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Data Warehousing

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Lecture-39
Web Warehousing: An introduction
Through the billions of Web pages created with HTML and XML, or generated dynamically by
underlying Web database service engines, the Web captures almost all aspects of human endeavor
and provides a fertile ground for data mining. However, searching, comprehending, and using the
semi structured information stored on the Web poses a significant challenge because this data is
more sophisticated and dynamic than the information that commercial database systems store. To
supplement keyword -based indexing, which forms the cornerstone for Web search engines,
researchers have applied data mining to Web -page ranking. In this context, data mining helps
Web search engines find high -quality Web pages and enhances Web clickstream analysis. For the
Web to reach its full potential, believe this technology will play an increasingly important role in
meeting the challenges of developing the intelligent Web.
Putting the pieces together
Figure-39.1: Putting pieces together
Figure 39.1 shows the same multi-tier architecture for data warehousing, as was shown in a
previous lecture. The figure shows different components of an overall decision support system
and their interaction.
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Web
Dat
Figure-39.2: Putting pieces together ­Web warehousing
In case of Web warehousing, Figure 39.1 has been slightly modified to highlight components the
major components needed, as shown in Figure 39.2. Here the main source i.e. data source is the
World Wide Web. The central repository Data Warehouse has changed to a Web data warehouse
and data mining tools are customized to cater for the semi structured and dynamic web data.
Background
Internet population stands at around a billion users.
Exponential growth rate of web and size. Indexable web is more than 11.5 billion pages (Jan
2005).
Adoption rate of intranet & extranet warehouse having similar growth rate
Web enabled versions of tools are available, but adoption differ.
Media is div erse and other than only data i.e. text, image, audio etc.
We are all familiar with the growth rate of the public Web. Regardless of the metric used to
measure its growth attached networks, servers, users or pages the growth rate continues to
exhibit a n exponential pattern. In the same vein, the adoption rate of intranet and extranet data
warehouses (i.e., Web warehouses) has exhibited a similar pattern, although the pattern has
lagged public adoption. While data warehouse and business intelligence vend ors have offered
Web-enabled versions of their tools for the past few years, and the sales of their Web offerings
began to substantially exceed the sales of their traditional client/server versions. However, the
patterns of adoption differ by more than a simple lag.
The media delivered by a Web warehouse include not only data, but text, graphics, image, sound,
video, and other forms as well.
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Reasons for web warehousing
1. Searching the web (web mining).
2. Analyzing web traffic.
3. Archiving the web.
The three reasons for warehousing web data are as listed in the slide. First, web warehousing can
be used to mine the huge web content for searching information of interest. Its like searching the
golden needle from the haystack. Second reason of Web warehousing is to analyze the huge web
traffic. This can be of interest to Web Site owners, for e-commerce, for e-advertisement and so
on. Last but not least reason of Web warehousing is to archive the huge web content because of
its dynamic nature. As we proceed we will discuss all theses concepts in further detail.
Web searching
Web is large, actually very large.
To make it useful must be able to find the page(s) of interest/relevance.
How can the search be successful?
Three major types of searches, as follows:
1.  Keyword-based search
2.  Querying deep Web sources
3.  Random surfing
The success of google
The Web--an immense and dynamic collection of pages that includes countless hyperlinks and
huge volumes of access and usage information--provides a rich and unprecedented data mining
source. How can a search identify that portion of the Web that is truly relevant to one user's
interests? How can a search find high-quality Web pages on a specified topic?
Currently, users can choose from three major approaches when ac cessing information stored on
the Web:
(i) Keyword -based search or topic -directory browsing with search engines such as Google or
Yahoo, which use keyword indices or manually built directories to find documents with specified
keywords or topics;
(ii) Querying deep Web sources--where information, such as amazon.com's book data and
realtor.com's real-estate data, hides behind searchable database query forms --that, unlike the
surface Web, cannot be accessed through static URL links; and
(iii)Random surfing that follows Web linkage pointers.
The success of these techniques, especially with the more recent page ranking in Google and
other search engines shows the Web's great promise to become the ultimate information system.
