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Journal ArticleDOI

Big data

TLDR
This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing based on structuralism and functionalism paradigms and strengths and weaknesses of these technologies are analyzed.
About
This article is published in International Journal of Information Management.The article was published on 2016-12-01. It has received 964 citations till now. The article focuses on the topics: Big data & Analytics.

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Citations
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Beyond the hype

TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
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Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err

TL;DR: It is shown that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster, and this phenomenon, which is called algorithm aversion, is costly, and it is important to understand its causes.
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Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

TL;DR: The challenges associated with the use and impact of revitalised AI based systems for decision making are identified and a set of research propositions for information systems (IS) researchers are offered.
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The Challenges of Data Quality and Data Quality Assessment in the Big Data Era

TL;DR: The data characteristics of the big data environment are analyzed, quality challenges faced by big data are presented, and a hierarchical data quality framework is formulates from the perspective of data users.
Journal ArticleDOI

Big Data and cloud computing: innovation opportunities and challenges

TL;DR: This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.
References
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Journal ArticleDOI

Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Book

Pattern Recognition with Fuzzy Objective Function Algorithms

TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI

Nonlinear dimensionality reduction by locally linear embedding.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI

A fast learning algorithm for deep belief nets

TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
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jModelTest 2: more models, new heuristics and parallel computing.

TL;DR: jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada.
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