Authors:
Pavel Surynek
1
and
Petra Surynková
2
Affiliations:
1
Charles University Prague, Czech Republic
;
2
Charles University in Prague, Czech Republic
Keyword(s):
Big Data, Data Analysis, Logic Reasoning, Graph Theory, Graph Drawing, Propositional Satisfiability.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Domain Analysis and Modeling
;
Enterprise Software Technologies
;
Intelligent Problem Solving
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
This paper addresses a problem of knowledge discovery in big data from the point of view of theoretical computer science. Contemporary characterization of big data is often preoccupied by its volume, velocity of change, and variety that causes technical difficulties to handle the data efficiently while theoretical challenges that are offered by big data are neglected at the same time. Contrary to this preoccupation with technical issues, we would like to discuss more theoretical issues focused on the goal briefly expressed as what be understood from big data by imitating human like reasoning through logic and algorithmic means. The ultimate goal marked out in this paper is to develop an automation of the reasoning process that can manipulate and understand data in volumes that is beyond human abilities and to investigate if substantially different patterns appear in big data than in small data.