Ankur Gupta


Ankur Gupta
Assistant Professor, Computer Science
Butler University, United States

Ankur Gupta is an Assistant Professor of Computer Science and Software Engineering at Butler University. He finished his PhD from Duke University in 2007 under the guidance of Dean Jeffrey Scott Vitter. Prior to that, he finished a Bachelors in Mathematics, a Bachelors in Computer Science, and a Masters in Computer Science from the University of Texas at Dallas in 2000. His research interests are broadly in the area of design and analysis of algorithms and data structures, with recent application to such topics as data compression, text indexing, and dynamic and streaming data.

Wisdom Is Compression: Data Compression as a Mathematical Measure of Wisdom
The world is drowning in data, and we are faced with the challenge of understanding it quickly and well. The idea of well-understood varies based on the data we have, but the universal goal is to distill the huge amount of information into its most essential components. This filtration process was considered a practical definition of wisdom by a number of thinkers in the Victorian Age. In their view, wisdom serves as a verifiable process of cognitive thought with respect to the real world. This pragmatic definition corresponds strongly with the nature of information from a computer scientist’s perspective, and in particular, to the task of compression. In an increasingly technical world, it is of critical importance to update our notions of wisdom to incorporate a new information-processing aspect to wisdom. It is no longer sufficient to consider a model where wisdom is dispensed by a human expert to a single individual. Computers can retain huge amounts of information and process it to find the answer to any question contained therein -- why disallow the concept of wisdom in this case? Careful organization of the data may address both the speed issue and the quality of the result; the organization requiring the least amount of memory capacity may be termed as wisdom. In this project, we draw a parallel between the definition of wisdom and compression, which is often achieved by reorganizing data to reduce redundancy.

We pursued a literature survey to define wisdom as applicable to a computing world. The initial thrust focused around developing the idea of in-time wisdom in literature; that is, the notion that wisdom is useless if it is not dispensed in a reasonable time. We study how to incorporate in-time constraints into our evaluation of the value of wisdom. We approached the claim that wisdom is compression with the development of a novel data structure (and algorithms) for the problem of finding items in an unsorted list of numbers, based on their rank (overall position in the sorted sequence) or value. The data structures involved also progressively sort and compress the input. Further improvement of these results is ongoing. The quality of the compression and the speed of access speak to the notion of in-time wisdom. The quantitative measure of wisdom is the compression ratio achieved; the speed of query access is the “in-time" component.

The results of this study are being submitted for publication, and are being disseminated on Butler campus through a pilot course on wisdom and data compression (Spring 2009). This course explains the technical basis for data compression, and its connection to the notion of wisdom. Particular emphasis is placed on how wisdom is distinct from information content.

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