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Drawbacks of traditional web sear ches
1. Limited to keyword based matching.
2. Can not distinguish between the contexts in which a link is used.
3. Coupling of files has to be done manually.
Data warehousing concepts are being applied over the Web today. Traditionally, simple search
engines have been used to retrieve information from the Web. These serve the basic purpose of
data recovery, but have several drawbacks. Most of these engines are based on keyword searches
limited to string matching only. That narrows down our retrieval options. Als o we have links, at
times several levels of them in a particular context. But simple search engines do not do much
justice to obtaining information present in these links. They provide direct information recovery,
but not enough indirect link information. Also if we have files related to certain subjects and need
to couple these, the coupling has to be done manually. Web search engines do not provide
mechanisms to incorporate the above features. These and other reasons have led to further
research in the area of Web knowledge discovery and have opened the window to the world of
Web Warehousing..
Why web warehousing-Reason no. 1?
Web data is unstructured and dynamic, keyword search is insufficient.
To increase usage of web, must make it more comprehensible.
Data Mining is required for understanding the web.
Data mining used to rank and find high quality pages, thus making most of search time.
The Web with billions of Web pages provides a fertile ground for data mining. However,
searching, comprehend ing, and using the semi structured information stored on the Web poses a
significant challenge because this data is more sophisticated and dynamic than the information
that commercial database systems store.
To supplement keyword -based indexing, which forms the cornerstone for Web search engines,
researchers have applied data mining to Web -page ranking. In this context, data mining helps
Web search engines find high -quality Web pages and enhances Web clickstream analysis. For the
Web to reach its full potential, however, we must improve its services, make it more
comprehensible, and increase its usability. As researchers continue to develop data mining
techniques, we believe this technology will play an increasingly important role in meeting the
challenges of developing the intelligent Web.
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Why web warehousing-Reason no. 2?
Web log contains wealth of information, as it is a key touch point.
Every customer interaction is recorded.
Success of email or other marketing campaign can be measured by integrating with other
operational systems.
Common measurements are:
·
Number of visitors
·  Number of sessions
·
Most requested pages
·
Robot activity
·  Etc.
The Web log contains a wealth of information about the company Web site, a touch point for e -
business. The Web log, or clickstream, gathers this information by recording every interaction a
customer or potential customer has with the business over the Internet. The success of a specific
e-mail, marketing or ad campaign can be directly measured and quantified by integrating the Web
log with other operational systems such as sales force automation (SFA), customer relationship
management (CRM) and enterprise resource planning (ERP) applications.
Common measurements include number of visitors, number of sessions, most requested page,
most requested download, most accessed directories, leading referrer sites, leading browser and
operating system, visits by geographic breakdown, and many others. This information can be
used to modify the design of the Web site, change ad campaigns or develop partnering
relationships with other sites. While these metrics are insightful and important for managing the
corporate e-business on a short term or tactical basis, the real value of this knowledge comes
through integration of this e-resource with other customer touch-point information.
Why web warehousing-Reason no. 3?
Shift from distribution platform to a general communication platform.
New uses from e-government to e -commerce and new forms of art etc. between different levels of
society.
Thus web is worthy to be archived to be used for several other projects.
Such as snapshots of preserving time.
In recent years, we have seen not only an incredible growth of the amount of information
available on the Web, but also a sh ift of the Web from a platform for distributing information
among IT-related persons to a general platform for communication and data exchange at all levels
of society. The Web is being used as a source of information and entertainment; forms the basis
for e-government and e-commerce; has inspired new forms of art; and serves as a general
platform for meeting and communicating with others via various discussion forums. It attracts
and involves a broad range of groups in our society, from school children to professionals of
various disciplines to seniors, all forming their own unique communities on the Web. This
situation gave rise to the recognition of the Web's worthiness of being archived, and the
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subsequent creation of numerous projects aiming at the creation of World Wide Web archives.
Snapshot -like copies of the Web preserve an impression of what hyperspace looked like at a
given point in time, what kind of information, issues, and problems people from all kinds of
cultural and sociological backgrounds were interested in, the means they used to communicate
their interests over the Web, characteristic styles of how Web sites were designed to attract
visitors, and many other facets of this medium.
How Client Server interactions take place?
§
Understanding the interactions essential for understanding the source and meaning of the
data in the clickstream
§
Sequence of actions for the browser and Web site interaction using HTTP
Understanding the interactions between a Web client (browser) and a Web server (W eb site) is
essential for understanding the source and meaning of the data in the clickstream. In Figure in
next slide, we show a browser, designated "Visitor Browser." We'll look at what happens in a
typical interaction from the perspective of a browser user. The browser and Web site interact with
each other across the Internet using the Web's communication protocol -the Hypertext Transfer
Protocol (HTTP).
How Client Server interactions take place?
Illustration
Figure-39.3: Il lustration of how Client Server interactions
Figure 39.3 illustrates the steps during a client server interaction on the WWW. Each of the
activity or action has been shown with a sequence . Lets briefly look at these sequence of actions.
