U.S. patent application number 14/753196 was filed with the patent office on 2016-12-29 for determining user preferences for data visualizations.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to T. Alan Keahey, Daniel J. Rope, Graham J. Wills.
Application Number | 20160379084 14/753196 |
Document ID | / |
Family ID | 57601190 |
Filed Date | 2016-12-29 |
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United States Patent
Application |
20160379084 |
Kind Code |
A1 |
Keahey; T. Alan ; et
al. |
December 29, 2016 |
DETERMINING USER PREFERENCES FOR DATA VISUALIZATIONS
Abstract
A method for determining user preferences for data
visualizations is provided. The method may include receiving data
visualizations. The method may also include collecting the shapes,
the line segments, and the colors associated with the data
visualizations. The method may further include converting the
shapes and the line segments to polygonal outlines. Additionally,
the method may include categorizing and measuring the line
segments. The method may further include identifying and
categorizing the angles formed by the line segments and determining
weighted values for the angles. The method may further include
calculating the total length for the line segments, and the total
weighted value for the angles. The method may also include
characterizing the line segments based on the categorization of the
line segments and the angles based on the categorization of the
angles. The method may further include scoring the at least one
data visualization based on the characterizations.
Inventors: |
Keahey; T. Alan;
(Naperville, IL) ; Rope; Daniel J.; (Reston,
VA) ; Wills; Graham J.; (Naperville, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
57601190 |
Appl. No.: |
14/753196 |
Filed: |
June 29, 2015 |
Current U.S.
Class: |
382/165 |
Current CPC
Class: |
G06F 16/24578 20190101;
G06F 16/00 20190101; G06F 16/248 20190101; G06F 16/44 20190101;
G06F 16/9038 20190101; G06K 9/46 20130101; G06F 16/338
20190101 |
International
Class: |
G06K 9/52 20060101
G06K009/52; G06K 9/46 20060101 G06K009/46; G06K 9/62 20060101
G06K009/62 |
Claims
1. A method for determining user preferences for at least one data
visualization, the method comprising: receiving the at least one
data visualization; collecting a plurality of shapes, a plurality
of line segments, and a plurality of colors associated with the at
least one data visualization; converting the plurality of collected
shapes and the plurality of collected line segments to a plurality
of polygonal outlines; categorizing the converted plurality of
collected line segments; measuring a plurality of lengths
associated with the converted plurality of collected line segments;
identifying and categorizing a plurality of angles formed by the
converted plurality of collected line segments; determining a
plurality of weighted values associated with the plurality of
angles; calculating a total length for the converted plurality of
collected line segments, and calculating a total weighted value for
the plurality of angles; characterizing the converted plurality of
collected line segments based on the categorization of the
converted plurality of collected line segments and the plurality of
angles based on the categorization of the plurality of angles; and
scoring the at least one data visualization based on the
characterization of the converted plurality of collected line
segments and the plurality of angles; and determining the user
preferences for the at least one data visualization by presenting
an impact characterization to users based on the scored at least
one data visualization, wherein the impact characterization is a
determination of an impact of the at least one data visualization
on a plurality of users.
2. The method of claim 1, wherein the at least one data
visualization is in a format selected from a group comprising at
least one of a portable document format and a vector graphic.
3. The method of claim 1, wherein the categorizing the converted
plurality of collected line segments is based on a selection from a
group comprising at least one of an angled line segment, a
horizontal line segment, and a vertical line segment.
4. The method of claim 1, wherein the categorizing the plurality of
angles is based on a selection from a group comprising at least one
of a sharp angle, a right angle, and a gentle angle.
5. The method of claim 1, wherein the measuring the plurality of
lengths associated with the converted plurality of collected line
segments and the determining the plurality of weighted values
associated with the plurality of angles are based on a plurality of
pixels.
6. The method of claim 1, wherein the scoring the at least one data
visualization is based on a ratio of the plurality of lengths
associated with the converted plurality of collected line segments
to the total length for the converted plurality of collected line
segments, and a ratio of the plurality of weighted values
associated with the plurality of angles to the total weighted value
for the plurality of angles.
7. The method of claim 1, further comprising: calculating a total
area of the plurality of polygonal outlines; categorizing the
plurality of collected colors; determining a plurality weighted
values associated with the collected colors; and calculating a
ratio of the plurality of weighted values associated with the
plurality of collected colors to the total area of the polygonal
outlines.
8. A computer system for determining user preferences for at least
one data visualization, comprising: one or more processors, one or
more computer-readable memories, one or more computer-readable
tangible storage devices, and program instructions stored on at
least one of the one or more storage devices for execution by at
least one of the one or more processors via at least one of the one
or more memories, wherein the computer system is capable of
performing a method comprising: receiving the at least one data
visualization; collecting a plurality of shapes, a plurality of
line segments, and a plurality of colors associated with the at
least one data visualization; converting the plurality of collected
shapes and the plurality of collected line segments to a plurality
of polygonal outlines; categorizing the converted plurality of
collected line segments; measuring a plurality of lengths
associated with the converted plurality of collected line segments;
identifying and categorizing a plurality of angles formed by the
converted plurality of collected line segments; determining a
plurality of weighted values associated with the plurality of
angles; calculating a total length for the converted plurality of
collected line segments, and calculating a total weighted value for
the plurality of angles; characterizing the converted plurality of
collected line segments based on the categorization of the
converted plurality of collected line segments and the plurality of
angles based on the categorization of the plurality of angles;
scoring the at least one data visualization based on the
characterization of the converted plurality of collected line
segments and the plurality of angles; and determining the user
preferences for the at least one data visualization by presenting
an impact characterization to users based on the scored at least
one data visualization, wherein the impact characterization is a
determination of an impact of the at least one data visualization
on a plurality of users.
