ANALYSIS OF RESEARCH ISSUES IN WEB DATA MINING

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 2, Issue 3 (May-June 2014), PP. 18-24
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ANALYSIS OF RESEARCH ISSUES IN WEB
DATA MINING
Ch.Dhanunjaya Rao1
, Prof.Dr.G.Manoj Someswar 2
1
Research Scholar, Bharat University, Chennai, Tamilnadu, India.
2
Professor, Bharat University, Chennai, Tamilnadu, India.
Abstract: In this Research paper, we present an overview of
research issues in web mining. We discuss mining with respect to
web data referred here as web data mining. In particular, our
focus is on web data mining research in context of our web
warehousing project.We have categorized web data mining into
three areas; web content mining, web structure mining and web
usage mining. We have highlighted and discussed various
research issues involved in each of these web data mining
category. We believe that web data mining will be the topic of
exploratory research in near future.
Key words— web structure mining, web content mining, web
usage mining, warehouse concept mart, Web query, Web bags.
I. INTRODUCTION
The advent of the World Wide Web has caused a dramatic
increase in the usage of the Internet. The World Wide Web is a
broadcast medium where a wide range of information can be
obtained at a low cost.Information on the WWW is important
not only to individual users, but also to the business
organizations especially when the critical decision-making is
concerned. Most users obtain WWW information using a
combination of search engines and browser, however, these
two types of retrieval mechanisms do not necessarily address
all of a user’s information needs.
This is particularly true in the case of business
organizations that currently lack suitable tools to systematically
harness strategic information from the web and analyze these
data to discover useful knowledge to support decision making.
A recent study provides a comprehensive and comparative
evaluation of the most popular search engines [1]. A more
recent survey of web query processing has appeared in [23].
The resulting growth in on-line information combined with
the almost unstructured web data necessitates the development
of powerful yet computationally efficient web data mining
tools. Web data mining can be defined as the discovery and
analysis of useful information from the WWW data. Web
involves three types of data; data on the WWW, the web log
data regarding the users who browsed the web pages and the
web structure data. Thus, the WWW data mining should focus
on three issues; web structure mining, web content mining [8]
and web usage mining [2,10,13]. Web structure mining
involves mining the web document’s structures and links. In
[24], some insight is given on mining structural information on
the web. Our initial study [5] has shown that web structure
mining is very useful in generating information such visible
web documents, luminous web documents and luminous paths;
a path common to most of the results returned. In this paper, we
have discussed some applications in web data mining and E-
commerce where we can use these types of knowledge. Web
content mining describes the automatic search of information
resources available on-line. Web usage mining includes the
data from server access logs, user registration or profiles, user
sessions or transactions etc. A survey of some of the emerging
tools and techniques for web usage mining is given in [2]. In
our discussion here, we focus on the research issues in web
data mining with respect to the web warehousing project called
Warehouse of Web Data.
The key objective of this research paper is to design and
implement a web warehouse that materializes and manages
useful information from the web to support strategic decision
making. We are building a web warehouse [7] using the
database approach of managing a web warehouse containing
strategic information coupled from the web that may also inter-
operate with conventional data warehouses. One of the
important areas of our work involves the development of
techniques for mining useful information from the web. We
would be integrating this web warehousen with intelligent tools
for information retrieval and extend the data mining techniques
to provide a higher level of data organization for unstructured
data available on
the web. With respect to our web data mining approach, we
argue that extracting information from a very small subset of
all HTML web pages is also an instance of web data mining. In
web warehousing, we focus on mining a subset of web pages
stored in one or more web tables because we believe that due to
the complexity and vastness of the web, mining information
from a subset of web stored in the web tables is more feasible
option. Our web warehousing approach allows us to do this
effectively as we materialize only the results returned in
response to a user's query graph.
