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From big data to insights

Orit Shaer, Ali Mazalek, Brygg Ullmer, Miriam Konkel
2013 Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction - TEI '13  
We propose that tangible, embedded, and embodied interaction (TEI) offers unique opportunities for enhancing discovery and learning in genomics.  ...  Also, designing for problems in genomics can help move forward the theory and practice of TEI.  ...  ACKNOWLEDGMENTS We thank Consuelo Valdes (Wellesley College) and Andy Wu (Georgia Tech). This work has been partially funded by NSF IIS-1017693, DRL-097394084, and CNS-1126739.  ... 
doi:10.1145/2460625.2460642 dblp:conf/tei/ShaerMUK13 fatcat:2uel44oqcja6vmugz4syljdtmm

Big Data and IT Network Data Visualization

Lidong Wang
2018 International journal of mathematical, engineering and management sciences  
The challenges of visualization and Big Data analytics in IT network visualization are also discussed.  ...  Applications and technology progress of visualization in IT network analysis and big data in IT network visualization are presented.  ...  The VSOutlier system was developed to support interactive exploration of outliers in big stream data.  ... 
doi:10.33889/ijmems.2018.3.1-002 fatcat:vghrgd2535gfxejhg3qp5yrjq4

The role of data visualization in railway Big Data Risk Analysis [chapter]

M Figueres-Esteban, P Hughes, C Van Gulijk
2015 Safety and Reliability of Complex Engineered Systems  
Big Data techniques allow a great quantity of information to be handled from different types of sources (e.g. unstructured text, signaling and train data).  ...  Big Data Risk Analysis (BDRA) is one of the possible alleys for the further development of risk models in the railway transport.  ...  Scientific visualisation would develop visual techniques to depict scientific and spatial data, whilst information visualisation represents abstract and non-spatial data (Tory & Möller 2004) .  ... 
doi:10.1201/b19094-377 fatcat:fchbkt2p6nbrzpgvvlf3uk3yta

Philosophy of Big Data: Expanding the Human-Data Relation with Big Data Science Services

Melanie Swan
2015 2015 IEEE First International Conference on Big Data Computing Service and Applications  
Keywords-big data; philosophy; information; scientific method; research; methodology; human-data relations "The Philosophy of Information is a completely new development with a capacity to revolutionize  ...  philosophy and human interactions with science, technology, data, and reality" -Luciano Floridi, Philosophy of Information, Oxford [1].  ...  As part of the continual questioning process in our relation with data, Ruppert reminds that any mode of interacting with big data is representation and not necessarily reality [40] .  ... 
doi:10.1109/bigdataservice.2015.29 dblp:conf/bigdataservice/Swan15 fatcat:zgxisq5pmzexzlq6qrhoupy45e

The Intersection of Big Data and the Data Life Cycle: Impact on Data Management

Janet L. Kourik
2017 International Journal of Knowledge Engineering  
The bandwidth of communications places constraints on the transmission of large volumes of data. New techniques are needed to efficiently transfer and receive vast volumes of data.  ...  Fig. 2 . 2 Big data value discovery process. Fig. 3 . 3 Big data life cycle. Fig. 4 . 4 Interaction of big data life cycle and dimensions.  ... 
doi:10.18178/ijke.2017.3.2.083 fatcat:exjhyatl6bbihibektsii2dpmy

Big data exploration requires collaboration between visualization and data infrastructures

Danyel Fisher
2016 Proceedings of the Workshop on Human-In-the-Loop Data Analytics - HILDA '16  
Using these techniques involves a number of difficult design tradeoffs for both the ways that data can be represented, and the ways that users can interact with the visualizations.  ...  It identifies techniques for handling large scale data, grouped into "look at less of it," and "look at it faster."  ...  New techniques are emerging that can inform big data exploration.  ... 
doi:10.1145/2939502.2939518 dblp:conf/sigmod/Fisher16 fatcat:t334jjwepvhcldtldkyotgglu4

DIVE: a data intensive visualization engine

Dennis Bromley, Steven J. Rysavy, Robert Su, Rudesh D. Toofanny, Tom Schmidlin, Valerie Daggett
2013 Computer applications in the biosciences : CABIOS  
DIVE is a software framework intended to facilitate big data analysis and reduce the time to scientific insight.  ...  Modern scientific investigation is generating increasingly larger datasets, yet analyzing these data with current tools is challenging.  ...  As research becomes more data-driven and reliant on data mining and visualization, big data visual analytics solutions should provide a new perspective for scientific investigation.  ... 
doi:10.1093/bioinformatics/btt721 pmid:24336804 pmcid:PMC3928528 fatcat:akamzgkqrfb53hcyopi3pudahy

