This document discusses visual data mining and analytical processing of large amounts of data. It explores how interactive database systems integrate various forms of data to make them accessible. Data mining involves applying algorithms to detect patterns and extract knowledge from data. Online analytical processing (OLAP) performs multidimensional analysis of data to provide calculations, trend analysis, and modeling. Visualizing eye movement data can provide insights into cognitive processes. Data mining benefits many professions by facilitating decision-making through analysis of large datasets.

1. VISUAL DATA MINING
Deborah Adams, Northcentral University

2. INTRODUCTION
The presentation will review the various aspects of interactive database systems,
which integrates various forms of data making them readily available for access,
communication, etc. Additionally, it will explore how large amounts of data are
subject to the analytic process, while explaining how online analytical processing
performs data analysis on a multidimensional level providing complex calculations,
trend analysis, and sophisticated data modeling. Viewers of this presentation should
receive an understanding of data mining and analytical processing of data that
benefits professionals.
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3. INTERACTIVE DB SYSTEMS
Data
Services
Architecture
Service-
Enabling
Data Stores
Through ‘service-enabling’
a data store enables access
to web clients/applications
inaccessible external
data by publishing data
services (i.e. Microsoft WCF
Data Services and Oracles’
ODSI).
Data services are
employed on top of
data stores exposing
data/meta data to
consumers through an
external model
displaying a map
between the
store/external model
of the data store.
Carey et al. , 2012
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4. INTERACTIVE SYSTEMS
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www.vmvizura.si
www.3dmediawn.comwww.interactive.com.hk

5. PROCESS OF DATA MINING
Data Mining Defined
Data mining - automated application of
processes detecting patterns while
extracting knowledge from data. This
algorithm counts patterns, fits models
from/ to data. It is a step in the concept
of knowledge discovery in databases
(KDD) allowing for data sets to be
analyzed, searching patterns and
discovering rules. Data mining which is
automated makes it easier to apply the
scheme of decision support systems.
Discovery of knowledge from data is
found in techniques like associations,
classification, clustering and trend
analysis.
Attribute Focusing
The end-user is targeted through the use
of algorithms which leads the user
through data analysis. This method was
known in a earlier application of software
process engineering. It has been
applied/discovered interesting patterns
in the NBA.
Data Mining Applications
Take a large amount of computing power
which equates to multiple hours of valuable
time to mine large databases/construct
complex models. The reduction of wait time,
increase productivity and increases
understanding of knowledge discovery
process noted through scalable parallel
systems. Using numerous processors enables
more memory and a larger database to be
utilized and handled in the main memory
attached to the processors.
Goil et al., 1997
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6. OLAP PROCESS
On-line Analytical Processing—OLAP—
systems enable insight to be gained into the
performance of an enterprise by multiple
views of organized data that reflects the
multidimensional nature of enterprise data.
OLAP provides fast, consistent, interactive
access to multiple views of information. It
answers what if, why, who, and what
questions that create decision support
systems and help to extract knowledge from
data. According to multiple dimensions, OLAP
summarizes, consolidates, views, applies,
formulates to, and synthesizes.
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Goil et al., 1997
www.ctg.Albany.edu

7. OLAP PROCESS
The total of all possible dimension
combinations is what the data cube
computes, which is useful for answering
OLAP queries that uses an aggregate
combinations of various attributes.
An important function of OLAP queries
is aggregations, which data cube
operators may be helped by.
OLAP systems are required to provide
efficient analytical query processing in
high performance computing.
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Goil et al., 1997
www.searchbusinessanalytics.techtarget.com

8. VISUAL DATA/IMAGE DB
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www.devart.com
www.aquafold.com
 Gaze data/eye movements are
complex, eye movements are
created moment to moment
through interacting processes—
cognitive, perceptual, and motor.
 Eye movements are revealing and
can be used to study the
dynamics of cognitive systems.
‘Gaze’ actively gathers world
information, while binding
objects in the physical realm to
internal cognitive programs in a
moment by moment fashion.
It is critical to decipher momentary eye
movement data for the understanding
of how external sensory data
with internal cognitive processes.
Yu et al., 2012

9. DECISIONS MADE FROM DATA
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 Data combined with other data
provides improved results to
the public.
 We are able to get and use
data for better results for
companies and individuals.
 Products from data brokers prevent
fraud, improve product offerings &
deliver tailored consumer
advertisements.
 Brokers foster competition enabling
small businesses to pitch innovative
products to unreachable
consumers.
Information about individuals is complied by brokers from
online and offline sources—email, personal websites,
social media posts, U.S. Census records, retailers, DMV
records, and real estate records—using progressive
analytic tools, for selling to other brokers and businesses.
Anthes, 2015

10. BENEFITS OF DATA MINING
Mountains of data
 There are numerous types of data
collected across industries, states, and
governments.
 Data-informed decision-making over
the last decade has become a
movement due to so much data that
is available.
 Google the term ‘decision making’ or
‘data’ there are more than 50 million
entries.
Formats
 Accounts need to be either in the
same or complementary format for
translating into a common format.
 Most times different suppliers of data
use different formats.
 Data must be cleaned—edited, tested
for correctness and consistency.
Data 4 knowledge
 Translating knowledge from data
requires assessment, interpretation,
and access to sources of data and the
continuous accumulation of data.
 It is essential to use data for decision-
making. Data mining is the search
method for said process.
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Jianping et al., 2010; Schoors, 2000

11. CONCLUSION
Numerous, mostly all, businesses/professions benefit from data mining (i.e. medical,
education, legal, banking, politics, etc.). Having access to data in a visual format as well as
textual assists in the analytical process. Data contained in data-mines must be edited and
tested to ensure accuracy and consistency. The data accessed is typically utilized for decisions
and knowledge purposes like projecting, training, forecasting, budgeting, planning, etc. This
presentation provides information and examples of data mining its processes and how it is
used/benefits individuals/businesses.
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12. REFERENCES
Anthes, G. (2015). Data Brokers Are Watching You. Communications of The ACM, 58(1), 28-30. doi:10.1145/2686740
Carey, M.J., Onose, N., Petropoulos, M. (2012). Data Services. Communications of the ACM, 55(6), 86-97.
doi:10.1145/2184319.2184340
Goil, S., & Choudhary, A. (1997). High performance OLAP and data mining on parallel computers. Data Mining and
Knowledge Discovery, 1(4), 391-417. doi:http://dx.doi.org/10.1023/A:1009777418785
Jianping, S., Cooley, V. E., Reeves, P., Burt, W. L., Ryan, L., Rainey, J. M., & Wenhui, Y. (2010). Using data for decision-
making: perspectives from 16 principals in Michigan, USA. International Review of Education / Internationale Zeitschrift
Für Erziehungswissenschaft, 56(4), 435-456. doi:10.1007/s11159-010-9172-x
Schoors, K. (2000). A note on building a database on russian banks: Fieldwork against the odds. Post - Communist
Economies,12(2), 241-249. Retrieved from
http://search.proquest.com.proxy1.ncu.edu/docview/222605083?accountid=28180
Yu, C., Yurovsky, D., & Xu, T. (. (2012). Visual data mining: An exploratory approach to analyzing temporal patterns of eye
movements. Infancy, 17(1), 33-60. doi:10.1111/j.1532-7078.2011.00095.x
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