A Note On Lookahead In Real Life And Computing

• URL: http://arxiv.org/abs/2403.17942v1
• Date: Fri, 2 Feb 2024 06:17:49 GMT
• Title: A Note On Lookahead In Real Life And Computing
• Authors: Burle Sharma, Rakesh Mohanty, Sucheta Panda,
• Abstract summary: Look-Ahead(LA) deals with the future prediction of information and processing of input to produce the output in advance. In this article, our main objective is to learn, understand and explore the concept of LA and design novel models as solution for real world problems. We introduce interesting real life applications and well known computing problems where LA plays a significant role for making a process, system or algorithm efficient.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Past, Present and Future are considered to be three temporal and logical concepts which are well defined by human beings for their existence and growth. We, as human beings, have the privilege of using our intelligence to mentally execute an activity before physical occurrence of the same in the real world. Knowledge of the past, aplomb of present and visualisation for the future correspond to three concepts such as look-back, look-at and look-ahead respectively in real life as well as in diversified domains of computing. Look-Ahead(LA) deals with the future prediction of information and processing of input to produce the output in advance. In this article, our main objective is to learn, understand and explore the concept of LA and design novel models as solution for real world problems. We present three well known algorithmic frameworks used in practice based on availability of input information such as offline, online and semi-online. We introduce interesting real life applications and well known computing problems where LA plays a significant role for making a process, system or algorithm efficient. We define new types of LA and propose a taxonomy for LA based on literature review for designing novel LA models in future. Using the concept of LA, We identify and present many interesting and non-trivial research challenges as future potential research directions. Intuitively, we observe that LA can be used as a powerful tool and framework for future researchers in design of efficient computational models and algorithms for solving non-trivial and challenging optimization problems.

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