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure [article]

Hai Zhuge
2015 arXiv   pre-print
What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing?  ...  However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data?  ...  The author thanks the Chinese Academy of Sciences and Aston University for the cooperative research environment and time provided for this work. The author thanks Dr.  ... 
arXiv:1507.06500v1 fatcat:qpxjdezd45byrci5hbhtmg5pja

Big Data Analytics for Data Visualization: Review of Techniques

Geetika Chawla, Savita Bamal, Rekha Khatana
2018 International Journal of Computer Applications  
Researchers have explored a new way to visualize and analyze complex and dynamic datasets using virtual reality.  ...  We have also investigated how virtual reality has radically changed the world of Big Data Visualization.  ...  To get deep insight and 360 degree view of big data researchers are now focusing on using Virtual reality as a new technique for big data visualization.  ... 
doi:10.5120/ijca2018917977 fatcat:adlcone3zzhuxnhdpzvnrefpl4

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

James H. Faghmous, Vipin Kumar
2014 Big Data  
scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data.  ...  More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses  ...  J.H.F. was also funded by an NSF Graduate Research Fellowship and a University of Minnesota Doctoral Dissertation Fellowship.  ... 
doi:10.1089/big.2014.0026 pmid:25276499 pmcid:PMC4174912 fatcat:jxldvpyj7bd4fnhyx5bsxiz3qq

Scientific Data Analysis Using Data-Intensive Scalable Computing: The SciDISC Project

Patrick Valduriez, Marta Mattoso, Reza Akbarinia, Heraldo Borges, Jose J. Camata, Alvaro L. G. A. Coutinho, Daniel Gaspar, Noel Moreno Lemus, Ji Liu, Hermano Lustosa, Florent Masseglia, Fabrício Nogueira da Silva (+8 others)
2018 Very Large Data Bases Conference  
In this context, the SciDISC project addresses the grand challenge of scientific data analysis using DISC, by developing architectures and methods to combine simulation and data analysis.  ...  Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data-intensive scalable computing (DISC), as exemplified by frameworks such as Hadoop  ...  Acknowledgements This work was partially funded by CNPq, FAPERJ and Inria (SciDISC project), EU H2020 Programme and MCTI/RNP-Brazil (HPC4E grant no. 689772), and performed (for Inria) in the context of  ... 
dblp:conf/vldb/ValduriezMABCCG18 fatcat:fk2hp6vhxjebtpgpi7y6p3b24e

Big Data: Understanding Big Data [article]

Kevin Taylor-Sakyi
2016 arXiv   pre-print
However Big Data Analytics has a few concerns including Management of Data-lifecycle, Privacy & Security, and Data Representation.  ...  effectiveness of Big Data Analytics and presents how it could be of greater good in the future if handled appropriately.  ...  Management of data life cycle, data representation, and data privacy & security are among the pressing issues within big data analytics. A.  ... 
arXiv:1601.04602v1 fatcat:t6vubm6v6je6tdhlhjsknbxal4

A Review on the Development of Big Data Analytics and Effective Data Visualization Techniques in the Context of Massive and Multidimensional Data

J. Jabanjalin Hilda, C. Srimathi, Bhulakshmi Bonthu
2016 Indian Journal of Science and Technology  
The taxonomy detailed here show that the local and global structure of the data can be visualized in an interactive manner and has a massive advantage.  ...  For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, Information retrieval and intelligence analysis.  ...  The first step is often to pre-process and transform the data to derive different representations for further exploration.  ... 
doi:10.17485/ijst/2016/v9i27/88692 fatcat:37fdoyhz7fh67fb6jtsol2e4gi

Impact of big data on computer graphics

Pramila Joshi
2017 International Journal of Advanced Technology and Engineering Exploration  
architecture. 4.Sources of big data Sources of big data are mostly based on Computer graphics applications.  ...  The disciplines which have the greatest impact of computer graphics in the form of special effects can be classified as advertising and entertainment industry, visual representation techniques and industrial  ...  Scientific visualization means to be able to extract meaningful information from the huge ocean of data sets. One important aspect about visualizing big data is absence of interactivity.  ... 
doi:10.19101/ijatee.2017.432002 fatcat:uebzfepc5ves3hwwqt5plxtebi

Data science

Longbing Cao
2017 Communications of the ACM  
data, or broadly data analytics, and only a limited number of new data-driven challenges and directions have been explored.  ...  While data science has emerged as a contentious new scientific field, enormous debates and discussions have been made on it why we need data science and what makes it as a science.  ...  For this, new methodologies and techniques need to be developed.  ... 
doi:10.1145/3015456 fatcat:67rgituyxzehfc5yvcmukhvbqa
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