"Smart Decision Making Needs Automated Analysis" Making sense out of big data in real-time

2014 ◽  
Author(s):  
Brian Crockett ◽  
Kshitiz Kurrey
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Automated Analysis ◽  
Making Sense

scholarly journals Data-driven decision support under concept drift in streamed big data

2019 ◽  
Vol 6 (1) ◽  
pp. 157-163 ◽  
Author(s):  
Jie Lu ◽  
Anjin Liu ◽  
Yiliao Song ◽  
Guangquan Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Concept Drift ◽  
High Volume ◽  
Streaming Data ◽  
Data Driven ◽  
Research Directions ◽  
Decision Outcomes ◽  
Past Data

Abstract Data-driven decision-making ($$\mathrm {D^3}$$D3M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$D3M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$D3M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$D3M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\mathrm {D^3}$$D3M in streamed big data environments.

scholarly journals Symposium review: Real-time continuous decision making using big data on dairy farms

2020 ◽  
Vol 103 (4) ◽  
pp. 3856-3866 ◽  
Author(s):  
Victor E. Cabrera ◽  
Jorge A. Barrientos-Blanco ◽  
Hector Delgado ◽  
Liliana Fadul-Pacheco
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Dairy Farms ◽  
Symposium Review

Real-Time Big Data Warehousing

2019 ◽  
pp. 28-57
Author(s):  
Francisca Vale Lima ◽  
Carlos Costa ◽  
Maribel Yasmina Santos
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Large Volume ◽  
Data Warehousing ◽  
Methodological Approach ◽  
Big Data Analytics ◽  
Data Flows ◽  
Main Components ◽  
Batch Data

The large volume of data that is constantly being generated leads to the need of extracting useful patterns, trends, or insights from this data, raising the interest in business intelligence and big data analytics. The volume, velocity, and variety of data highlight the need for concepts like real-time big data warehouses (RTBDWs). The lack of guidelines or methodological approaches for implementing these systems requires further research in this recent topic. This chapter presents the proposal of a RTBDW architecture that includes the main components and data flows needed to collect, process, store, and analyze the available data, integrating streaming with batch data and enabling real-time decision making. Using Twitter data, several technologies were evaluated to understand their performance. The obtained results were satisfactory and allowed the identification of a methodological approach that can be followed for the implementation of this type of system.

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