9. The computer system of claim 8, wherein the at least one data
visualization is in a format selected from a group comprising at
least one of a portable document format and a vector graphic.
10. The computer system of claim 8, wherein the categorizing the
converted plurality of collected line segments is based on a
selection from a group comprising at least one of an angled line
segment, a horizontal line segment, and a vertical line
segment.
11. The computer system of claim 8, wherein the categorizing the
plurality of angles is based on a selection from a group comprising
at least one of a sharp angle, a right angle, and a gentle
angle.
12. The computer system of claim 8, wherein the measuring the
plurality of lengths associated with the converted plurality of
collected line segments and the determining the plurality of
weighted values associated with the plurality of angles are based
on a plurality of pixels.
13. The computer system of claim 8, wherein the scoring the at
least one data visualization is based on a ratio of the plurality
of lengths associated with the converted plurality of collected
line segments to the total length for the converted plurality of
collected line segments, and a ratio of the plurality of weighted
values associated with the plurality of angles to the total
weighted value for the plurality of angles.
14. The computer system of claim 8, further comprising: calculating
a total area of the plurality of polygonal outlines; categorizing
the plurality of collected colors; determining a plurality weighted
values associated with the collected colors; and calculating a
ratio of the plurality of weighted values associated with the
plurality of collected colors to the total area of the polygonal
outlines.
15. A computer program product for determining user preferences for
at least one data visualization, comprising: one or more
computer-readable storage devices and program instructions stored
on at least one of the one or more tangible storage devices, the
program instructions executable by a processor, the program
instructions comprising: program instructions to receive the at
least one data visualization; program instructions to collect a
plurality of shapes, a plurality of line segments, and a plurality
of colors associated with the at least one data visualization;
program instructions to convert the plurality of collected shapes
and the plurality of collected line segments to a plurality of
polygonal outlines; program instructions to categorize the
converted plurality of collected line segments; program
instructions to measure a plurality of lengths associated with the
converted plurality of collected line segments; program
instructions to identify and categorize a plurality of angles
formed by the converted plurality of collected line segments;
program instructions to determine a plurality of weighted values
associated with the plurality of angles; program instructions to
calculate a total length for the converted plurality of collected
line segments, and calculate a total weighted value for the
plurality of angles; program instructions to characterize the
converted plurality of collected line segments based on the
categorization of the converted plurality of collected line
segments and the plurality of angles based on the categorization of
the plurality of angles; program instructions to score the at least
one data visualization based on the characterization of the
converted plurality of collected line segments and the plurality of
angles plurality of angles; and program instructions to determine
the user preferences for the at least one data visualization by
presenting an impact characterization to users based on the scored
at least one data visualization, wherein the impact
characterization is a determination of an impact of the at least
one data visualization on a plurality of users.
16. The computer program product of claim 15, wherein the program
instructions to categorize the converted plurality of collected
line segments is based on a selection from a group comprising at
least one of an angled line segment, a horizontal line segment, and
a vertical line segment.
17. The computer program product of claim 15, wherein the program
instructions to categorize the plurality of angles is based on a
selection from a group comprising at least one of a sharp angle, a
right angle, and a gentle angle.
18. The computer program product of claim 15, wherein the program
instructions to measure the plurality of lengths associated with
the converted plurality of collected line segments and the program
instructions to determine the plurality of weighted values
associated with the plurality of angles are based on a plurality of
pixels.
19. The computer program product of claim 15, wherein the file path
information associated with the computer file comprises a plurality
of synchronization status indicators.
20. The computer program product of claim 15, further comprising:
program instructions to calculate a total area of the plurality of
polygonal outlines; program instructions to categorize the
plurality of collected colors; program instructions to determine a
plurality weighted values associated with the collected colors; and
program instructions to calculate a ratio of the plurality of
weighted values associated with the plurality of collected colors
to the total area of the polygonal outlines.
Description
BACKGROUND
[0001] The present invention relates generally to the field of
computing, and more specifically, to data visualizations.
[0002] Data visualizations are generally used as communication
tools to help users understand information. Specifically, effective
data visualizations help users in analyzing data and evidence, and
make complex data more accessible and usable. The typically used
data visualizations include information graphs, statistical graphs,
charts, tables, and plots. For example, users may need to visually
represent a particular analytical task, such as a comparison of
data and/or understanding the causality of data, and the data
visualization selected may be based on that particular task.
Furthermore, designers of data visualizations may not only want to
communicate information clearly, but may also want to stimulate
viewer engagement and attention. Thus, users often want data
visualizations to be both aesthetically and functionally
satisfactory. Ordinarily, these design choices are based on user
preferences.
SUMMARY
[0003] A method for determining user preferences for at least one
data visualizations is provided. The method may include receiving
the at least one data visualization. The method may also include
collecting a plurality of shapes, a plurality of line segments, and
a plurality of colors associated with the at least one data
visualization. The method may further include converting the
plurality of collected shapes and the plurality of collected line
segments to a plurality of polygonal outlines. Additionally, the
method may include categorizing the converted plurality of
collected line segments. The method may also include measuring a
plurality of lengths associated with the converted plurality of
collected line segments. The method may further include identifying
and categorizing a plurality of angles formed by the converted
plurality of collected line segments. The method may also include
determining a plurality of weighted values associated with the
plurality of angles. The method may further include calculating a
total length for the converted plurality of collected line
segments, and calculating a total weighted value for the plurality
of angles. The method may also include characterizing the converted
plurality of collected line segments based on the categorization of
the converted plurality of collected line segments and the
plurality of angles based on the categorization of the plurality of
angles. The method may further include scoring the at least one
data visualization based on the characterization of the converted
plurality of collected line segments and the plurality of angles
plurality of angles.