II. WEB WAREHOUSING
In WEB WAREHOUSING, we introduced our web data
model. It consists of a hierarchy of web objects. The
fundamental objects are Nodes and Links, where nodes
correspond to HTML text documents and links 3 correspond to
hyper-links interconnecting the documents in the WWW. These
objects consist of a set of attributes as follows: Nodes = [url,
title, format, size, date, text] and link = [source-url, target-url,
label, link-type]. In our web warehouse, Web Information
Coupling System (WICS) [9] is a database system for
managing and manipulating coupled information extracted
from the Web. We have defined a set of coupling operators to
manipulate the web tables and correlate additional useful and
related information [9].
We materialize web data as web tuples stored in web tables.
Web tuples, representing directed connecting graphs, are
comprised of web objects (Nodes and Links). We associate
with each web table a web schema that binds a set of web
tuples in a web table. A web schema contains the meta-data
that binds a set of web tuples to a web table in the form of
connectivities and predicates defined on node and link
variables. Connectivities represent structural properties of web

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tuples by describing possible paths between node variables.
Predicates on the other hand specify the additional conditions
that must be satisfied by each tuple to be included in the web
table. In WICS, a user expresses a web query in the form of a
query graph consisting of some nodes and links representing
web documents and hyperlinks in those documents,
respectively. Each of these nodes and links can have some
keywords imposed on them to represent those web documents
that contain the given keywords in the documents and/or
hyperlinks.
When the query graph is posted over the WWW, a set of
web tuples each satisfying the query graph are harnessed from
the WWW. Thus, the web schema of a table resembles the
query graph used to derive the web tuples stored in web table.
Note that the results are returned as web tuples. Note that some
nodes and links in the query graph may not have keywords
imposed. They are called unbound nodes and
links,respectively.
Consider a query to find all data mining related publications
by the computer science faculty at Stanford University, starting
with the web page
http://www.cs.stanford.edu/people/faculty.html.
The query above may be expressed as follows :
AI or database epublications
http://www.cs.stanford.edu/people/faculty.html data mining
x y z [4].
The above query graph is assigned as schema to the web
table generated in response the above query. The schema
corresponding to the above query graph can be formally
expressed as <Xn, Xl, C, P> where Xn is the set node
variables; x,y,z in the example above, Xl is the set of link
variables; - (unbound link) and e in the example, C is set of
connectivities ; k1 L k2 where k1 = x<->y, k2 = y<e>z and P is
a set of predicates as follows : p1 L p2 L p3 L p4 such that p1
(x) = [x.url EQUALS
http://www.cs.standford.edu/people/faculty.html], p2 (e) =
[e.label CONTAINS "publications"], p3 (y) = [y.text contains
"AI or database"], p4 (z) = [z.text CONTAINS "data mining"].
The query returns all web tuples satisfying the web schema
given above. These web tuples contain the faculty page, the
faculty member’s page that should contain the word such "AI
or database" and the respective publications page if it contains
the word "data mining". Thus, many instances of the query
graph shown above will be returned as web tuples. We show
one of the instance of the above query graph below:
Widom active database Researchpublications
http://www.cs.stanford.edu/people/faculty.html web data
mining
III. WEB STRUCTURE MINING
Web information retrieval tools make use of only the text
on pages, ignoring valuable information contained in links.
Web structure mining aims to generate structural summary
about web sites and web pages. The focus of structure mining
is therefore on link information, which is an important aspect of
web data. Given a collection of interconnected web documents,
interesting and informative facts describing their connectivity
in the web subset can be discovered. We are interested in
generating the following structural information from the web
tuples stored in the web tables.
Measuring the frequency of the local links in the web tuples
in a web table. Local links connect the different web documents
residing in the same server. This informs about the web tuples
(connected documents) in the web table that have more
information about inter-related documents existing at the same
server. This also measures the completeness of the web sites in
a sense that most of the closely related information are
available at the same site. For example, an airline’s home page
will have more local links connecting the “routing information
with air-fares and schedules” than external links.