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Action 1
User tries to access the site using its URL.
Action 2
The server returns the requested page, websitepage.html. Once the document is entirely retrieved,
the visitor's browser scans for references to other Web documents that it must fulfill before its
work is completed. In order to speed up the response time, most browsers will execute these
consequential actions in parallel, typically with up to 4 or more HTTP requests being serviced
concurrently.
Action 3
The visitor's browser finds a reference to a logo imag e that is located at Website. Com. The
browser issues a second request to the server, and the server responds by returning the specified
image.
Action 4
The browser continues to the next reference for another image from Banner-ad.com. The browser
makes this request, and the server at Banner -ad.com interprets a request for the image in a special
way. Rather than immediately sending back an image, the banner-ad server first issues a cookie
request to the visitor's browser requesting the contents of any cookie that might have been placed
previously in the visitor's PC by Banner-ad.com. There are two options based on the response of
the cookie request;
Option I: Cookie Request Fulfilled: The banner-ad Web site retrieves this cookie and uses the
contents as a ke y to determine which banner ad the visitor should receive. This decision is based
on the visitor's interests or on previous ads. Once the banner-ad server makes a determination of
the optimal ad, it returns the selected image to the visitor. The banner-ad server then logs which
ad it has placed along with the date and the clickstream data from the visitor's request.
Option II: No Cookie Found: If thebanner -ad server had not found its own cookie, it would have
sent a new persistent cookie to the visitor's browser for future reference, sent a random banner ad,
and started a history in its database of interactions with the visitor's browser.
Referrer: The HTTP request from the visitor's browser to the banner-ad server carried with it a
key piece of informat ion known as the referrer. The referrer is the URL of the agent responsible
for placing the link on the page. In the example the referrer is Website.com/websitepage.html.
Because Banner-ad.com now knows who the referrer was, it can credit Website. com for having
placed an advertisement on a browser window.
Action 5
In the original HTML document, websitepage.html had a hidden field that contained a request to
retrieve a specific document from Profiler.com. When this request reached the profiler server,
Profiler.com immediately tried to find its cookie in the visitor's browser. This cookie would
contain a user ID placed previously by the profiler that is used to identify the visitor and serves as
a key to personal information contained in the profiler's database.
Action 6
The profiler might either return its profile data to the visitor's browser to be sent back to the initial
Web site or send a real-time notification to the referrer, Website.com, via an alternative path
alerting Website.com that the visitor is currently logged onto Website. com and viewing a
specific page. This information also could be returned to the HTML document to be returned to
the referrer as part of a query string the next time an HTTP request is sent to Website.com.
Although Figu re 39.3 shows three different sites involved in serving the contents of one
document, it is possible, indeed likely, that these functions will be combined into fewer servers. It
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is likely that advertising and profiling will be done within the same enterp rise, so a single request
(and cookie) would suffice to retrieve personal information that would more precisely target the
ads that are returned. However, it is equally possible that a Web page could contain references to
different ad/profile services, providing revenue to the referrer from multiple sources.
Low level web traffic Analysis
Is anyone coming?
If people are coming, identify which pages they are viewing.
Once you rank your content, you can tailor it to satisfy your visitors.
Detailed analysis helps increase traffic, such which sites are referring visitors.
There are many reasons why you might want to analyze your Web site's traffic. At the lowest
level, you want to determine if anyone is coming to your Web site in order to justify the site's
existence.
Once you have determined that people are indeed visiting your Web site, the next step is to
identify which pages they are viewing. After determining what content is popular and what
content is ignored, you can tailor your content to sat isfy your visitors.
High level web traffic analysis
Combining your Web site traffic data with other data sources to find which banner ad campaigns
generated the most revenue vs. just the most visitors.
Help calculate ROI on specific online marketing ca mpaigns.
Help get detailed information about your online customers and prospects.
A more detailed analysis of your Web site traffic will assist you in increasing the traffic to your
Web site. For example, by determining which sites are referring visitors to your site, you can
determine which online marketing activities are most successful at driving traffic to your site. Or,
by combining your Web site traffic data with other data sources, such as your customer databases,
you can determine which banner ad campaigns, for example, generated the most revenue versus
just the most visitors. This allows you to calculate the return on investment (ROI) on specific
online marketing campaigns as well as get detailed information about your online customers and
prospects.
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What information can be extracted?
Identify the person by the originating URL if filled a form.
