abstract

Using "big data" to solve "small data" problems

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Chris NeumannAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

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https://doi.org/10.1145/2487575.2491135

Published: 11 August 2013 Publication History

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Abstract

The brief history of knowledge discovery is filled with products that promised to bring "BI to the masses". But how do you build a product that truly bridges the gap between the conceptual simplicity of "questions and answers" and the structure needed to query traditional data stores?

In this talk, Chris Neumann will discuss how DataHero applied the principles of user-centric design and development over a year and a half to create a product with which more than 95% of new users can get answers on their first attempt. He'll demonstrate the process DataHero uses to determine the best combination of algorithms and user interface concepts needed to create intuitive solutions to potentially complex interactions, including:

Determining the structure of files uploaded by users

Accurately identifying data types within files

Presenting users with an optimal visualization for any combination of data

Helping users to ask questions of data when they don't know what to do

Chris will also talk about what it's like to start a "Big Data" company and how he applied lessons from his time as the first engineer at Aster Data Systems to DataHero.

Index Terms

Using "big data" to solve "small data" problems
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2013

1534 pages

ISBN:9781450321747

DOI:10.1145/2487575

Editors:
Rayid Ghani
University of Chicago
,
Ted E. Senator
SAIC
,
Paul Bradley
MethodCare, Inc.
,
Rajesh Parekh
Groupon
,
Jingrui He
Stevens Institute of Technology
,
General Chairs:
Robert L. Grossman
University of Chicago and Open Data Group
,
Ramasamy Uthurusamy
General Motors Corporation (retired)
,
Program Chairs:
Inderjit S. Dhillon
University of Texas
,
Yehuda Koren
Google

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2013

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Conference

KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11 - 14, 2013

Illinois, Chicago, USA

Acceptance Rates

KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Index Terms

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From Big Data to Big Data Mining: Challenges, Issues, and Opportunities

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