[0004] A computer system for determining user preferences for at
least one data visualizations is provided. The computer system may
include one or more processors, one or more computer-readable
memories, one or more computer-readable tangible storage devices,
and program instructions stored on at least one of the one or more
storage devices for execution by at least one of the one or more
processors via at least one of the one or more memories, whereby
the computer system is capable of performing a method. The method
may include receiving the at least one data visualization. The
method may also include collecting a plurality of shapes, a
plurality of line segments, and a plurality of colors associated
with the at least one data visualization. The method may further
include converting the plurality of collected shapes and the
plurality of collected line segments to a plurality of polygonal
outlines. Additionally, the method may include categorizing the
converted plurality of collected line segments. The method may also
include measuring a plurality of lengths associated with the
converted plurality of collected line segments. The method may
further include identifying and categorizing a plurality of angles
formed by the converted plurality of collected line segments. The
method may also include determining a plurality of weighted values
associated with the plurality of angles. The method may further
include calculating a total length for the converted plurality of
collected line segments, and calculating a total weighted value for
the plurality of angles. The method may also include characterizing
the converted plurality of collected line segments based on the
categorization of the converted plurality of collected line
segments and the plurality of angles based on the categorization of
the plurality of angles. The method may further include scoring the
at least one data visualization based on the characterization of
the converted plurality of collected line segments and the
plurality of angles plurality of angles.
[0005] A computer program product for determining user preferences
for at least one data visualizations is provided. The computer
program product may include one or more computer-readable storage
devices and program instructions stored on at least one of the one
or more tangible storage devices, the program instructions
executable by a processor. The computer program product may include
program instructions to receive the at least one data
visualization. The computer program product may also include
program instructions to collect a plurality of shapes, a plurality
of line segments, and a plurality of colors associated with the at
least one data visualization. The computer program product may
further include program instructions to convert the plurality of
collected shapes and the plurality of collected line segments to a
plurality of polygonal outlines. Additionally, the computer program
product may also include program instructions to categorize the
converted plurality of collected line segments. The computer
program product may further include program instructions to measure
a plurality of lengths associated with the converted plurality of
collected line segments. The computer program product may also
include program instructions to identify and categorize a plurality
of angles formed by the converted plurality of collected line
segments. The computer program product may further include program
instructions to determine a plurality of weighted values associated
with the plurality of angles. The computer program product may also
include program instructions to calculate a total length for the
converted plurality of collected line segments, and calculating a
total weighted value for the plurality of angles. The computer
program product may further include program instructions to
characterize the converted plurality of collected line segments
based on the categorization of the converted plurality of collected
line segments and the plurality of angles based on the
categorization of the plurality of angles. The computer program
product may also include program instructions to score the at least
one data visualization based on the characterization of the
converted plurality of collected line segments and the plurality of
angles plurality of angles.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0006] These and other objects, features and advantages of the
present invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings. The various
features of the drawings are not to scale as the illustrations are
for clarity in facilitating one skilled in the art in understanding
the invention in conjunction with the detailed description. In the
drawings:
[0007] FIG. 1 illustrates a networked computer environment
according to one embodiment;
[0008] FIG. 2 is an example of a data visualization according to
one embodiment;
[0009] FIG. 3 is an example of data visualization measurements for
determining user preferences for data visualizations according to
one embodiment;
[0010] FIG. 4 is an operational flowchart illustrating the steps
carried out by a program for determining user preferences for data
visualizations according to one embodiment;
[0011] FIG. 5 is a block diagram of the system architecture of a
program for determining user preferences for data visualizations
according to one embodiment;
[0012] FIG. 6 is a block diagram of an illustrative cloud computing
environment including the computer system depicted in FIG. 1, in
accordance with an embodiment of the present disclosure; and
[0013] FIG. 7 is a block diagram of functional layers of the
illustrative cloud computing environment of FIG. 6, in accordance
with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0014] Detailed embodiments of the claimed structures and methods
are disclosed herein; however, it can be understood that the
disclosed embodiments are merely illustrative of the claimed
structures and methods that may be embodied in various forms. This
invention may, however, be embodied in many different forms and
should not be construed as limited to the exemplary embodiments set
forth herein. Rather, these exemplary embodiments are provided so
that this disclosure will be thorough and complete and will fully
convey the scope of this invention to those skilled in the art. In
the description, details of well-known features and techniques may
be omitted to avoid unnecessarily obscuring the presented
embodiments.
[0015] Embodiments of the present invention relate generally to the
field of computing, and more particularly, to data visualizations.
The following described exemplary embodiments provide a system,
method and program product for determining user preferences for
data visualizations. Therefore, the present embodiment has the
capacity to improve the technical field of data visualizations by
determining user preferences for data visualizations based on the
emotive impact of data visualizations to users. Specifically, the
present embodiment may determine user preferences for data
visualizations by characterizing the emotive impact of data
visualizations based on measurements associated with the data
visualizations.
[0016] As previously described with respect to data visualizations,
data visualizations are designed to communicate information clearly
and to stimulate viewer engagement and attention. Specifically,
designers and users of data visualizations may select data
visualizations that are both aesthetically and functionally
satisfactory. However, users may differ on the structure and color
of data visualizations based on the users' preferences. Therefore,
measurements may be used to determine user preferences for data
visualizations and the affect the data visualizations may have on
users. For example, the appearance of data visualizations may have
an impact on user preferences. Specifically, factors such as the
size, shape, and color of the data visualizations may have an
emotive impact on users, and thus, influence the preference users
have for the data visualizations. As such, it may be advantageous,
among other things, to provide a system, method and program product
for determining user preferences for data visualizations by
characterizing the emotive impact of data visualizations based on
measurements associated with the data visualizations. Therefore,
the system, method and program product may be used to learn user
preferences for data visualizations based on the characterization
of the data visualization, as well as used to recommend data
visualizations according to the learned user preferences.
