12,346 research outputs found
Big Data Visualization Tools
Data visualization is the presentation of data in a pictorial or graphical
format, and a data visualization tool is the software that generates this
presentation. Data visualization provides users with intuitive means to
interactively explore and analyze data, enabling them to effectively identify
interesting patterns, infer correlations and causalities, and supports
sense-making activities.Comment: This article appears in Encyclopedia of Big Data Technologies,
Springer, 201
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS
Many distributed machine learning frameworks have recently been built to
speed up the large-scale data learning process. However, most distributed
machine learning used in these frameworks still uses an offline algorithm model
which cannot cope with the data stream problems. In fact, large-scale data are
mostly generated by the non-stationary data stream where its pattern evolves
over time. To address this problem, we propose a novel Evolving Large-scale
Data Stream Analytics framework based on a Scalable Parsimonious Network based
on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving
algorithm is distributed over the worker nodes in the cloud to learn
large-scale data stream. Scalable PANFIS framework incorporates the active
learning (AL) strategy and two model fusion methods. The AL accelerates the
distributed learning process to generate an initial evolving large-scale data
stream model (initial model), whereas the two model fusion methods aggregate an
initial model to generate the final model. The final model represents the
update of current large-scale data knowledge which can be used to infer future
data. Extensive experiments on this framework are validated by measuring the
accuracy and running time of four combinations of Scalable PANFIS and other
Spark-based built in algorithms. The results indicate that Scalable PANFIS with
AL improves the training time to be almost two times faster than Scalable
PANFIS without AL. The results also show both rule merging and the voting
mechanisms yield similar accuracy in general among Scalable PANFIS algorithms
and they are generally better than Spark-based algorithms. In terms of running
time, the Scalable PANFIS training time outperforms all Spark-based algorithms
when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure
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Visual analysis of social networks in space and time using smartphone logs
We designed and applied novel interactive visualisation to investigate how social networks - derived from smartphone logs - are embedded in time and space. Social networks were identified through direct calls between participants and calls to mutual contacts of participants. Direct contact between participants was sparse and deriving networks through mutual contacts helped enrich the social networks. Our resulting interactive visualisation tool offers four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data. Brushing and altering techniques help us investigate how these aspects relate. We also simultaneously display some demographic and attitudinal variables to help add context to the behaviours we observe. Using these techniques, we were able to characterise spatial and temporal aspects of participants' social networks and suggest explanations for some of them. We reflect on the extent to which such analysis helps us understand social communication behaviour
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
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