Challenges of Big Data Visualization in Internet-of ... of Big Data... · decision making. It presents the essential challenges of data visualization and its relationship with big
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will happen in the emergency cases? That requires to
contact to doctor or communicate with the nearest hospital
or ambulance to save his life. May be the results are similar
but the meaning of them not the same so the difficulty of
understanding. Also the users requires to have the correct
numbers in every point with right interpretation of these
data.
Running the queries, processes and reports on these
data takes a long time that is also a challenge of the data
visualization. The classical tools for data visualization are
limited with the big data can’t interpret the huge data and
changes continuously. They worked to improve latency of
data but they are still faced performance problems. For any
Big Data visualization tool should to be able to deal with
semi-structured and unstructured data because big data
usually have variant format data types. The parallelism
approach is not an enough solution because it featured to
break down frequently.
The challenges [13, 29, 30] of big data visualization
are not focus on the industry of business only but also
targets the researchers to improve the results and tools. So
this paper shows open research problems of visualization to
support researchers in finding a new open research areas and
directions.
Figure.7: the data visualization challenges
These challenges faces data visualization in the
implementation process of any visualization tool:
Figure.8: the open research challenges of data visualization
A. Context-awareness Data Model
One significant challenge [30, 31] to data models in
existing visualization software is that the types of data they
are expected to handle is growing. In the scientific space,
new refinement structures, new types of polynomial fields,
and even high dimensional grids are becoming more
common. The need for these structures are motivated both
by the demands of new science and by the evolution of
scientific computing algorithms.
Internet-of-things environments and mobile sensors
environments are considered a hot area of research in data
visualization that requires to unite the structure of the
variety of context into one context-type. The researchers
[19, 20] proposed a system to generate interactive context-
aware visualization of federated data sources provided by an
underlying context-aware framework called Augmented
World Model (AWM). It relies on the extracted data from
internet-of-things devices and sensors in real-time.
The authors presented a new visualization tool entitled
[31] to visualize big data graphically and detect the outliers
of data such as error or event based on historical tutorial.
Recently, this research [32] talked about Rules in Mobile
Context-Aware Systems and provides a SKE to the
development of mobile context-aware systems. The problem
of this research in the output data extracted from Mobile
sensors in performance and support the correct meaning of
users, the adaptive with users, conditions, environments,
efficiency with high usage resources and responsiveness.
This research presented [33] a new paradigm of the
data visualization based on machine learning of context-
aware identification. It provided the recommendations of the
charts types for given datasets based on specific domain.
The authors [34] proposed a systems for data visualization
that could support the integration of common visualization
modules in big data streams. It was based on the parallelized
visualization that can be scalable and flexible for the
heterogeneous data.
The previous observation of several visualization tools
that can manage the context-aware types, we notice that: the
visualization tool requires to be:
1. Adaptive: dynamic system
2. Easy to use: to allow users to change, update and
search by various dimensions such as time,
geographic locations.
3. High performance (low running time)
4. Classify patterns: that is based on features,
conditions, and patterns recognition.
Other important notion there is a relationship between
the fusion level of data in IoT or integration process and the
reliable results of visualization tool.
, (1) or
(2)
The equations 1, 2 refer to the correct fusion or
integration produces the reliable visualization results. On
other words, the high fusion causes of the high reliability of
visualization. Another observation of the high detection of
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precentage rate 19% 22% 37% 45% 50% 57%
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Pre
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Challenges of Data Visualization
outliers can support the results of visualization accuracy as
equation 3.
, (3)
B. Transparency Visualization
It can Support the user in understanding the reasons
behind the recommendations. There is still challenge
concept because that there is lack of explanation of why are
the results showed. Recommender systems [18] lack
transparency, when they appear as "black boxes" to the user,
making it incomprehensible how recommendations are
generated and why a specific list of items is presented.
Improving transparency of data visualization is requires to
avoid risk management process in business. That also
provides any user to recognize the reasoning behind the
visualization results.
The proposed method used to enhance visualization
transparency (as known by "Justification") of a
recommender system and the users’ trust in the results, are
explanations. They can help users to understand the reason
behind a recommendation, increase the user’s sense of
involvement in the recommendation process and can lead to
a greater acceptance of the recommender system as a
decision aide. The concept affords transparency of the
recommendations by visualizing an average rating (position)
as well as an individual rating for each user (glyphs). Viola
[31] also provided reasoning and explanation of the outliers
in data visualization.
This is still open research in visualization, the recent
motivations targets using deep learning and data reduction
based on features for creating an explanation of the data
visually.