[0017] According to at least one implementation of the present
embodiment, data visualizations may be received. Then, the shapes
and line segments associated with the received data visualization
may be collected. Thereafter, the shapes and line segments may be
converted to polygonal outlines. Next, the line segments may be
categorized. Then, the lengths of the line segments may be
calculated. Additionally, the angles formed by the line segments
may be identified and categorized. Next, weighted values for the
angles may be determined. Furthermore, the total length of the
collected line segments and the total weighted value of the angles
may be calculated. Thereafter, the collected line segments and the
angles may be characterized. Then, the data visualization may be
scored based on the characterizations.
[0018] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0019] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0020] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0021] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0022] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0023] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0024] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0025] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0026] The following described exemplary embodiments provide a
system, method and program product for determining user preferences
for data visualizations by characterizing the emotive impact of the
data visualizations based on data visualization measurements.
[0027] According to at least one implementation, at least one data
visualization may be received. Then, one or more shapes and line
segments associated with the at least one received data
visualization may be collected. Thereafter, the collected shapes
and line segments may be converted to polygonal outlines. Next, the
collected line segments may be categorized. Then, the lengths of
the line segments may be calculated. Additionally, the angles
formed by the line segments may be identified and categorized.
Next, weighted values for the angles may be determined.
Furthermore, the total length of the collected line segments and
the total weighted value of the angles may be calculated.
Thereafter, the collected line segments and the angles may be
characterized. Then, the data visualization may be scored based on
the characterized line segments and characterized angles.
[0028] Referring now to FIG. 1, an exemplary networked computer
environment 100 in accordance with one embodiment is depicted. The
networked computer environment 100 may include a computer 102 with
a processor 104 and a data storage device 106 that is enabled to
run a data visualization analysis program 108A and a software
program 114. The software program 114 may be an application program
such as an internet browser and an email program. The data
visualization analysis program 108A may communicate with the
software program 114. The networked computer environment 100 may
also include a server 112 that is enabled to run a data
visualization analysis program 108B and a communication network
110. The networked computer environment 100 may include a plurality
of computers 102 and servers 112, only one of which is shown for
illustrative brevity.
[0029] According to at least one implementation, the present
embodiment may also include a database 116, which may be running on
server 112. The communication network may include various types of
communication networks, such as a wide area network (WAN), local
area network (LAN), a telecommunication network, a wireless
network, a public switched network and/or a satellite network. It
may be appreciated that FIG.1 provides only an illustration of one
implementation and does not imply any limitations with regard to
the environments in which different embodiments may be implemented.
Many modifications to the depicted environments may be made based
on design and implementation requirements.
[0030] The client computer 102 may communicate with server computer
112 via the communications network 110. The communications network
110 may include connections, such as wire, wireless communication
links, or fiber optic cables. As will be discussed with reference
to FIG. 5, server computer 112 may include internal components 800a
and external components 900a, respectively and client computer 102
may include internal components 800b and external components 900b,
respectively. Server computer 112 may also operate in a cloud
computing service model, such as Software as a Service (SaaS),
Platform as a Service (PaaS), or Infrastructure as a Service
(IaaS). Server 112 may also be located in a cloud computing
deployment model, such as a private cloud, community cloud, public
cloud, or hybrid cloud. Client computer 102 may be, for example, a
mobile device, a telephone, a personal digital assistant, a
netbook, a laptop computer, a tablet computer, a desktop computer,
or any type of computing device capable of running a program and
accessing a network. According to various implementations of the
present embodiment, the data visualization analysis program 108A,
108B may interact with a database 116 that may be embedded in
various storage devices, such as, but not limited to a mobile
device 102, a networked server 112, or a cloud storage service.
[0031] According to the present embodiment, a program, such as a
data visualization analysis program 108A and 108B may run on the
client computer 102 or on the server computer 112 via a
communications network 110. The data visualization analysis program
108A, 108B may determine user preferences for data visualizations.
Specifically, a user using a computer, such as computer 102, may
run a data visualization analysis program 108A, 108B, that
interacts with a database 116, to determine the user preferences
for data visualizations by characterizing the emotive impact of
data visualizations based on data visualization measurements.
[0032] Referring now to FIG. 2, an example of a data visualization
200 in accordance with one embodiment is depicted. As previously
described in FIG. 1, the data visualization analysis program 108A,
108B (FIG. 1) may receive and measure data visualizations to
determine user preferences. Specifically, the data visualization
analysis program 108A, 108B (FIG. 1) may collect the set of shapes,
the line segments, and the colors comprising the data
visualization. Then, the data visualization analysis program 108A,
108B (FIG. 1) may convert the set of shapes and the line segments
to polygonal outlines. Next, the data visualization analysis
program 108A, 108B (FIG. 1) may measure the line segments and the
angles formed by the line segments. Furthermore, to determine the
user preferences, the data visualization analysis program 108A,
108B (FIG. 1) may characterize the data visualizations based on the
measurements. For example, the data visualization 200 may be a bar
graph having line segments such as a y-axis 202 and an x-axis 204,
a set of shapes such as squares 206 that have horizontal and
vertical line segments forming the set of shapes, and different
colors 208 presented on different parts of the data visualization
200. As such, the data visualization analysis program 108A, 108B
(FIG. 1) may measure the line segments and the angles formed by the
line segments, and characterize the data visualization 200 based on
the measurements, to determine the user preferences for bar
graphs.