Came through search engine, or referral, if search engine using which keyword.
Viewing which pages, using which path and how long a view.
W hich visitors spent the most money...
Thus a lot to discover.
First, you can determine who is visiting your Web site. Minimally, you can determine what
company the person is from (the host computer that they are using to surf the Web-- ford.com
would be the Ford Motor Company, for example). Additionally, if a visitor filled out an online
form during a visit to your Web site, you can link the form data with his or her Web site traffic
data and identify each visitor by name, address, and phone number (and any other data that your
online forms gather).
You can also learn where your visitors are coming from. For example, did they find your site by
using a search engine such as Google or did they click on a link at another site? If they did use a
search engine, which keywords did they use to locate your site?
Furthermore, you can identify which pages your Web site visitors are viewing, what paths they
are taking within your site, and how long they are spending on each page and on the site. You can
also determine w hen they are visiting your site and how often they return.
At the highest level, you can determine which of your Web site visitors spent the most money
purchasing your products and services and what the most common paths and referring pages were
for these visitors.
As you can see, you can discover a great deal about your Web site visitors --and we only touched
upon a few introductory topics.
Where does traffic info. come from?
1. Log files.
2. Cookies.
3. Network traffic.
4. Page tagging.
5. ISP (Internet Service Provider)
6. Others
To track traffic on a web site
http://www.alexa.com/data/details/traffic_details?q=&url=http://www.domain.com
The principal sources of web traffic are as follows:
1. Log files.
2. Cookies.
3. Network traffic.
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4. Page tagging.
5. ISP
Others
We will not discuss all of them.
ISPs
Besides log files, we may get clickstream data from referring partners or from Internet service
providers (ISPs). If we are an ISP providing Web access to directly connected customers, we
have a unique perspective because we see every click of our familiar captive visitors that may
allow much more powerful and invasive analysis of the end visitor's sessions
Others
We also may get clickstream data from Web-watcher services that we have hired to place a
special control on certain Web pages that alert them to a visitor opening the page. Another
important form of clickstream data is the search specification given to a search engine that then
directs the visitor to the Web site.
Web traffic record: Log files
Connecting to a web site c onnects to web sever that serves files.
Web server records each action in a text file.
In raw log file is useless, as unstructured and very large.
Many formats and types of log files, can make your own too.
Analyzers can be configured to read most log files.
Web traffic record: Log file format
This is just a portion of the first 12 lines of a 250,000 line log file (the file scrolls to the left for
several more pages).
This is a 10 megabyte log file and it represents one day's worth of traffic to a low volume Web
site.
Figure- 39.4: A sample Log file format
Figure 39.4 shows a sample web log file, indeed a very small portion of the actual log file. Here,
12 lines have been shown and the file can be scrolled to left to see more columns. The
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complexity of dealing with Web log data can well be understood , and mostly Web can be even
more complex and huge than this sample file.
Web log file formats
Format of web log dependent on many factors, such as:
·  Web server
·  Application
·  Configuration options
Several servers support CLF ECLF format.
Web log file formats vary depending on the Web server application and configuration options
selected during installation.  Most Web server applications (including those from Apache,
Microsoft and Nets cape) support Common Log file Format (CLF, sometimes pronounced "clog")
or Extended Common Log file Format (ECLF). CLF and ECLF formats share the same initial
seven fields, but ECLF adds referrer and agent elements.
Web Log File Formats
Table-39.1: Web Log File Formats
Our example proxy log data file contained following fields
i. Timestamp (date in Table 39.1)
ii. Elapsed Time
This is the time that transaction busied the cache. This time is given in milliseconds. For the
request where there was a cache-miss this time is minimal, where the request engaged the cache,
this time is considerable.
iii. Client Address (host in Table 39.1)
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iv. Log Tag
This field tells the result of the cache operation.
v. HTTP Code (status in Table 39.1)
vi. Size (bytes in Table 39.1)
vii. Request Method (request in Table 39.1)
This is the method which client used to initiate the request and be dealt with the proper treatment
on the server side.
viii. URL
This is the URL which was request by the client. Therecan be many variations in the
representation, start and termination of the URL.
ix. User Ident (ident in Table 39.1)
This field is used to identify the requesting user on the network.
x. Hierarchy Data
This field provides the hierarchy data of the request from the same client in the current request.
xi. Content Type
This field contains the type of data which was requested. The values in this field are the standard
MIME types which describe the data contents.
Web traffic record: Cookies
Web log contains one-way traffic record i.e. server to client.
