This document provides an overview of data warehousing and data mining. It defines a data warehouse as a centralized repository of integrated data from various sources used to support management decision making. Key characteristics of a data warehouse include being subject-oriented, integrated, non-volatile, and time-variant. The document contrasts operational data with data in a warehouse and discusses components of a data warehouse system like data acquisition, staging areas, and data marts. It also outlines the history and growth of data warehousing and data mining as well as their applications in domains like marketing, finance, fraud detection, and more.
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
meaning of data warehousing
needs of data warehousing
applications of data warehousing
architecture of data warehousing
advantages of data warehousing
disadvantages of data warehousing.
meaning of data mining
needs of data mining
applications of data mining
architecture of data mining
advantages of data mining
disadvantages of data mining
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
meaning of data warehousing
needs of data warehousing
applications of data warehousing
architecture of data warehousing
advantages of data warehousing
disadvantages of data warehousing.
meaning of data mining
needs of data mining
applications of data mining
architecture of data mining
advantages of data mining
disadvantages of data mining
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
This IT 812 business intelligence and data warehousing looks into the various factors including data warehousing, data mining and business intelligence as well the use and benefit of these for the modern day business organizations.
This IT 812 business intelligence and data warehousing looks into the various factors including data warehousing, data mining and business intelligence as well the use and benefit of these for the modern day business organizations.
Difference between data warehouse and data miningmaxonlinetr
What exactly is a Data Warehouse?
Termed as a special type of database, a Data Warehouse is used for storing large amounts of data, such as analytics, historical, or customer data, which can be leveraged to build large reports and also ensure data mining against it.@ http://maxonlinetraining.com/why-is-data-warehousing-online-training-important/
What is Data mining?
The process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions’
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The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Data Preprocessing- Data Warehouse & Data MiningTrinity Dwarka
Data Preprocessing- Data Warehouse & Data Mining
Data Quality
Reasons for inaccurate data
Reasons for incomplete data
Major Tasks in Data Preprocessing
Forms of Data Preprocessing
Data Cleaning
Incomplete (Missing) Data
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CONTACT NUMBER --
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7508509709
6months industrial training in data mining, jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
Address-Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
CONTACT NUMBER --
09041262727
07508509730
7508509709
6 weeks summer training in data mining,ludhianadeepikakaler1
E2marix is leading Training & Certification Company offering Corporate Training Programs, IT Education Courses in diversified areas.Since its inception, E2matrix educational Services have trained and certified many students and professionals.
TECHNOLOGIES PROVIDED -
MATLAB
NS2
IMAGE PROCESSING
.NET
SOFTWARE TESTING
DATA MINING
NEURAL networks
HFSS
WEKA
ANDROID
CLOUD computing
COMPUTER NETWORKS
FUZZY LOGIC
ARTIFICIAL INTELLIGENCE
LABVIEW
EMBEDDED
VLSI
Address
Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
email-e2matrixphagwara@gmail.com
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ANDROID
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ARTIFICIAL INTELLIGENCE
LABVIEW
EMBEDDED
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CONTACT NUMBER --
09041262727
07508509730
7508509709
Big data-analytics-changing-way-organizations-conducting-businessAmit Bhargava
Hi Friends ,
There is an interesting post on how to leveraging Big data analytics in an Integrated GRC Environment in an Organize to have visibility in core enterprises issues on real time basis . This presentation is from Metric stream -an international and Global GRC soloutioning providers in association with Dr. Kirk. D. Borne - Big data consultant and Adviser .Hope you like it and enjoy as well.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
5. Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
Which customers
are most likely to go
to the competition ?
What impact will
new products/services
have on revenue
and margins?
What product prom-
-otions have the biggest
impact on revenue?
What is the most
effective distribution
channel?
A PRODUCER WANTS TO KNOW….
6. DATA, DATA EVERYWHERE
YET ...
I can’t get the data I need
need an expert to get the data
I can’t understand the data I found
available data poorly documented
I can’t use the data I found
results are unexpected
data needs to be transformed from
one form to other
I can’t find the data I need
data is scattered over the network
many versions, subtle differences
7. 1960s:
Data collection, database creation, IMS and network DBMS
1970s:
Relational data model, relational DBMS implementation
1980s:
RDBMS, advanced data models (extended-
relational, OO, deductive, etc.) and application-oriented DBMS
(spatial, scientific, engineering, etc.)
1990s—2000s:
Data mining and data warehousing, multimedia databases, and
Web databases
7
8. DATA WAREHOUSE
The data warehouse is that portion of an overall
Architected Data Environment that serves as the
single integrated source of data for processing
information.
12. HOW IS THE WAREHOUSE
DIFFERENT?
The data warehouse is distinctly different from
the operational data used and maintained by day-
to-day operational systems. Data warehousing is
not simply an “access wrapper” for operational
data, where data is simply “dumped” into tables
for direct access.