[0033] Referring now to FIG. 3, an example of data visualization
measurements 300 in accordance with one embodiment of the present
invention is depicted. Specifically, the data visualization
analysis program 108A, 108B (FIG. 1) may categorize the line
segments associated with the polygonal outlines as angled,
horizontal, or vertical. Furthermore, the data visualization
analysis program 108A, 108B (FIG. 1) may categorize the angles
formed by the line segments as sharp, right, or gentle. Then, at
302, the data visualization analysis program 108A, 108B (FIG. 1)
may measure the lengths of the line segments by calculating the
number of pixels that form the line segments. Additionally, at 304,
the data visualization analysis program 108A, 108B (FIG. 1) may
calculate the angles formed by the line segments by determining
weighted values for the angles. Then, at 306, the data
visualization analysis program 108A, 108B (FIG. 1) may calculate
the total length of the line segments, and calculate the total
weighted value of the angles. Furthermore, the data visualization
analysis program 108A, 108B (FIG. 1) may characterize the line
segments and the angles. Next, at 308, the data visualization
analysis program 108A, 108B (FIG. 1) may score the data
visualization 200 (FIG. 2) based on the characterizations by
dividing the lengths of the characterized line segments by the
total length of the line segments, and dividing the weighted value
of the characterized angles by the total weighted value of the
angles.
[0034] For example, the data visualization measurements 300 may be
based on the data visualization 200 (FIG. 2) such as the bar graph.
As such, the data visualization analysis program 108A, 108B (FIG.
1) may collect the set of shapes, the line segments, and the colors
associated with the bar graph 200 (FIG. 2). Next, the data
visualization analysis program 108A, 108B (FIG. 1) may convert the
set of shapes and the line segments to polygonal outlines.
Thereafter, the data visualization analysis program 108A, 108B
(FIG. 1) may categorize the line segments. Specifically, the data
visualization analysis program 108A, 108B (FIG. 1) may determine
that the bar graph 200 (FIG. 2) comprises horizontal and vertical
line segments, and comprises no angled line segments. Thus, the
data visualization analysis program 108A, 108B (FIG. 1) may
categorize the line segments associated with the data visualization
200 (FIG. 2) as horizontal and vertical. Furthermore, the data
visualization analysis program 108A, 108B (FIG. 1) may categorize
the angles formed by the line segments. Specifically, the data
visualization analysis program 108A, 108B (FIG. 1) may determine
that the data visualization 200 (FIG. 2) comprises horizontal and
vertical line segments that form right angles. Therefore, the data
visualization analysis program 108A, 108B (FIG. 1) may categorize
the angles formed by the line segments as right.
[0035] Next, at 302, the data visualization analysis program 108A,
108B (FIG. 1) may measure the lengths of the line segments by
calculating the number of pixels forming the line segments.
Specifically, the data visualization analysis program 108A, 108B
(FIG. 1) may determine that the lengths of the horizontal line
segments are 8830 pixels. Furthermore, the data visualization
analysis program 108A, 108B (FIG. 1) may determine that the lengths
of the vertical line segments are 5752 pixels. Then, at 304, the
data visualization analysis program 108A, 108B (FIG. 1) may
calculate the angles formed by the line segments by determining
weighted values for the angles. Specifically, the data
visualization analysis program 108A, 108B (FIG. 1) may determine
that the weighted value of the right angles is 9574 pixels. Then,
at 306, the data visualization analysis program 108A, 108B (FIG. 1)
may calculate the total length of the line segments and the total
weighted value of the angles. Therefore, the data visualization
analysis program 108A, 108B (FIG. 1) may determine that the total
length of the line segments is 14582 pixels and that the total
weighted value is 9574 pixels.
[0036] Next, the data visualization analysis program 108A, 108B
(FIG. 1) may characterize the line segments and the angles based on
the categorizations of the line segments and the angles.
Specifically, the data visualization analysis program 108A, 108B
(FIG. 1) may characterize the angled line segments as dynamic, the
horizontal line segments as passive, and the vertical line segments
as strong. Furthermore, the data visualization analysis program
108A, 108B (FIG. 1) may characterize the sharp angles as severe,
the right angles as rational, and the gentle angles as gentle.
Then, at 308, based on the characterizations, the data
visualization analysis program 108A, 108B (FIG. 1) may score the
data visualization 200 (FIG. 2) by dividing the lengths of the line
segments by the total length of the line segments, and dividing the
weighted values of the angles by the total weighted value of the
angles. Therefore, the data visualization analysis program 108A,
108B (FIG. 1) may determine the following scores for the data
visualization 200 (FIG. 2): dynamic=0.0, passive=0.606,
strong=0.394, severe=0.0, rational=1.0, and gentle=0.0. As such,
based on the calculated scores, the data visualization analysis
program 108A, 108B (FIG. 1) may characterize the emotive impact of
the data visualization 200 (FIG. 2) as passive, strong, and very
rational.
[0037] Referring now to FIG. 4, an operational flowchart 400
illustrating the steps carried out by a program for determining
user preferences for data visualizations is depicted. At 402, the
data visualization analysis program 108A, 108B (FIG. 1) may receive
at least one data visualization. Specifically, the data
visualization analysis program 108A, 108B (FIG. 1) may receive data
visualizations in formats such as, but not limited to, portable
document format (PDF) and scalable vector graphic (SVG). For
example, the data visualization analysis program 108A, 108B (FIG.
1) may receive a data visualization 200 (FIG. 2), such as a bar
graph.