, (4)
(5)
Our observation shows a high relationship between the
correct data reduction has Positive relationship with correct
fusion that will effect on the reliable visualization as
equations 4, and 5.
C. Social Internet-of-things (SIoT)
The internet-of-things refers to interconnected set of
sensors connected via internet that hold huge data about one
or more objective. The social internet-of-things [ 35, 36]
refers to the various user’s targets and the affected
attributes. There are levels of meaning of any data that are
very sensitive with the user authorities and goals. The
Interactivity of visualization data is complex process
because it is based on the complex features and levels of
data. So that will require new motivations to can manage the
data levels with user’s variety. The observation here reach to
this relationships in equation 6 and 7.
, (6)
(7)
D. Virtual Reality (VR):
Virtual Reality is going to have a huge impact on the
potential for data visualizations [18], allowing people to
interact with data in the third dimension for the first time.
Imagine being able to pick a data set and move it around on
any axis to compare it to another, it isn’t too far away.
According to SAS. People can process only 1 kilobit of
information per second on a flat screen, which can be
increased significantly if it’s analyzed in a 3D VR world.
This challenge has a big effect on the future results, such as
profit of the finance data. The visualization tools can predict
the profit, and loss in several cases and graph the
imagination based on real data concurrently. That can
improve the performance rate yearly.
VI. CONCULSION AND FUTURE WORKS
This paper presents a survey study of data visualization
as a hot area of research. It illustrates the relationship
between the big data and data visualization. It demonstrates
the benefits of the visualization. This paper aims to explain
the four challenges in this field: Context-awareness,
transparency, social internet-of-things based on the different
levels of understanding and virtual reality.
Challenges are discussed in the following briefly,
A. Context-Awareness: Considering different
situations, such as mood, time, individual, or
collaborative scenarios
B. Transparency. Supporting the user in understanding
the reasons behind the recommendations.
C. Social Internet-of-Things and Meaningful Data
levels: The difficulty for designing visualizations to
match up to the wide-ranging understanding of data
and data visualizations.
D. Virtual Reality: is going to have a huge impact on
the potential for data visualizations, allowing
people to interact with data in the third dimension
for the first running time.
In future work, we will propose a new visualization tool
can treat the mentioned challenges to support social internet-
of-things. This tool should to be adaptive, flexible, and easy
to use. That can will improve the fusion and integration
problems to support the high accuracy and performance of
the context types. We target to optimize the time
automatically to work on the real-time streams analysis.
REFERENCES
[1] V.Bhuvaneswari, and R.Porkodi, The Internet of Things (IoT) Applications and Communication Enabling Technology Standards: An Overview, 2014 International Conference on Intelligent Computing Applications, 2014
[2] Giancarlo Fortino, and Paolo Trunfio, Internet of Things Based on Smart Objects, Technology, Middleware and Applications, 2014, Internet of things, springer,2014
[3] Jin X, Wah BW, Cheng X, and Wang Y, “Significance and challenges of big data research,” Big Data Research, 30;2(2):59-64, 2015.
[4] “Expanding Florida’s Use and Accessibility of Telehealth”, Telehealth Advisory Council, October 31, 2017.
[5] Wilfried Elmenreich, An Introduction to Sensor Fusion, 2002
[6] Ahmed Sammoud, Ashok Kumar, Magdy Bayoumi, Tarek Elarabi, Real-time streaming challenges in Internet of Video Things (IoVT),IEEE International Symposium on Circuits and Systems (ISCAS),2017
[7] Senthil Kumar Janahan, M.R.M. Veeramanickam, S. Arun, Kumar Narayanan, R. Anandan, Shaik Javed Parvez, IoT based smart traffic signal monitoring system using vehicles counts, International Journal of Engineering & Technology, volume 7, 2018
[8] Alexandros Labrinidis, and H. V. Jagadish, Challenges and Opportunities with Big Data,Proceedings of the VLDB Endowment, Vol. 5, No. 12, 2012
[9] Lidong Wang, Guanghui Wang, and Cheryl Ann Alexander, “Big Data and Visualization: Methods, Challenges and Technology Progress,” Digital Technologies, vol. 1, no. 1, pp. 33-38. doi:10.12691/dt-1- 1-7, 2015.
[11] Ekaterina OlshannikovaEmail author, Aleksandr Ometov, Yevgeni Koucheryavy and Thomas Olsson, Visualizing Big Data with augmented and virtual reality: challenges and research agenda, Journal of Big Data, 2015.