Note that our web warehouse is populated using a query
graph initiated by the user. If the results returned (i..e, web
tuples) are having more local links then we can know that the
query basically crawl some particular web sites locally. In such
a case, for next execution of such a query, one can optimize the
query graph to start crawling with those sites directly. This
case, for example, may arise when the web crawler passes
through many unbound nodes and links, but actual web pages
are then found at single site.
Note that in many cases the information requested may be
found at many different servers across the network. Identifying
local links depicts the fact that integrated information is also
available at some particular web site, but may be in different
files. Thus, such an information can reduce the query execution
time over the internet.
· Measuring the frequency of web tuples in a web table
containing links which are interior; links which are within the
same document. This measures a web document’s ability to
cross-reference other related web pages within the same
document. This also measures the flow of the web documents.
For example, a news-paper should always refer to other news
items locally (within the same news-paper). This information
depicts that the relevant information is available within the
same file. Measuring the frequency of web tuples in a web
table that contains links that are global; links which span
different web sites. This measures the visibility of the web
documents and ability to relate similar or related documents
across different sites. For example, research documents related
to “semistructured data” will be available at many sites and
such sites should be visible to other related sites by providing
cross references by the popular phrases such as “more related
links”. Also, in case of a document like a research paper, it
should have more external links as it should refer to other
related papers. This expresses a research paper's ability to
cross-reference other related work.
Measuring the frequency of identical web tuples that
appear in a web table or among the web tables. This measures
the replication of web documents across the web warehouse
and may help in identifying, for example, the mirrored sites.
This information concludes that some web pages provide
integrated information on various topics. We have also used
duplicate web tuples to identify, for example, visible web pages
etc. We discuss this issue in next subsection. 6 On average, we

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may need to find how many web tuples are returned in
response to a query on some
popular phrases such as “Bio-science” with respect to
queries containing keywords like “ earth-science” .This can
give an estimation of the results returned in response to some
popular queries. This also gives an indication whether we
should store the results of such a query in our warehouse for
further reference.
Another interesting issue is to discover the nature of the
hierarchy or network of hyperlinks in the web sites of a
particular domain. For example, with respect URLs with
domains like .edu, one would like to know how most of the
web sites are designed with respect to information flow in
educational institutes. What is the flow of the information they
provide and how are they related conceptually. Is it possible to
extract a conceptual hierarchical information for designing web
sites of a particular domain. This may help in generalizing the
flow of information in web sites representing information in
some particular domain. This will help for example in building
a common web schema or wrappers for educational institutes.
Thus it can make query processing easier.
What is the in-degree and out-degree of each node (web
document)? What is the meaning of high and low in- and out-
degrees? For example, a high in-degree may be a sign of a very
popular web site or document. Similarly, a high out-degree
may be a sign of luminous web site. Out-degree also measures
a site's connectivity. We discuss these issues in next sub-
section. If a web page is directly linked to another web page or
are near to each other then we would like to
discover the relationships among those web pages. These
relationships might be of the following
types. The two web pages might be related by synonyms or
ontology or having similar topics, both the web pages are in the
same server and in that case both the pages may be authored by
the same person.
While the above information is discovered at the inter-
document level, web structure mining can also have another
direction - discovering the structure of web documents
themselves. Web document structure mining can be used to
reveal the structure (schema) of web pages. While this would
be useful for navigational purpose and several other operations
such comparing and integrating web page schemes can be
made possible. This type of structure mining would facilitate
web document classification and clustering on the basis of
structure. It will also contribute towards introducing database
techniques for accessing information in web pages by
providing a reference schema. The availability of semantic
markup 7 language XML [14] has made it possible to identify
tree structure within such documents. These structures can be
compared using n-array tree matching algorithms. Schema
integration can be carried out by
generating representative structures such as centroid and
spanning trees. Related work on schema
discovery of semi-structured documents includes [15,16]
which uses the Object Exchange Model (OEM) devised in
LORE [18] and is similar to approach of using representative
objects in [19]. Another work [17] uses OEM but derives a type
hierarchy using measures similar to support and confidence
encountered earlier, to represent the inherent structure of large
collections of semi-structured data.