[0038] Next, at 404 the data visualization analysis program 108A,
108B (FIG. 1) may collect the set of shapes, the line segments, and
the colors associated with the data visualization 200 (FIG. 2). As
previously described in FIG. 2, the data visualization analysis
program 108A, 108B (FIG. 1) may receive a data visualization 200
(FIG. 2), such as a bar graph. Furthermore, the data visualization
analysis program 108A, 108B (FIG. 1) may collect the set of shapes,
the line segments, and the colors associated with the data
visualization 200 (FIG. 2). For example, the data visualization
analysis program 108A, 108B (FIG. 1) may collect the line segments
such as the y-axis 202 (FIG. 2) and the x-axis 204 (FIG. 2), the
set of shapes such as squares 206 (FIG. 2) that have horizontal and
vertical line segments forming the set of shapes, and the different
colors 208 (FIG. 2) presented on different parts of the data
visualization 200 (FIG. 2).
[0039] Then, at 406, the data visualization analysis program 108A,
108B (FIG. 1) may convert the collected set of shapes and line
segments to polygonal outlines. As previously described at step
404, the data visualization analysis program 108A, 108B (FIG. 1)
may collect the set of shapes, the line segments, and the colors
associated with a data visualization 200 (FIG. 2). As such, the
data visualization analysis program 108A, 108B (FIG. 1) may convert
the collected set of shapes and collected line segments to
polygonal outlines to provide a mechanism for measuring the data
visualizations 200 (FIG. 2). For example, and as previously
described in FIG. 2, the data visualization analysis program 108A,
108B (FIG. 1) may collect the set of shapes and the line segments
associated with the data visualization 200 (FIG. 2), such as the
bar graph. Therefore, the data visualization analysis program 108A,
108B (FIG. 1) may convert the collected set of shapes and the
collected line segments to polygonal outlines to provide a
mechanism for measuring the data visualization 200 (FIG. 2).
[0040] Next, at 408, the data visualization analysis program 108A,
108B (FIG. 1) may categorize the collected line segments. As
previously described in FIG. 3, the data visualization analysis
program 108A, 108B (FIG. 1) may categorize the collected line
segments associated with the polygonal outlines as angled,
horizontal, or vertical. Specifically, according to one embodiment,
the data visualization analysis program 108A, 108B (FIG. 1) may use
the following criteria to categorize the collected line segments:
vertical=line segments within 5 degrees of being a vertical line,
horizontal=line segments within 5 degrees of being a horizontal
line, and angled=line segments not meeting the vertical and
horizontal line segment criteria. For example, based on the line
segment criteria, the data visualization analysis program 108A,
108B (FIG. 1) may determine that the data visualization 200 (FIG.
2) comprises horizontal and vertical line segments, and no angled
line segments. Therefore, the data visualization analysis program
108A, 108B (FIG. 1) may categorize the collected line segments
associated with the data visualization 200 (FIG. 2) as vertical and
horizontal.
[0041] Then, at 410, the data visualization analysis program 108A,
108B (FIG. 1) may measure the lengths of the collected line
segments. Specifically, the data visualization analysis program
108A, 108B (FIG. 1) may measure the lengths of the collected line
segments by calculating the number of pixels forming the collected
line segments. For example, and as previously described in FIG. 3,
the data visualization analysis program 108A, 108B (FIG. 1) may
determine that the lengths of the collected horizontal line
segments are 8830 pixels (FIG. 3). Furthermore, the data
visualization analysis program 108A, 108B (FIG. 1) may determine
that the lengths of the vertical line segments are 5752 pixels
(FIG. 3).
[0042] Next, at 412, the data visualization analysis program 108A,
108B (FIG. 1) may categorize the angles formed by the collected
line segments. As previously described in FIG. 3, the data
visualization analysis program 108A, 108B (FIG. 1) may categorize
the angles formed by the collected line segments as sharp, right,
or gentle. Specifically, according to one embodiment, the data
visualization analysis program 108A, 108B (FIG. 1) may use the
following criteria to categorize the angles formed by the collected
line segments: sharp=angles less than 45 degrees, right=angles
within 5 degrees of forming a right angle, and gentle=angles over
145 degrees. For example, based on the angle criteria, the data
visualization analysis program 108A, 108B (FIG. 1) may determine
that the bar graph 200 (FIG. 2) comprises horizontal and vertical
line segments that form right angles. Therefore, the data
visualization analysis program 108A, 108B (FIG. 1) may categorize
the collected angles formed by the line segments as right.
[0043] Then, at 414, the data visualization analysis program 108A,
108B (FIG. 1) may measure the angles formed by the collected line
segments by determining weighted values for the collected angles.
As previously described in FIG. 3, the data visualization analysis
program 108A, 108B (FIG. 1) may measure the angles formed by the
collected line segments by determining weighted values for the
angles. Specifically, according to one embodiment, the data
visualization analysis program 108A, 108B (FIG. 1) may use the
following criteria to determine weighted values for the angles
formed by the collected line segments: for angles formed by at
least two line segments, the weighted value is the minimum length
of the line segment forming the angle. For example, a right angle
may be formed by a line segment with a length of 7 pixels and a
line segment with a length of 12 pixels; therefore, the weighted
value of the right angle is 7 pixels, since the line segment with
the length of 7 pixels is the minimum length as compared to the
line segment with the length of 12 pixels. For example, as
previously described in FIG. 3, the data visualization analysis
program 108A, 108B (FIG. 1) may determine a weighted value for the
sum of right angles formed in the data visualization 200 (FIG. 2).
Specifically, the data visualization analysis program 108A, 108B
(FIG. 1) may determine that the weighted value of the right angles
is 9574 pixels (FIG. 3). Furthermore, the data visualization
analysis program 108A, 108B (FIG. 1) may determine that the lengths
of the vertical line segments are 5752 pixels (FIG. 3).
[0044] Next, at 416, the data visualization analysis program 108A,
108B (FIG. 1) may calculate the total length of the collected line
segments, and calculate the total weighted value of the angles.
