Silva Sousa