[12] Introduction to Data Visualization Techniques Using Microsoft Excel 2013 & Web-based Tools,Tufts Data Lab,2016
[13] White paper, Data Visualization Techniques From Basics to Big Data With SAS® Visual Analytics,2018
[14] David stodder, Data Visualizatio n AND DISCOVERY FOR BETTER BUSINESS DECISIONS, SAS, third quarter, Tdwi best pracitcis report, 2013
[15] Hodge, Victoria J. orcid.org/0000-0002-2469-0224 and Austin, James orcid.org/0000-0001-5762-8614 (2018) An Evaluation of Classification and Outlier Detection Algorithms. Working Paper
[16] Samuel Li, N.Marsaglia, christoph Garth, et al., Data Reduction Techniques for Scientific Visualization and Data Analysis,March 2018Computer Graphics Forum 37(2), 2018
[17] Samuel kaski, and Jaakko peltonen, Dimensionality Reduction for Data Visualization,
Published in: IEEE Signal Processing Magazine ( Volume: 28 , Issue: 2 , March 2011 )
[18] MANDY KECK, DIETRICH KAMMER , Exploring Visualization Challenges for Interactive Recommender Systems, VisBIA 2018
[19] Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama and Danilo P. Mandic, "Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives", Foundations and Trends® in Machine Learning: Vol. 9: No. 6, pp 431-673,2017.
[20] Interactive Context-Aware Visualization for Mobile Devices, International Symposium on Smart Graphics SG: Smart Graphics pp 167-178, 2009.
[21] Steffen Oeltze, Helmut Doleisch, Helwig Hauser, Gunther Weber., Interactive Visual Analysis of Scientific Data. Presentation at IEEE VisWeek 2012, Seattle (WA), USA
[22] Zoltan Konyha, Alan Lez, Kreimir Matkovic, Mario Jelovic, and Helwing Hauser, Interactive visual analysis of families of curves using data aggregation and derivation, i-KNOW '12 Proceedings of
the 12th International Conference on Knowledge Management and Knowledge Technologies,2012
[23] Jacqueline Strecker, Report :Data Visualization In Review ,2012
[24] Sebastian Raschka, MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack, the journal of open source software, 2018.
[25] Keita Fujino, Sozo Inoue, and Tomohire Shibata, Machine Learning of User Attentions in Sensor Data Visualization, International Conference on Mobile Computing, Applications, and Services, Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 240), 2018.
[26] Kanit Wongsuphasawat ; Daniel Smilkov ; James Wexler ; Jimbo Wilson ; Dandelion Mané, et al., Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow,Published in: IEEE Transactions on Visualization and Computer Graphics , Volume: 24 , Issue: 1 , 2018.
[27] Junyuan Xie, Ross Girshick , Ali Farhadi, Unsupervised Deep Embedding for Clustering Analysis, ICML'16 Proceedings of the 33rd International Conference on International Conference on Machine Learning – Volume48 ,2016.
[28] Jack G. Zheng, Data Visualization for Business Intelligence, In book: Global Business IntelligenceChapter: 6Publisher: Taylor & Francis, 2017
[29] White paper: Data Visualization: Making Big Data Approachable and Valuable, Market pluse, SOURCE: IDG RESEARCH SERVICES, SAS, Custom Solution Group, 2012.
[30] Mohsen Marjani, Fariza Nasaruddin , Abdullah Gan,Ahmad Karim, Ibrahim Abaker Targio Hashem* , Aisha Siddiqa,- Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges, IEEE Access, 5, pages 5247-5261.
[31] Nan Cao, Chaoguang Lin, Qiuhan Zhu, Yu-Ru Lin, Xian Teng, Xidao Wen, Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data, IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 24, NO. 1, JANUARY 2018
[32] Grzegorz J. Nalep, Rules in Mobile Context-Aware Systems, Modeling with Rules Using Semantic Knowledge Engineering pp 403-430
[33] W.A.D. Kanchana, G.D.L. Madushanka, et al., context-aware recommendation for data visualization, 2016
[34] Harald Sanftmann, Nazario Cipriani, and Daniel Weiskopf, Distributed Context-Aware Visualization,8th IEEE International Workshop on Middleware and System Support for Pervasive Computing, 2011
[35] Bo-Shen Chen ; Varsha A. Kshirsagar ; Shou-Chih Lo,Platform design for social Internet of Things,2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW),2017
[36] Moneeb Gohar, Muhammad Muzammal, Arif Ur Rahman ,SMART TSS: Defining transportation system behavior using big data analytics in smart cities, Sustainable Cities and Society Volume 41, Pages 114-119, 2018