Trifacta Inc.

Splunk Inc.

Philip John Vander Broek

Sean Philip Kandel

Wei Zheng

Adam Silberstein

Edward Eli Marschner

Alon Bartur

Jeffrey Heer

Seshadri Sadasivan Mahalingam

Zain Asgar

Lars Grammel

Vihang Jitendra Mehta

Systems and methods are provided for an enhanced graphical user interface (GUI) for presenting events. In particular, a graphical user interface includes, a visualization of unique values. Such a visualization includes a first set of rows for each unique value, wherein each row is divided into regions each representing a timeslot within the time range. Each region in a particular row of the first set of rows graphically illustrates a count of events from the plurality of events that both have an associated timestamp that is within a respective timeslot represented by the region and include a particular unique value associated with the particular row. The graphical user interface also includes a table including a second set of rows that each correspond to the first set of rows. Each row includes a set of statistics determined from events that each include the particular unique value associated with the row.

Enhanced graphical user interface for representing events

A system standardizes values that occur in large datasets before the dataset is analyzed. The system identifies values in a dataset that are similar to each other and associates those values with each other to form groups. The system determines a canonical value for each group of associated values. Within each group, the system replaces values that have been associated with each other with the canonical value for the group. As a result, the dataset is transformed into a dataset that has standardized values, and the standardized dataset is provided as input for analysis by a data analysis system. By standardizing the dataset in this manner, the data analysis system can process a larger portion of the dataset.

Standardizing values of a dataset

A system analyzes transformations for processing datasets. The transformations may be used for build a transformation script for preprocessing data for analysis by big data analysis systems. The system receives a new transformation for analysis. The system determines a measure of an impact of the new transformation operation on a dataset. The system determines statistical information describing rows of the transformed dataset that are impacted by the new transformation. The system receives request to add the new transformation to the transformation script responsive to presenting the statistical information.

Analyzing transformations for preprocessing datasets

A system determines samples of datasets that are typically processed by big data analysis systems. The samples are for use for development and testing of transformations for preprocessing the datasets in preparation for analysis by big data systems. The system receives one or more transform operations input datasets for the transform operations. The system determines samples associated with the transform operations. According to a sampling strategy, the system determines samples that return at least a threshold number of records in the result set obtained by applying a transformation. According to another sampling strategy, the system receives criteria describing the result of the transform operations and determines sample sets that generate result sets satisfying the criteria as a result of applying the transform operations.

Sampling for preprocessing big data based on features of transformation results

Transformation based sampling for preprocessing big data

A system provides data profile information describing attributes of a dataset. The system determines relative frequency of occurrences of attribute values with respect to a set of bins from a histogram of another attribute. The system presents a user interface that presents statistical information describing attributes of a dataset based on the relative frequency of occurrences of attribute values. The system generates a transformation script based on the user interactions for transforming records of the dataset. The transformation script is configured to preprocess data of the dataset for further analysis.

Data profiling of large datasets

Systems and methods are provided for visualizing the number of events having different values for a field of interest over a selected time range. The events may be derived from machine data obtained from one or more data sources. User input received via a graphical user interface may specify the field of interest, a time range, and a time granularity for displaying counts of the number of events having various values during different time slots within the selected time range. Events including the specified field during the user-selected time range are identified and values for the field are extracted from the identified events. A visualization indicating a relation between a number of the events occurring within each of a plurality of time slots over the selected time range and each of the unique extracted values of the field is provided to the user via the graphical user interface.

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San Francisco, CA, United States of America

Philip John Vander Broek