Specifically, the data visualization analysis program 108A, 108B
(FIG. 1) may calculate the total length of the line segments by
calculating the sum of the collected line segments, and may
calculate the total weighted value of the angles by determining the
sum of the weighted values for the categorized angles. For example,
and as previously described in FIG. 3, the data visualization
analysis program 108A, 108B (FIG. 1) may calculate the total length
of the collected line segments associated with the bar graph 200
(FIG. 2) by adding the horizontal line segments and the vertical
line segments. Therefore, the data visualization analysis program
108A, 108B (FIG. 1) may determine that the total length of the
collected line segments is 14582 (FIG. 3). Furthermore, the data
visualization analysis program 108A, 108B (FIG. 1) may determine
that the total weighted value of the angles for the bar graph 200
(FIG. 2) is 9574 (FIG. 3).
[0045] Then, at 418, the data visualization analysis program 108A,
108B (FIG. 1) may characterize the collected line segments and the
angles. Specifically, the data visualization analysis program 108A,
108B (FIG. 1) may characterize the collected line segments and the
angles based on the categorization of the collected line segments
and the angles. Therefore, according to one embodiment, the data
visualization analysis program 108A, 108B (FIG. 1) may use the
following criteria to characterize the collected line segments
based on the categorization of the line segments: line segments
categorized as vertical=strong, line segments categorized as
horizontal=passive, line segments categorized as angled=dynamic.
Furthermore, according to one embodiment, the data visualization
analysis program 108A, 108B (FIG. 1) may use the following criteria
to characterize the angles based on the categorization of the
angles: angles categorized as sharp=severe, angles categorized as
right=rational, angles categorized as gentle=gentle.
[0046] Next, at 420, the data visualization analysis program 108A,
108B (FIG. 1) may score the data visualization based on the
characterizations. Specifically, the data visualization analysis
program 108A, 108B (FIG. 1) may score the data visualization by
calculating values for the characterized line segments and the
characterized angles. As previously described in step 418, the data
visualization analysis program 108A, 108B (FIG. 1) may characterize
the collected line segments as strong, passive, and dynamic.
Furthermore, the data visualization analysis program 108A, 108B
(FIG. 1) may characterize the angles as severe, rational, and
gentle. Then, the data visualization analysis program 108A, 108B
(FIG. 1) may calculate values for the characterized line segments
and the characterized angles by dividing the lengths of the line
segments by the total length of the line segments, and by dividing
the weighted values of the angles by the total weighted value of
the angles, respectively. For example, and as previously described
in FIG. 3, the data visualization analysis program 108A, 108B (FIG.
1) may divide the lengths of the line segments by the total length
of the line segments to receive the following scores based on the
characterized line segments: dynamic=0.0, passive=0.606,
strong=0.394. Furthermore, the data visualization analysis program
108A, 108B (FIG. 1) may divide the weighted value of the angles by
the total weighted value of the angles to receive the following
scores based on the characterized angles: severe=0.0, rational=1.0,
and gentle=0.0. As such, based on the calculated scores, the data
visualization analysis program 108A, 108B (FIG. 1) may characterize
the data visualization 200 (FIG. 2) as passive, strong, and very
rational.
[0047] It may be appreciated that FIGS. 1-4 provide only an
illustration of one implementation and does not imply any
limitations with regard to how different embodiments may be
implemented. Many modifications to the depicted environments may be
made based on design and implementation requirements. For example,
in step 404 (FIG. 4), the data visualization analysis program 108A,
108B (FIG. 1) may identify, in the collected set of shapes, the
shapes that may include data, and may tag the shapes that include
data with a Boolean value. Furthermore, the data visualization
analysis program 108A, 108B (FIG. 1) may multiply the
lengths/weighted values of the shapes that include data by a fixed
amount to emphasize the shapes that include data. Additionally, the
data visualization analysis program 108A, 108B (FIG. 1) may
calculate the area of the polygonal outlines. Furthermore, the data
visualization analysis program 108A, 108B (FIG. 1) may categorize
the collected colors, determine weighted values for the collected
colors, and measure the ratio of the weighted values for the
collected colors to the total area of the polygonal outlines. Also,
according to one implementation of the present embodiment, in step
410 (FIG. 4) the data visualization analysis program 108A, 108B
(FIG. 1) may determine that line segments with a length of more
than 40 pixels may be measured as 40 pixels. Furthermore, according
to implementation, in step 410 (FIG. 4), the data visualization
analysis program 108A, 108B (FIG. 1) may determine that line
segments less than 3 pixels may be ignored.
[0048] FIG. 5 is a block diagram 500 of internal and external
components of computers depicted in FIG. 1 in accordance with an
illustrative embodiment of the present invention. It should be
appreciated that FIG. 5 provides only an illustration of one
implementation and does not imply any limitations with regard to
the environments in which different embodiments may be implemented.
Many modifications to the depicted environments may be made based
on design and implementation requirements.
[0049] Data processing system 800, 900 is representative of any
electronic device capable of executing machine-readable program
instructions. Data processing system 800, 900 may be representative
of a smart phone, a computer system, PDA, or other electronic
devices. Examples of computing systems, environments, and/or
configurations that may represented by data processing system 800,
900 include, but are not limited to, personal computer systems,
server computer systems, thin clients, thick clients, hand-held or
laptop devices, multiprocessor systems, microprocessor-based
systems, network PCs, minicomputer systems, and distributed cloud
computing environments that include any of the above systems or
devices.