13. OPERATIONAL DATA
Application oriented
Detailed
Accurate, as of the moment
of access
Serves the clerical
community
Performance sensitive
(immediate response required
when entering a transaction)
Flexible structure; variable
contents
Small amount of data used in
a process
DATA WAREHOUSE
Subject oriented
Summarized
Represents values over time
Serves the managerial
community
Performance relaxed
(immediacy not required)
Static structure
large amount of data used in
a process
15. Data explosion problem
Automated data collection tools and mature database
technology lead to tremendous amounts of data stored in
databases, data warehouses and other information
repositories
We are drowning in data, but starving for knowledge!
Solution: Data warehousing and data mining
Extraction of interesting knowledge (rules, regularities,
patterns, constraints) from data in large databases
15
16. Data mining (knowledge discovery in databases):
Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) information or patterns
from data in large databases
Alternative names:
Data mining: a misnomer?
Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting, business
intelligence, etc.
What is not data mining?
(Deductive) query processing.
Expert systems or small ML/statistical programs
16
17. Database analysis and decision support
Market analysis and management
target marketing, customer relation management, market
basket analysis, cross selling, market segmentation
Risk analysis and management
Forecasting, customer retention, improved
underwriting, quality control, competitive analysis
Fraud detection and management
Other Applications
Text mining (news group, email, documents)
Stream data mining
Web mining.
DNA data analysis
17
18. Where are the data sources for analysis?
Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
Target marketing
Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over time
Conversion of single to a joint bank account: marriage, etc.
Cross-market analysis
Associations/co-relations between product sales
Prediction based on the association information
18
19. 19
Customer profiling
data mining can tell you what types of customers buy what
products (clustering or classification)
Identifying customer requirements
identifying the best products for different customers
use prediction to find what factors will attract new customers
Provides summary information
various multidimensional summary reports
statistical summary information (data central tendency and
variation)
20. Finance planning and asset evaluation
cash flow analysis and prediction
contingent claim analysis to evaluate assets
cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
Resource planning:
summarize and compare the resources and spending
Competition:
monitor competitors and market directions
group customers into classes and a class-based pricing procedure
set pricing strategy in a highly competitive market
20
21. Applications
widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
Approach
use historical data to build models of fraudulent behavior and use
data mining to help identify similar instances
Examples
auto insurance: detect a group of people who stage accidents to
collect on insurance
money laundering: detect suspicious money transactions (US
Treasury's Financial Crimes Enforcement Network)
medical insurance: detect professional patients and ring of
doctors and ring of references
21
22. 22
Detecting inappropriate medical treatment
Australian Health Insurance Commission identifies that in many
cases blanket screening tests were requested (save Australian
$1m/yr.).
Detecting telephone fraud
Telephone call model: destination of the call, duration, time of
day or week. Analyze patterns that deviate from an expected
norm.
British Telecom identified discrete groups of callers with frequent
intra-group calls, especially mobile phones, and broke a
multimillion dollar fraud.
Retail
Analysts estimate that 38% of retail shrink is due to dishonest
employees.
23. Sports
IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
Astronomy
JPL and the Palomar Observatory discovered 22 quasars with the
help of data mining
Internet Web Surf-Aid
IBM Surf-Aid applies data mining algorithms to Web access logs
for market-related pages to discover customer preference and
behavior pages, analyzing effectiveness of Web
marketing, improving Web site organization, etc.
23
Editor's Notes
A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context
Subject-Oriented: Information is presented according to specific subjects or areas of interest, not simply as computer files. Data is manipulated to provide information about a particular subject. For example, the SRDB is not simply made accessible to end-users, but is provided structure and organized according to the specific needs. Integrated: A single source of information for and about understanding multiple areas of interest. The data warehouse provides one-stop shopping and contains information about a variety of subjects. Thus the OIRAP data warehouse has information on students, faculty and staff, instructional workload, and student outcomes. Non-Volatile: Stable information that doesn’t change each time an operational process is executed. Information is consistent regardless of when the warehouse is accessed. Time-Variant: Containing a history of the subject, as well as current information. Historical information is an important component of a data warehouse. Accessible: The primary purpose of a data warehouse is to provide readily accessible information to end-users. Process-Oriented: It is important to view data warehousing as a process for delivery of information. The maintenance of a data warehouse is on-going and iterative in nature.
Data Mart: A data structure that is optimized for access. It is designed to facilitate end-user analysis of data. It typically supports a single, analytic application used by a distinct set of workers. Staging Area: Any data store that is designed primarily to receive data into a warehousing environment. OLAP (On-Line Analytical Processing): A method by which multidimensional analysis occurs where multidimensional analysis is the ability to manipulate information by a variety of relevant categories or “dimensions” to facilitate analysis and understanding of the underlying data. It is also sometimes referred to as “drilling-down”, “drilling-across” and “slicing and dicing”OLAP Tools: A set of software products that attempt to facilitate multidimensional analysis. Can incorporate data acquisition, data access, data manipulation, or any combination thereof.