IV. WEB BAGS
Most of the search engines fail to handle the following
knowledge discovery goals: From the query’s result returned
by search engines, a user may wish to locate the most visible
web sites [6] or documents for reference. That is, many paths
(high fan in) can reach that sites or documents. Presently, he
may only do so manually by visiting the documents in the
query result and then manually follow each links in the web
documents and then download the visible documents as files on
user’s hard disk for future reference. Nevertheless, this method
is tedious.
Reversing the concept of visibility, a user may wish to
locate the most luminous web sites [6] or documents for
reference. That is, web sites or documents which have the most
number of outgoing links. Currently, he may locate this
information by manually visiting each web documents.
Furthermore, a user may wish to find out the most traversed
path for a particular query result. This is important since it
helps the user to identify the set of most popular interlinked
web documents that have been traversed frequently to obtain
the query result.
Presently, he may only do so by visiting each document in
the search result and compare their link information. This
method is time consuming. We have defined a concept of a
web bag in [5] and used web bags for the types of the
knowledge discovery discussed above. Informally, a web bag is
a web table containing multiple occurrences of identical web
tuples. Note that a web tuple is a set of inter-linked documents
retrieved from the WWW that satisfies a query graph. A web
bag may only be created by projecting some of the nodes from
web tuples of a web table using the web project operator. A
web project operator is used to isolate the data of interest,
allowing subsequent queries to run over a smaller, perhaps
more structured web data. Unlike its relational counterpart, a
web project operator does not eliminate identical web tuples
autonomously. Thus, the projected web table may contain
identical web tuples (i.e., a web bag). The duplicate elimination
[8] process is initiated explicitly by a user. Autonomous
duplicate elimination may hinder the possibility of discovering
useful knowledge from a web table. This is due to the fact that
such knowledge may only be discovered from web bags.
Using web bags, we discover visible web documents,
luminous web documents and luminous paths [5]. Below we
define the three types of knowledge. Then we discuss the
applications of three types of knowledge, which we are
currently working.
V. VISIBILITY OF WEB DOCUMENTS :
Visibility of web documents D in a web table W measures
the number of

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different web documents in W that have links to D. We call
such documents visible since they are visible in the web table
as they are linked by large number of distinct nodes. The
significance of a visible node D is that the document D is
relatively more important compared to other documents or
nodes in W for the given query. In a web table, each node
variable may have a set of visible nodes. All of these may not
be useful to the user. Thus, we explicitly specify a threshold
value to control the search for visible nodes. The visibility
threshold indicates that there should exist at least some
reasonably substantial evidence of the visibility of instances of
the specified node variable in the web table to warrant the
presentation of visible nodes.
A. Applications :
Consider a query graph involving some keywords such as "
types of restaurants" and
"items" given below, where dotted lines implies unbound
node and link. We assume that such a site is there on WWW
which provides a list of types of restaurants (i.e., Italian, Asian,
etc.) which further have names of those restaurants. We also
assume that there is a web site which provides list of items for
all types of restaurants:
www.test.com items a restaurtants
The results returned in response to the query graph
imposing such predicates in our web warehouse system will
return the instances of restaurants selling different items. For
example, the three web tuples corresponding to the query graph
are as given below.
www.test.com Pizza Italian Restaurtants Milano-R x z X1
Z1
www.test.com Pizza European Restaurtants Paris-R
www.test.com Pesta Italian Restaurtants Milano-R
From the results returned, we can find the most visible web
pages by providing very high visibility threshold [5]. Assume
that this gives Z1 as the most visible web page (having more
incoming links from different URLs) which has details about
pizza. This can give an estimate about the different restaurants
which sell pizzas. By lowering the visibility-threshold, we can
get another set of visible web pages, and assume that this time
we get the set as {Z1, Z2} where Z2 is an instance of a
webpage which provides details of Pasta. Note that it is
possible that some restaurants can sell both pizza and pasta. By
comparing the set of different URLs corresponding to the
restaurants, we can derive the association rules such "out of
80% of restaurants which offer pizza to their customers, 40%
also provide pasta. Further, we can cluster (group) these
restaurants according to type and can generate rules like out of
80% of restaurants which sell pizza, 40% which sell pasta also
are of Italian types.
