scholarly journals Bridging the gap between decision-making and emerging big data sources: An application of a model-based framework to disaster management in Brazil

2017 ◽  
Vol 97 ◽  
pp. 12-22 ◽  
Author(s):  
Flávio E.A. Horita ◽  
João Porto de Albuquerque ◽  
Victor Marchezini ◽  
Eduardo M. Mendiondo
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Disaster Management ◽  
Data Sources ◽  
Model Based

scholarly journals Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot

2019 ◽  
Vol 8 (8) ◽  
pp. 330 ◽  
Author(s):  
Robert Jeansoulin
Keyword(s):  
Big Data ◽  
Land Cover ◽  
Disaster Management ◽  
Information Fusion ◽  
Data Sources ◽  
Current Status ◽  
Special Issue ◽  
Scientific Domain ◽  
Fusion Methods ◽  
Optical Radar

Since the launch of Landsat-1 in 1972, the scientific domain of geo-information has been incrementally shaped through different periods, due to technology evolutions: in devices (satellites, UAV, IoT), in sensors (optical, radar, LiDAR), in software (GIS, WebGIS, 3D), and in communication (Big Data). Land Cover and Disaster Management remain the main big issues where these technologies are highly required. Data fusion methods and tools have been adapted progressively to new data sources, which are augmenting in volume, variety, and in quick accessibility. This Special Issue gives a snapshot of the current status of that adaptation, as well as looking at what challenges are coming soon.

An REA Ontology-Based Model for Mapping Big Data to Accounting Information Systems Elements

2017 ◽  
Vol 31 (3) ◽  
pp. 45-61 ◽  
Author(s):  
Uday S. Murthy ◽  
Guido L. Geerts
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Systems ◽  
Accounting Information ◽  
Data Sources ◽  
Complex Data ◽  
Accounting Information Systems ◽  
Business Transaction ◽  
Business Transactions ◽  
Data Elements

ABSTRACT The term “Big Data” refers to massive volumes of data that grow at an increasing rate and encompass complex data types such as audio and video. While the applications of Big Data and analytic techniques for business purposes have received considerable attention, it is less clear how external sources of Big Data relate to the transaction processing-oriented world of accounting information systems. This paper uses the Resource-Event-Agent Enterprise Ontology (REA) (McCarthy 1982; International Standards Organization [ISO] 2007) to model the implications of external Big Data sources on business transactions. The five-phase REA-based specification of a business transaction as defined in ISO (2007) is used to formally define associations between specific Big Data elements and business transactions. Using Big Data technologies such as Apache Hadoop and MapReduce, a number of information extraction patterns are specified for extracting business transaction-related information from Big Data. We also present a number of analytics patterns to demonstrate how decision making in accounting can benefit from integrating specific external Big Data sources and conventional transactional data. The model and techniques presented in this paper can be used by organizations to formalize the associations between external Big Data elements in their environment and their accounting information artifacts, to build architectures that extract information from external Big Data sources for use in an accounting context, and to leverage the power of analytics for more effective decision making.

Application of Dempster–Shafer Data Fusion Technique in Support of Decision Making with Big Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Ping Yi ◽  
Songling Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Fusion ◽  
Incomplete Information ◽  
Data Sources ◽  
Fusion Technique ◽  
Crowd Management ◽  
Multiple Data ◽  
Data Source ◽  
Single Data

This paper introduces applications of the Dempster–Shafer (D-S) data fusion technique in transportation system decision making. D-S inference is a statistics-based data classification technique, and it can be used when data sources contribute discontinuous and incomplete information and no single data source can produce an overwhelmingly high probability of certainty for identifying the most probable event. The technique captures and combines the information contributed by the data sources by using Dempster’s rule to find the conjunction of the events and to determine the highest associated probability. The D-S theory is explained and its implementation described through numerical examples of a ride-hauling service and of crowd management at a subway station. Results from the applications have shown that the technique is very effective in dealing with incomplete information and multiple data sources in the era of big data.

scholarly journals Sentiment Trend Analysis of Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 59-64
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Sources ◽  
Product Reviews

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

scholarly journals Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making

2021 ◽  
Vol 8 ◽  
Author(s):  
Fernanda C. Dórea ◽  
Crawford W. Revie
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Integration ◽  
Data Access ◽  
Data Sources ◽  
Data Driven ◽  
Multiple Data ◽  
Combining Evidence ◽  
Complex Variety ◽  
Support Decision Making

The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

Learning Analytics in Higher Education

2021 ◽  
pp. 398-431
Author(s):  
Rushil Raghavjee ◽  
Prabhakar Rontala Subramaniam ◽  
Irene Govender
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Big Data ◽  
Learning Analytics ◽  
Data Sources ◽  
Data Visualisation ◽  
Research Challenges ◽  
Academic Decision Making

It is known that big data has penetrated several if not all spheres of life. In higher education, the ability to take these large amounts of data and process it into something meaningful for academic decision making is commonly referred to as learning analytics. This chapter provides an overview of learning analytics and its importance, as well as identifying academic data sources, techniques used for learning analytics and prediction, and data visualisation techniques used to present analysis for better understanding and eventual decision making. It also includes a discussion of learning analytics frameworks for research and some identified research challenges.

Sign in / Sign up

Export Citation Format

Share Document