Intelligent analysis of Big Data extendible collections under the limits of process-real time

The problem how to extract relevant to the fixed goal data from regularly extended by new information collections of Big Data not braking given limits for data analysis and decision making (being in agreement with so-called process-real time restrictions) is discussed. The proposed approach is based on implementation of modern artificial intelligence techniques including knowledge representation and reasoning formalization for so-called Intelligent Data Analysis (IDA) computer systems. Some critical barriers preventing efficient application of this type IDA (e. g., computational complexity of some related to IDA combinatorial problems, including provable getting some of them in well-known classes of computationally hard problems, some characteristic features of knowledge representation and search iteration enumeration control, optimization of accuracy, and completeness of search results) are analyzed. A formalized description for the designed IDA set of procedures is presented. The discussed approach is illustrated by examples of its implementation in a corporate computer system of malicious insider activities identification and counteraction operating in a large Russian commercial bank.