Journal ArticleDOI
Big data
Ibrar Yaqoob,Ibrahim Abaker Targio Hashem,Abdullah Gani,Salimah Binti Mokhtar,Ejaz Ahmed,Nor Badrul Anuar,Athanasios V. Vasilakos +6 more
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.read more
Citations
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Journal ArticleDOI
Beyond the hype
Amir H. Gandomi,Murtaza Haider +1 more
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
The Challenges of Data Quality and Data Quality Assessment in the Big Data Era
Li Cai,Yangyong Zhu +1 more
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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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.
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Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
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.
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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.
Journal ArticleDOI
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.