Consider another example where a new business venture
wants to do some analysis of their web sites which display
products for buying. By finding the visibility of its web site
with respect to other web sites selling such (or related)
products, the company can find ways to redesign (including
changes in product’s price etc.) its web site to improve
visibility. For example, if a web site sells PC monitors, they
must be providing links to web sites which sell CPU. Thus, if a
web site finds that its visibility is lower in comparison to other
web sites selling CPUs then the web site needs to improve in
terms of design, products, etc.
VI. LUMINOSITY OF WEB DOCUMENTS :
Reversing the concepts of visibility , luminosity of a web
document D in a web table W measures the number of
outgoing links, i..e, the number of other distinct web documents
in W that are linked from D. Similar to the determination of
visible nodes, we explicitly specify the node variable y based
on which luminous nodes are to be discovered and the
luminosity threshold.
A. Applications :
One can use luminosity of a web site, displaying a
particular or a set of products, to
identify the companies that make all those products. This
will given an estimate of the type that a
X1 Z1 X1 Z2 10 company whenever it makes a product
"A" also makes a set of products "B and C". Note that a
company can make a product B and/or C only with out
necessarily making a product A or it may be possible that a
certain percentage of companies demonstrate such rules. We
want to generate association rules such as X% of all the electric
companies which makes a product "A", Y% of them also
makes a set of other products "B and C" (support). Also, we
can generate a rule like whenever a company makes a product,
it also makes certain other products, for example, X% of
companies which make a product A may also make a product B
and C (confidence). Such rules help a new electric company in
taking a decision such as the set of products the company
should start manufacturing together.
Consider the following web tuples in a web table.
www.eleccompany.com www.elecproduct.org/productA
company A product A Product A
www.eleccompany.com www.elecproduct.org/productB
company A product B Product B
www.eleccompany.com www.elecproduct.org/productC
company A product C
www.eleccompany.com www.elecproduct.org/productB
company B product B Product B
www.eleccompany.com www.elecproduct.org/productA
company C product A product A
Note that in above example, certain companies (20%) if
they make a product A also make products B and C. However,
the company C makes only the product A. That is, 40% of
companies which make a product A , 20% of them also make
products B and C.
VII. LUMINOUS PATHS:
A web query result in Web warehousing system is set of
inter-linked web tuples materialized in the form of web table.

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Luminous paths in a web table is set of inter-linked nodes
(paths) which occurs some number of times across tuples in the
web table. That is, occurrences of this set of inter-linked nodes
is X1 Z1 X1 Z2 X1 Z3 X1 Z2 X1 Z2 [11] high compared to the
total number of web tuples in the web table. An implication is
that in order to couple the query results from the WWW, most
of the web tuples in the web table has to traverse the luminous
paths.
A. Applications :
Luminous paths can be used to optimize the visualization of
query results. Once the results are returned, one needs to
browse the nodes (web pages) in the set of luminous paths only
once. For example, it may be possible that between two web
pages there may exists two paths such that one is a subset of
another. In that case, common paths (web pages) need to
browse only once. Another interesting application is to find
whether two given queries are similar. Consider that two web
tables T1 and T2 corresponding to two query graphs Q1 and
Q2. If we find that sets of luminious paths in the two web
tables have common sets of luminous paths or sub-paths then
we can infer that the corresponding query graphs are similar.
