Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...InfluxData
Customers using ThingWorx and the Manufacturing Solutions often need to store property data longer than the Solutions default to. These customers are recommended to use InfluxDB, and this presentation will cover the key considerations for moving to InfluxDB vs the standard ThingWorx value streams. Join this session as Ward highlights ThingWorx’s solution and its easy implementation process.
Managing businesses electronically. E-Business and E-Commerce. Role of
transactions on the electronic media. Growth of E-Business in an organization. Impact of EBusiness
on industries; Components of E-Business technology; Role of websites and
Internet in E-Business. Emerging technologies for E-Business solutions
E-commerce Business Models, Major Business to Consumer (B2C) business models, Major Business to Business (B2B) business models, Business models in emerging E-commerce areas, How the Internet and the web change business: strategy, structure and process, The Internet: Technology Background, The Internet Today, Internet II-The Future Infrastructure, The World Wide Web, The Internet and the Web : Features
Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...InfluxData
Customers using ThingWorx and the Manufacturing Solutions often need to store property data longer than the Solutions default to. These customers are recommended to use InfluxDB, and this presentation will cover the key considerations for moving to InfluxDB vs the standard ThingWorx value streams. Join this session as Ward highlights ThingWorx’s solution and its easy implementation process.
Managing businesses electronically. E-Business and E-Commerce. Role of
transactions on the electronic media. Growth of E-Business in an organization. Impact of EBusiness
on industries; Components of E-Business technology; Role of websites and
Internet in E-Business. Emerging technologies for E-Business solutions
E-commerce Business Models, Major Business to Consumer (B2C) business models, Major Business to Business (B2B) business models, Business models in emerging E-commerce areas, How the Internet and the web change business: strategy, structure and process, The Internet: Technology Background, The Internet Today, Internet II-The Future Infrastructure, The World Wide Web, The Internet and the Web : Features
DI&A Slides: Descriptive, Prescriptive, and Predictive AnalyticsDATAVERSITY
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.
This webinar will compare and contrast these different data analysis activities and cover:
- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each
Role of business intelligence in knowledge managementShakthi Fernando
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.
Building a BI project is an important step in Business Intelligence gathering. This is a small introduction to its basic method and steps to be followed to build a BI project. Please comment on whether the slides were useful or not!
DI&A Slides: Descriptive, Prescriptive, and Predictive AnalyticsDATAVERSITY
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.
This webinar will compare and contrast these different data analysis activities and cover:
- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each
Role of business intelligence in knowledge managementShakthi Fernando
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.
Building a BI project is an important step in Business Intelligence gathering. This is a small introduction to its basic method and steps to be followed to build a BI project. Please comment on whether the slides were useful or not!
I have been drinking from a virtual fire hose since joining my most recent technology company, Anametrix, a cloud-based digital analytics innovator. A whole new book opened for me on how digital analytics can both increase top line revenue and reduce spend by shining a very bright flashlight into marketing efforts.
We are all painfully aware of the data explosion problem. In 2011, the Gartner Group stated that information volume collected by businesses today is growing at a minimum 59% annually. The rapid adoption of social media has also caused customer data to explode in the last few years, creating entirely new challenges for marketers. It is now imperative for organizations to think differently to accommodate the variety, volume, and velocity of their growing customer-related data.
This is where my recent experiences come in: I have personally seen how digital analytics can harness the power of massive amounts customer-related data. It can literally simplify the accelerating complexity by providing deep visibility – as well as clarity – into the effectiveness of various marketing efforts, across both online and offline channels.
I will now outline the role of IT and CFO in adopting cloud-based digital analytics solutions, discuss the benefits as well as challenges of moving to this emerging category, and provide some illustrative examples on how digital analytics can transform your marketing organization.
How Agile Application Portfolio Rationalization Delivers Digital SuccessCognizant
Application portfolios benefit from frequent tweaks based on honest user feedback; this enables IT to provide the right mix of applications with the least delay and cost to advance business objectives.
How to choose the right modern bi and analytics tool for your business_.pdfAnil
We highlight Top 5 Business Intelligence Tools as suggested by Gartner and ask critical questions that can help organizations make better and informed decisions.
17 Must-Do's to Create a Product-Centric IT OrganizationCognizant
Tightening IT-business alignment and embracing Agile, DevOps and Lean Startup principles, while transcending traditional project management disciplines by incorporating product engineering rigor, are critical to creating an effective, digitally enhanced business.
A Practical Approach for Power Utilities Seeking to Create Sustaining Busines...Cognizant
For power utilities, analytics are a key to enhanced operational performance and competitive standing. We offer a roadmap for determining and prioritizing relevant analytics, assessing analytics maturity, and implementing an effective analytics process encompassing smart meters, phasor measurement units and other useful sources.
