Real-time analytics for intelligent systems

By: Verma, ShikhaMaterial type: Computer fileComputer filePublication details: Ahmedabad Indian Institute of Management Ahmedabad 2022Description: 184p. Includes bibliographical referencesSubject(s): Big data | Intelligent Systems | Real-time analyticsDDC classification: TH 2022-12 Online resources: eThesis Summary: Data streams are defined as a sequence of observations arriving continuously at a fast pace. They pose unique computational challenges viz: Single-pass at incoming observations, huge storage requirements, and accounting for concept drift. Concept drift is a phenomenon where characteristics of data evolve over time. Concept drift renders the models built in conventional setup outdated for predictions on current data. Predictive machine learning methods are supposed to account for these challenges while processing data streams that have become ubiquitous due to the pervasive presence of sensors in the Internet of Things era. The prevalence of information and communication technologies for pervasive sensor data collection, a rapid decrease in data storage cost, and pervasive availability of computing power enables the analysis of “big data” for monitoring, planning, and operational purposes. The domain of ‘Intelligent Systems’ involves the use of advancements in communication and computation technologies to address challenges in data-driven systems. This leads to the production of high-velocity, information-rich data streams. These streams operate in dynamic environments and do not meet the requirements of a (time) stationary distribution which is often an important requirement for analysis of temporal data.
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Item type Current library Collection Call number Status Date due Barcode
Thesis (FPM) Vikram Sarabhai Library
Reference
Non-fiction TH 2022-12 (Browse shelf(Opens below)) Not for Issue (Restricted Access) CD002697

Thesis Advisory Committee
Professor Arnab Kumar Laha (Chairperson)
Professor Chetan Soman (Member)
Professor Sanjay Verma (Member)

Data streams are defined as a sequence of observations arriving continuously at a fast pace. They pose unique computational challenges viz: Single-pass at incoming observations, huge storage requirements, and accounting for concept drift. Concept drift is a phenomenon where characteristics of data evolve over time. Concept drift renders the models built in conventional setup outdated for predictions on current data. Predictive machine learning methods are supposed to account for these challenges while processing data streams that have become ubiquitous due to the pervasive presence of sensors in the Internet of Things era. The prevalence of information and communication technologies for pervasive sensor data collection, a rapid decrease in data storage cost, and pervasive availability of computing power enables the analysis of “big data” for monitoring, planning, and operational purposes. The domain of ‘Intelligent Systems’ involves the use of advancements in communication and computation technologies to address challenges in data-driven systems. This leads to the production of high-velocity, information-rich data streams. These streams operate in dynamic environments and do not meet the requirements of a (time) stationary distribution which is often an important requirement for analysis of temporal data.

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