The presentation discusses interactive database systems and their role in data mining and analytical processing, emphasizing the benefits of employing online analytical processing (OLAP) and data mining techniques to extract knowledge from large datasets. It details how automated processes can enhance decision-making across various industries by providing users with insights through data services and modeling. The conclusion highlights the necessity of accurate data and its formats for improving decision-making in business and other professions.

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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