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Big Data white paper - Benefits of a Strategic Visionpanoratio
Following the massive deployment of new mobile technologies and social media, sources of data regarding organizations’ customer and staff behaviors keep increasing. However only a few companies are able to have a real knowledge of all the corresponding data.
Big Data management provides new capabilities both in term of velocity and volume of heterogeneous data processing. Those new systems impact directly the way organizations manage operational data monitoring, and are still complex to implement.
The first benefit of Panoratio is to allow organizations to handle the strategic dimension of Big Data, to serve companies’ challenges and business priorities before to implement new optimized operational monitoring systems.
As per Gartner, global revenue in the business intelligence (BI) and analytics software market is forecast to reach $18.3 billion in 2017, an increase of 7.3 percent from 2016, according to the latest forecast from Gartner, Inc. By the end of 2020, the market is forecast to grow to $22.8 billion.
Report on strategic rules of Information System for changing the bases of com...Md. Khukan Miah
Achieving advantages requires broad IS management and user dialogue plus imagination. The process is complicated by the fact that many IS products are strategic though the potential benefits are very subjective and not easily verified. Often a strict ROI focus by senior management may turn attention toward narrow, well-defined targets as opposed to broader strategic opportunities that are harder to analyze.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Similar to Four Pillars of Business Analytics by Actuate (20)
SQL in Hadoop: To Boldly Go Where No Data Warehouse has Gone Before
Emma McGrattan
SVP Engineering, Actian Corp
Presentation at Strata + Hadoop World 2015
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1.
1
Best
Practices
Brief
Four
Pillars
of
Business
Analytics
Improve
customer
experience
and
analytic
capabilities
with
Actuate
BIRT
Business
goals
for
applications
must
address
data,
people,
process
and
technology,
according
to
Gartner’s
Jamie
Popkin.
In
a
keynote
presentation
at
the
Gartner
Catalyst
Conference,
Popkin
called
this
framework
the
Four
Pillars
of
Business
Analytics.
“Gartner
indicates
by
2015,
25%
of
analytic
capabilities
will
be
embedded
in
business
applications
and
designing
data
visualizations
for
web
and
mobile
apps
will
become
a
major
growth
engine
for
the
worldwide
Business
Intelligence
and
Analytics
Software
Market.”
–
Jamie
Popkin,
Managing
VP,
Gartner
Transforming
analytic
data
into
usable
business
information
and
designing
compelling
data-‐
driven
customer-‐facing
applications
remains
both
an
art
and
a
science,
and
a
clear
path
to
success
is
sometimes
hard
to
identify.
Inspired
by
Popkin’s
talk,
Actuate
believes
the
Four
Pillars
framework,
shown
in
Figure
1:
Gartner’s
Four
Pillars
of
Business
Analytics,
can
help
application
initiatives
succeed.
The
Four
Pillars
can
help
developers
and
IT
managers
ask
better
questions
–
and
get
better
answers
–
when
they
develop
business
analytics
applications.
An
emerging
set
of
design
principles,
inspired
by
the
Four
Pillars,
provides
a
blueprint
for
delivering
apps
that
inform,
connect,
and
motivate
end
users.
This
best
practices
brief
describes
the
Four
Pillars
of
Business
Analytics
framework,
then
shows
how
you
can
employ
the
Four
Pillars
to
understand
your
application
needs
and
design
and
build
applications
that
inform,
connect
and
motivate
users.
The
brief
also
explains
why
Actuate’s
BIRT
platform
is
ideal
for
high-‐user,
high-‐volume
analytic
applications.
Best
Practices
Brief
2.
2
Best
Practices
Brief
Understanding
the
Four
Pillars
Figure
1:
Gartner’s
Four
Pillars
of
Business
Analytics
1. Information
management
foundation
(Data)
The
Data
pillar
balances
governance
and
access
in
the
information-‐driven
enterprise.
It
requires
connecting
to
disparate
data
sources
–
regardless
of
their
type
and
location
–
to
build
a
virtual
data
warehouse
that
is
easy
and
secure
to
consume
and
use.
2. Organization
(People)
The
People
pillar
brings
IT
and
Business
communities
together
to
meet
shared
company
goals.
IT
people
require
a
visual,
programmatic
and
assembly
style
development
environment,
with
deep
integration
APIs
for
embedding
processes.
Business
people
need
secure
and
personalized
self-‐service,
along
with
the
ability
to
embed
analytics
in
existing
applications
and
display
them
anywhere
–
including
wearable
and
mobile
devices
–
to
boost
usage.
For
IT
people,
engaging
business
groups
early
in
the
application
design
and
development
process
helps
to
drive
conversations
forward.
3.
