Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications

TL;DR

This paper explores the use of incremental, biologically-inspired mind-maps as adaptive symbolic engines for managing and analyzing data streams across various applications like network traffic, user behavior, and natural language interaction.

Contribution

It introduces a novel application of symbolic, biologically-inspired mind-maps for incremental data stream management and analysis in diverse real-world scenarios.

Findings

Mind-maps adaptively model normal and abnormal network traffic.

They support user behavior detection in search engines.

They can serve as a hidden communication layer for natural language.

Abstract

In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either stronger or weaker depending on the transactional stream. Based on the underlying biologic principle, these symbolic cells and their connections as well may adaptively survive or die, forming different cell agglomerates of arbitrary size. In this work, we intend to prove mind-maps' eligibility following diverse application scenarios, for example being an underlying management system to represent normal and abnormal traffic behaviour in computer networks, supporting the detection of the user behaviour within search engines, or being a hidden communication layer for natural language interaction.