We would also like to find the similar relationships; that is,
whether, they are conceptually related or the keywords present
in two web pages are synonyms to each other, or they are
topically related.
VIII. WEB CONTENT MINING
Web content mining involves mining web data contents.
The open question is what does it mean to mine content from
the web? In effect web content mining is the analog of data
mining techniques for relational databases since we can expect
to find similar types of knowledge from unstructured data
residing in web documents. The unstructured nature of web
data forces a different approach towards web content mining.
The web contains a mix of many different data types such as
textual data, image data, audio and video, etc. In Web
warehousing system, currently we primarily focus on mining
useful information from the web hypertext data. In particular,
we consider the following issues of web content mining in the
web warehouse context:
Similarity and difference between web content mining in
web warehouse context and conventional data mining. In
relational database, the data are flat are very well arranged in a
tabular structure defined using attributes whose domains are
known. In case of web data, documents are totally unstructured
and different attributes in documents may have semantically
similar meaning across WWW or vice versa. For example, one
web site could display the price of same car in numeric figure
others may do in words. An attribute may have an atomic value
in one document, but a set of values in other documents. In
order to do content mining, one must first resolve the problems
of semantic integration across web documents[12]. Selection
of type of data in the WWW to do web content mining. Web
content mining needs to select useful information before
analysis. It is not practical to expect data mining system to
search the entire
WWW to discover knowledge requested by the user. In our
case, we mine based on the meta-data available. Even if
scalability argument is ignored, large amount of redundant ,
uninteresting pieces of information may be returned. The user
must therefore be provided the facility to identify a subset of
the web, which pertains to the domain of the knowledge
discovery task. Then, depending on the specific kind of
knowledge to be mined another level of data selection must be
carried out to extract relevant data into a suitable representative
model. Cleaning of selected data to mine effectively. This is
the step after the web data is selected for mining.
Before mining, one may need to transform the data into
some data model, which is well understood. For example, in
our case, we transform the web data in the form of web tuples
consisting of nodes and links and having keywords specified
over them. We also use web algebraic operators to filter out the
irrelevant information.
Types of knowledge that can be discovered in a web
warehouse context. The types of knowledge to be discovered
are as follows: generalized relation, characteristic rule,
discriminate rule, classification rule, association rule, and
deviation rule [11]. Do these data mining techniques applicable
to web data mining and if yes, how? For example, we are
interested in generating the following types of rules: 80% of
web tuples (i..e, web pages) in response to a “ travel
information query from Hong Kong to Macau” suggest that
popular means of traveling is by ferry.
Discovery of types of information hidden in a web
warehouse which are useful for decision making. Web data
sources being heterogeneous , diverse and unstructured, are
difficult to categorize. In many cases, the user would be even
more unsure about the knowledge hidden beneath the contents
of a document than that in a database. An interactive and
iterative process is therefore necessary to enable exploratory
data mining. A suitable data mining query language is one of
the means to materliaze such a user-mediated process. In our
Web warehousing project, we are building a query language
which support mining.
To perform interactive web content mining. Presentation of
discovered knowledge to the users to expedite complex
decision making. For example, a query interface is often
necessary to specify the interesting set of data to be studied, the
kind of rules to be discovered, etc. A graphical user interface is
[13] helpful for interactive mining of multiple-level rules [12]
because it facilitates interactive modification of the threshold
values, warehouse concept mart (discussed later), concept
levels, output styles and formats. Role of WICM (web
information coupling model [9]) aid in web content mining. We
have build a coupling system, which brings the results from the
web using a query. WICM plays an important role is
populating the warehouse as our web data mining is restricted
to the results returned in response to a query. Therefore, web
coupling plays an important role in selecting of data.