3
Best
Practices
Brief
3. Fact-‐based
decision
making
(Process)
The
Process
pillar
requires
having
the
right
information
at
the
right
time
to
make
better,
faster
decisions.
Because
different
roles
make
different
types
of
decisions,
it’s
important
to
leverage
the
same
data
to
support
a
variety
of
processes.
For
example,
operational
and
executive
users
require
dashboards;
customers
want
statements,
proposals
and
reports;
and
departments
need
performance
scorecards.
All
of
these
outputs
should
be
built
with
reusable
components
and
shared
across
groups
to
ensure
maximum
use.
4. Appropriate
technology
platform
(Technology)
The
Technology
pillar
encompasses
development
and
deployment,
with
systems
that
break
down
silos
of
capability.
Integrated,
open,
extensible
tools
support
growth,
so
Actuate
embraces
standards-‐based
content
development
environment
and
provides
a
flexible,
scalable
and
secure
automated
deployment
server
(BIRT
iHub).
This
combination
has
the
flexibility
to
deliver
data
from
any
source
and
embed
it
in
any
application.
Another
way
to
understand
the
Four
Pillars
is
through
the
Business
Analytics
Framework
shown
in
Figure
2.
In
this
arrangement,
the
Data
pillar
is
the
Information
foundation
of
the
framework,
and
the
People,
Process,
and
Platform
(Technology)
pillars
are
broken
out
by
their
specific
needs
and
requirements.
It’s
important
to
note
in
Figure
2:
The
Business
Analytics
Framework
the
“Business
Models,
Business
Strategy
and
Enterprise
Metrics”
spans
all
of
the
pillars,
as
does
system
performance.
Figure
2:
the
Business
Analytics
Framework
4.
4
Best
Practices
Brief
Addressing
Complexity
in
Customer
Facing
Applications
Once
you
understand
your
application
needs
in
the
context
of
the
Four
Pillars,
look
at
each
application
in
terms
of
users
and
data.
How
many
people
will
use
an
application,
and
how
much
personalized
data
each
user
will
require
from
the
app?
As
illustrated
in
Figure
3:
Customer-‐Facing
Applications
–
Complexity
Comparison,
applications
with
the
most
users
and
the
highest
volume
of
personalized
data
per
user
are
typically
the
most
complex,
and
the
most
challenging
in
terms
of
design,
data
access,
management,
and
delivery.
These
applications
require
a
secure,
scalable
platform
–
Actuate
BIRT
–
to
meet
unique
challenges:
• Take
a
customer-‐centric
view,
in
order
to
focus
on
adding
value
• Manage
increased
complexity
as
customers
and
data
are
added.
These
apps
–
particularly
those
used
by
financial
institutions’
customers
–
must
support
millions
of
users
who
aren’t
consistently
tech-‐savvy
and
who
have
unique
information
requirements
• Serve
enterprise
analytics
needs.
These
apps
must
move
beyond
departmental
scale
to
support
massive
amounts
of
data
and
users
Figure
3:
Customer-‐Facing
Applications
–
Complexity
Comparison
5.
5
Best
Practices
Brief
Applications
in
the
upper-‐right
quadrant
–
those
with
large
numbers
of
users
and
high
volumes
of
data
per
user
–
deliver
more
value
to
users
when
they
employ
analytics.
Analytics
is
the
discipline
that
applies
logic
and
mathematics
to
data
to
provide
insights
that
help
people
make
better
decisions.
(Indeed,
analytics
is
synonymous
with
“fact-‐based
decision-‐making”
found
in
the
Process
pillar.)
Four
types
of
analytics
–
descriptive,
diagnostic,
predictive,
and
prescriptive
–
are
illustrated
in
Figure
4:
Four
Types
of
Analytics.
Each
type
of
analytics
starts
with
data
and
poses
a
question,
and
each
requires
some
amount
of
human
input
to
arrive
at
a
decision.
In
the
case
of
decision
automation
–
a
subset
of
prescriptive
analytics
–
specific
actions
can
be
taken
based
on
data
without
human
input.
Each
of
the
four
types
of
analytics
has
a
place
in
an
information-‐driven
enterprise
and
in
your
analytics
strategy.
They
are
not
a
hierarchy;
prescriptive
analytics
are
not
better
than
predictive
analytics,
for
example,
and
each
type
of
analytics
is
applicable
to
specific
use
cases.
Figure
4:
Four
Types
of
Analytics
The
ways
users
consume
and
interact
with
analytics
vary.
Embedded
analytics,
dashboards
and
reports
are
common
methods
for
presenting
analytics
to
users.
Capabilities
such
as
queries,
data
visualizations
and
packaged
analytic
solutions
for
specific
business
problems
are
often
built
into
analytic
applications.