[0050] User client computer 102 (FIG. 1), and network server 112
(FIG. 1) include respective sets of internal components 800a, b and
external components 900a, b illustrated in FIG. 5. Each of the sets
of internal components 800a, b includes one or more processors 820,
one or more computer-readable RAMs 822 and one or more
computer-readable ROMs 824 on one or more buses 826, and one or
more operating systems 828 and one or more computer-readable
tangible storage devices 830. The one or more operating systems
828, the software program 114 (FIG. 1), the data visualization
analysis program 108A (FIG. 1) in client computer 102 (FIG. 1), and
the data visualization analysis program 108B (FIG. 1) in network
server computer 112 (FIG. 1) are stored on one or more of the
respective computer-readable tangible storage devices 830 for
execution by one or more of the respective processors 820 via one
or more of the respective RAMs 822 (which typically include cache
memory). In the embodiment illustrated in FIG. 5, each of the
computer-readable tangible storage devices 830 is a magnetic disk
storage device of an internal hard drive. Alternatively, each of
the computer-readable tangible storage devices 830 is a
semiconductor storage device such as ROM 824, EPROM, flash memory
or any other computer-readable tangible storage device that can
store a computer program and digital information.
[0051] Each set of internal components 800a, b, also includes a R/W
drive or interface 832 to read from and write to one or more
portable computer-readable tangible storage devices 936 such as a
CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical
disk or semiconductor storage device. A software program, such as a
data visualization analysis program 108A and 108B (FIG. 1), can be
stored on one or more of the respective portable computer-readable
tangible storage devices 936, read via the respective R/W drive or
interface 832 and loaded into the respective hard drive 830.
[0052] Each set of internal components 800a, b also includes
network adapters or interfaces 836 such as a TCP/IP adapter cards,
wireless Wi-Fi interface cards, or 3G or 4G wireless interface
cards or other wired or wireless communication links. The data
visualization analysis program 108A (FIG. 1) and software program
114 (FIG. 1) in client computer 102 (FIG. 1), and the data
visualization analysis program 108B (FIG. 1) in network server 112
(FIG. 1) can be downloaded to client computer 102 (FIG. 1) from an
external computer via a network (for example, the Internet, a local
area network or other, wide area network) and respective network
adapters or interfaces 836. From the network adapters or interfaces
836, the data visualization analysis program 108A (FIG. 1) and
software program 114 (FIG. 1) in client computer 102 (FIG. 1) and
the data visualization analysis program 108B (FIG. 1) in network
server computer 112 (FIG. 1) are loaded into the respective hard
drive 830. The network may comprise copper wires, optical fibers,
wireless transmission, routers, firewalls, switches, gateway
computers and/or edge servers.
[0053] Each of the sets of external components 900a, b can include
a computer display monitor 920, a keyboard 930, and a computer
mouse 934. External components 900a, b can also include touch
screens, virtual keyboards, touch pads, pointing devices, and other
human interface devices. Each of the sets of internal components
800a, b also includes device drivers 840 to interface to computer
display monitor 920, keyboard 930 and computer mouse 934. The
device drivers 840, R/W drive or interface 832 and network adapter
or interface 836 comprise hardware and software (stored in storage
device 830 and/or ROM 824).
[0054] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0055] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0056] Characteristics are as Follows:
[0057] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0058] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0059] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0060] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0061] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0062] Service Models are as Follows:
[0063] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0064] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0065] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0066] Deployment Models are as Follows:
[0067] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0068] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0069] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0070] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0071] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0072] Referring now to FIG. 6, illustrative cloud computing
environment 600 is depicted. As shown, cloud computing environment
600 comprises one or more cloud computing nodes 100 with which
local computing devices used by cloud consumers, such as, for
example, personal digital assistant (PDA) or cellular telephone
600A, desktop computer 600B, laptop computer 600C, and/or
automobile computer system 600N may communicate. Nodes 100 may
communicate with one another. They may be grouped (not shown)
physically or virtually, in one or more networks, such as Private,
Community, Public, or Hybrid clouds as described hereinabove, or a
combination thereof. This allows cloud computing environment 600 to
offer infrastructure, platforms and/or software as services for
which a cloud consumer does not need to maintain resources on a
local computing device. It is understood that the types of
computing devices 600A-N shown in FIG. 6 are intended to be
illustrative only and that computing nodes 100 and cloud computing
environment 600 can communicate with any type of computerized
device over any type of network and/or network addressable
connection (e.g., using a web browser).
[0073] Referring now to FIG. 7, a set of functional abstraction
layers 700 provided by cloud computing environment 600 (FIG. 6) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 7 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0074] Hardware and software layer 710 includes hardware and
software components. Examples of hardware components include:
mainframes; RISC (Reduced Instruction Set Computer) architecture
based servers; storage devices; networks and networking components.
In some embodiments, software components include network
application server software.
[0075] Virtualization layer 712 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers; virtual storage; virtual networks, including
virtual private networks; virtual applications and operating
systems; and virtual clients.
[0076] In one example, management layer 714 may provide the
functions described below. Resource provisioning provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing provide cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal
provides access to the cloud computing environment for consumers
and system administrators. Service level management provides cloud
computing resource allocation and management such that required
service levels are met. Service Level Agreement (SLA) planning and
fulfillment provide pre-arrangement for, and procurement of, cloud
computing resources for which a future requirement is anticipated
in accordance with an SLA. A Data Visualization Analysis program
may determine user preferences for data visualizations by
characterizing the emotive impact of data visualizations based on
measurements associated with the data visualizations.
[0077] Workloads layer 716 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation; software development and lifecycle
management; virtual classroom education delivery; data analytics
processing; and transaction processing.
[0078] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
of the described embodiments. The terminology used herein was
chosen to best explain the principles of the embodiments, the
practical application or technical improvement over technologies
found in the marketplace, or to enable others of ordinary skill in
the art to understand the embodiments disclosed herein.
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