No information about the visitor.
Thus complex heuristics employed that analyze links, time etc. to identify the user.
Cookie is a small text file generated by the web server and stored on the client machine.
This file is read by the web server on a repeat visit.
One of the fundamental flaws of analyzing server log files is that log files contain information
about files transferred from the server to the client--not information about people visiting the
Web site. For this reason, most log analyzers employ complex heuristics that analyze the raw log
data and make educated guesses based on the client's hostname or IP address, the time between
page views, the browser type, and the referring page to estimate the number of visits that a site
has received. While these estimates are typically good enough, and often better than metrics you
can obtain with traditional media, sometimes you simply need more.
A cookie is a small piece of information generated by the Web server and stored on the client
machine. Advanced Web site analysis solutions offer the option to use cookies to get much more
accurate visitor counts, while also allowing analysis of repeat visitors.
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Table of Contents:
  1. Need of Data Warehousing
  2. Why a DWH, Warehousing
  3. The Basic Concept of Data Warehousing
  4. Classical SDLC and DWH SDLC, CLDS, Online Transaction Processing
  5. Types of Data Warehouses: Financial, Telecommunication, Insurance, Human Resource
  6. Normalization: Anomalies, 1NF, 2NF, INSERT, UPDATE, DELETE
  7. De-Normalization: Balance between Normalization and De-Normalization
  8. DeNormalization Techniques: Splitting Tables, Horizontal splitting, Vertical Splitting, Pre-Joining Tables, Adding Redundant Columns, Derived Attributes
  9. Issues of De-Normalization: Storage, Performance, Maintenance, Ease-of-use
  10. Online Analytical Processing OLAP: DWH and OLAP, OLTP
  11. OLAP Implementations: MOLAP, ROLAP, HOLAP, DOLAP
  12. ROLAP: Relational Database, ROLAP cube, Issues
  13. Dimensional Modeling DM: ER modeling, The Paradox, ER vs. DM,
  14. Process of Dimensional Modeling: Four Step: Choose Business Process, Grain, Facts, Dimensions
  15. Issues of Dimensional Modeling: Additive vs Non-Additive facts, Classification of Aggregation Functions
  16. Extract Transform Load ETL: ETL Cycle, Processing, Data Extraction, Data Transformation
  17. Issues of ETL: Diversity in source systems and platforms
  18. Issues of ETL: legacy data, Web scrapping, data quality, ETL vs ELT
  19. ETL Detail: Data Cleansing: data scrubbing, Dirty Data, Lexical Errors, Irregularities, Integrity Constraint Violation, Duplication
  20. Data Duplication Elimination and BSN Method: Record linkage, Merge, purge, Entity reconciliation, List washing and data cleansing
  21. Introduction to Data Quality Management: Intrinsic, Realistic, Orr’s Laws of Data Quality, TQM
  22. DQM: Quantifying Data Quality: Free-of-error, Completeness, Consistency, Ratios
  23. Total DQM: TDQM in a DWH, Data Quality Management Process
  24. Need for Speed: Parallelism: Scalability, Terminology, Parallelization OLTP Vs DSS
  25. Need for Speed: Hardware Techniques: Data Parallelism Concept
  26. Conventional Indexing Techniques: Concept, Goals, Dense Index, Sparse Index
  27. Special Indexing Techniques: Inverted, Bit map, Cluster, Join indexes
  28. Join Techniques: Nested loop, Sort Merge, Hash based join
  29. Data mining (DM): Knowledge Discovery in Databases KDD
  30. Data Mining: CLASSIFICATION, ESTIMATION, PREDICTION, CLUSTERING,
  31. Data Structures, types of Data Mining, Min-Max Distance, One-way, K-Means Clustering
  32. DWH Lifecycle: Data-Driven, Goal-Driven, User-Driven Methodologies
  33. DWH Implementation: Goal Driven Approach
  34. DWH Implementation: Goal Driven Approach
  35. DWH Life Cycle: Pitfalls, Mistakes, Tips
  36. Course Project
  37. Contents of Project Reports
  38. Case Study: Agri-Data Warehouse
  39. Web Warehousing: Drawbacks of traditional web sear ches, web search, Web traffic record: Log files
  40. Web Warehousing: Issues, Time-contiguous Log Entries, Transient Cookies, SSL, session ID Ping-pong, Persistent Cookies
  41. Data Transfer Service (DTS)
  42. Lab Data Set: Multi -Campus University
  43. Extracting Data Using Wizard
  44. Data Profiling