Abstract: A computer-implemented method, a computer program product, and a computer system for measuring relevance of datasets to data science models. One or more servers implement steps: extract keywords in each data science model; determine first relative frequencies of the respective keywords in each data science model, for each source group in the data science models; extract keywords in each dataset; determine second relative frequencies of the respective keywords in each dataset, for each source group in the datasets; determine weights of the keywords; calculate first aggregated relevant scores of the respective keywords in each data science model, based on the first relative frequencies and the weights; calculate second aggregated relevant scores of the respective keywords in each dataset, based on the second relative frequencies and the weights. One or more servers calculate similarity between vectors of the first and second aggregated relevant scores, based on a similarity measure between vectors.

Abstract: A computer-implemented method, a computer program product, and a computer system for measuring relevance of datasets to data science models. One or more servers implement steps: extract keywords in each data science model; determine first relative frequencies of the respective keywords in each data science model, for each source group in the data science models; extract keywords in each dataset; determine second relative frequencies of the respective keywords in each dataset, for each source group in the datasets; determine weights of the keywords; calculate first aggregated relevant scores of the respective keywords in each data science model, based on the first relative frequencies and the weights; calculate second aggregated relevant scores of the respective keywords in each dataset, based on the second relative frequencies and the weights. One or more servers calculate similarity between vectors of the first and second aggregated relevant scores, based on a similarity measure between vectors.

Abstract: A computer-implemented method for quick data exploration of data to be uploaded may be provided. The method comprises uploading, from a local system, a first data set of a first size and determining that the first data set is not corrupted. The method also comprises in parallel to the uploading performing selecting from the first data set a predefined number of records and building a second data set, determining statistical data and metadata about the first data set, and visualizing the second data set, the statistical data and the metadata.

Abstract: Technology for making corrections to URL and/or URI requests (herein generically referred to as “UR requests”) based on machine learning of patterns that can be gleaned from previously received UR requests. The patterns are then used to correct new UR requests that are received with errors in the resource location path. In some embodiments, the errors detected and corrected are path errors. In some embodiments the path error is an erroneous inclusion of an extraneous reserved slash (“/”) character, which, when detected, is replaced in the UR string with its corresponding proper code (for example, - -%2F- -).

Abstract: A computer-implemented method that can prevent an upload of a data set upon detection of a modification of the data set. The method includes storing a first portion of a file in a buffer while being in a receiving mode. Upon determining that applying one of a predefined transformation performed to the first portion of an existing data set will result in reproducing a content of the buffer: generating a transformation notification signal; and upon receiving a stop message, stopping the receiving mode which results in using the existing file on the server.

Abstract: A computer-implemented method for quick data exploration of data to be uploaded may be provided. The method comprises uploading, from a local system, a first data set of a first size and determining that the first data set is not corrupted. The method also comprises in parallel to the uploading performing selecting from the first data set a predefined number of records and building a second data set, determining statistical data and metadata about the first data set, and visualizing the second data set, the statistical data and the metadata.

Abstract: In an approach to reducing resource utilization in cloud-based data services, one or more computer processors select a dataset for upload to a server. One or more computer processors determine a data transformation scheduled to be applied to the dataset by the server. One or more computer processors perform a dataset read on the dataset. One or more computer processors perform the data transformation on the dataset. One or more computer processors upload the transformed dataset to the server.

Abstract: Technology for making corrections to URL and/or URI requests (herein generically referred to as “UR requests”) based on machine learning of patterns that can be gleaned from previously received UR requests. The patterns are then used to correct new UR requests that are received with errors in the resource location path. In some embodiments, the errors detected and corrected are path errors. In some embodiments the path error is an erroneous inclusion of an extraneous reserved slash (“/”) character, which, when detected, is replaced in the UR string with its corresponding proper code (for example, - -%2F- -).

Abstract: A computer-implemented method that can prevent an upload of a data set upon detection of a modification of the data set. The method includes storing a first portion of a file in a buffer while being in a receiving mode. Upon determining that applying one of a predefined transformation performed to the first portion of an existing data set will result in reproducing a content of the buffer: generating a transformation notification signal; and upon receiving a stop message, stopping the receiving mode which results in using the existing file on the server.

Abstract: Distributed version control in a multi-region cloud in which each region includes an instance of a version control service. A first version control service in a first region receives an indicator to re-version a document saved in the first cloud region. For re-versioning due to creation of a new version of the document in the first cloud region, first version control service re-versions the document by saving the created new version of the document in the first cloud region. The first version control service identifies access information to a second version control service in a second cloud region and transmits the re-versioned document to the second version control service. For a re-versioning due to receiving a new version of the document from a second version control service, the first version control service re-versions the document by saving the received new version of the document in the first cloud region.

Abstract: Distributed version control in a multi-region cloud in which each region includes an instance of a version control service. A first version control service in a first region receives an indicator to re-version a document saved in the first cloud region. For re-versioning due to creation of a new version of the document in the first cloud region, first version control service re-versions the document by saving the created new version of the document in the first cloud region. The first version control service identifies access information to a second version control service in a second cloud region and transmits the re-versioned document to the second version control service. For a re-versioning due to receiving a new version of the document from a second version control service, the first version control service re-versions the document by saving the received new version of the document in the first cloud region.

Abstract: A method, computer program product, and computer system for identifying a first reference point on a display. A second reference point of an object of an application is identified, wherein the object is on the display. A distance is determined of the first reference point on the display from the second reference point of the object on the display. One or more properties of the application is adjusted based upon, at least in part, the distance of the first reference point on the display from the second reference point of the object on the display.

Abstract: A method, computer program product, and computer system for identifying a first reference point on a display. A second reference point of an object of an application is identified, wherein the object is on the display. A distance is determined of the first reference point on the display from the second reference point of the object on the display. One or more properties of the application is adjusted based upon, at least in part, the distance of the first reference point on the display from the second reference point of the object on the display.

Abstract: A method, computer program product, and computer system for identifying a first reference point on a display. A second reference point of an object of an application is identified, wherein the object is on the display. A distance is determined of the first reference point on the display from the second reference point of the object on the display. One or more properties of the application is adjusted based upon, at least in part, the distance of the first reference point on the display from the second reference point of the object on the display.

Abstract: A method, computer program product, and computer system for identifying a first reference point on a display. A second reference point of an object of an application is identified, wherein the object is on the display. A distance is determined of the first reference point on the display from the second reference point of the object on the display. One or more properties of the application is adjusted based upon, at least in part, the distance of the first reference point on the display from the second reference point of the object on the display.