Palantir Technologies Inc.

Nir Ackner

Eric Lin

David Cohen

Jordan Potter

Mark Elliot

Adam Borochoff

Benjamin Duffield

Punyashloka Biswal

Mark Schafer

Matthew Lynch

Walker Burgin

Gregory O'Connor

Caitlin Colgrove

Matthew Sills

Sander Kromwijk

Stephanie Yu

Gabrielle Javitt

Brandon Krieger

Alexandru Antihi

Samuel Szuflita

Phillip Chen

Diran Li

Helen Kaltegaertner

Shannon Scott

Maria Franco

The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. The data analysis system enables the user to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the user over the course of an investigation, as captured via the user's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on. The data analysis system enables a user to refresh or insert a node in a workflow. The data analysis system further provides data editing, reporting, enrichment, expression language, and charting functionality.

Interactive user interface for dynamically updating data and data analysis and query processing

A method comprises selecting a sample excerpt from a data input file; in response to the determining that a first row in the sample excerpt does not contain a delimited value and a second row does contain a delimited value, determining that the first row consists of header data; identifying one or more jagged rows based on row delimiters that were erroneously placed; causing displaying text that led to creation of a jagged row; receiving an addition or removal of a specific row delimiter to the text; updating the sample excerpt based on the addition or the removal; analyzing the sample excerpt to determine a row delimiter for the data input file; identifying a plurality of rows that is not included in the header data; identifying a plurality of candidate column delimiters and generating a candidate schema for the data input file.

Inferring a dataset schema from input files

Techniques for generating a schema for a data input file are described herein. In an embodiment, a server computer receives a data input file. The server computer system selects a sample excerpt from the data input which comprises a subset of the data input file. The server computer system analyzes the sample excerpt to determine a row delimiter for the data input file, a column delimiter for the data input file, and a plurality of data format types. Using the column delimiter, row delimiter, and plurality of data format types, the server computer system generates a candidate schema for the data input file.

A method and apparatus for a data analysis system for analyzing data object collections is provided. The data analysis system includes one or more graphical user interfaces comprising various interface elements that enable users to create visual queries. A visual query is constructed as a graph representing a pattern of interest in a collection of data objects. A visual query may include one or more graph elements and property information associated with the specified graph elements. After a user has constructed a visual query, the system may transform the visual query into a query template. A query engine may then execute the query template to search a data object collection for data object results corresponding to the specified pattern. The search for instances of a specified pattern in a collection of data objects is referred herein to as a 'search around.'

Search around visual queries

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Palo Alto, CA, United States of America

Nir Ackner