IX. WEB USAGE MINING
Web usage mining is the discovery of user access patterns
from web server logs, which maintain an account of each user

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browsing activities. Web servers automatically generate large
data stored in sever referred as logs containing information
about the user profile, access pattern for pages, etc. This can
provide information that can be used for efficient and effective
web site management and the user behavior. Apart from
finding paths traversed frequently by users as a series of URLs,
associations indicate which sites are likely to be visited
together can also be derived. One particular approach
mentioned in [11] using "maximal forward references"
obtained by filtering out "backward references" from traversal
subsequences in log data to extract frequently occurring
consecutive subsequences. This leads to "maximal reference
sequence" which are those frequent subsequences that are not
subset of others. The problem is similar to order version of
finding large itemsets in transaction databases [20,21]
discussed under mining of association rules. An improvement
over maximal forward references which considers backward
references is discussed in [22] and uses a transaction model for
data extracted from server access logs to discover sequential
patterns. This approach combines all the entries for a user in a
server log into a single transaction using clustering. Association
rules and sequential patterns can then be mined from the
grouped transactions.
In Web warehousing system, the user initiates a coupling
framework to collect related information. For example, a user
may be interested in coupling a query graph “ to find the hotel
information” with the query graph “ to find the places of
interest” . From this query graph, we can generate some user
access pattern of coupling framework. We can generate a rule
like “ 50% of users who query “ hotel” also couple their query
with “ places of interest” . This information can be used in the
warehouse in local coupling; coupling of materialized web
tables containing information on hotels with places of interests.
Another information that [14] can be of interest is to find
coupled concepts from the coupling framework. This can be
used in organizing web sites. For example, web documents that
provide information on “ hotels” should also have hyperlinks to
web pages providing information on “ places of interest” .
These coupled concepts can also be used to design the
Warehousing Concept Mart (WCM).
X. WAREHOUSE CONCEPT MART
Knowledge discovery in web data becomes more and more
complex due to the large number of data on WWW. We are
building the concept hierarchies involving web data to use them
in knowledge discovery. We call such collection of concept
hierarchies a Warehouse Concept Mart (WCM). The concept
mart is build by extracting and generalizing terms from web
documents to represent classification knowledge of a given
class hierarchy. For unclassified words, they can be clustered
based on their common properties. Once the clusters are
decided, the keywords can be labeled with their corresponding
clusters, and common features of the terms are summarized to
form the concept description. We can associate a weight at each
level of concept marts to evaluate the importance of a term with
respect to the concept level in the concept hierarchy. The
concept marts can be used for the following:
1. Intelligent answering of web queries
Knowledge discovery using the warehouse concept mart
facilitates querying web data and intelligent query answering in
web warehousing system. A user can supply the threshold for a
given key word in the warehouse concept mart and the words
with the threshold above the given value can be taken into
consideration when answering the query. The query can also be
answered using different levels of concept in the warehouse
concept mart [3] or can provide approximate answers [4].
Another interesting idea is to provide the user some knowledge
in framing the global coupling query graph. For example, If a
user frame a query graph to find some information about
“Database system” , he can be supplied some related concepts
like “ORACLE” in case he would like to pose a query by
coupling “Database system” with “ORACLE”.
2. Web Data Mining and Concept Mart
Warehouse Concept Mart (WCM) can be used for web data
or content mining. In web content mining, we make use of the
warehouse concept mart in generating some of the useful
knowledge. We are mining [15] association rules techniques to
mine the association between words appearing in the concept
mart at various levels and in the web tuples returned as the
result of a query. Mining knowledge at multiple levels may
help WWW users to find some interesting rules that are
difficult to be discovered otherwise. A knowledge discovery
process may climb up and step down to different concepts in
the warehouse concept mart’ s level with user’ s interactions
and instructions including different threshold values. Another
application of the warehouse concept mart is in web structure
mining. For example, as mentioned before, we can capture the
flow of web sites of particular domain and based on that, we
may be able to generalize the structure of web sites of
particular domain.
XI. CONCLUSIONS
In this research paper, we have discussed some web data
mining research issues in context of the web warehousing
project called Warehouse of Web Data. We have defined three
types of web data mining. In particular, we discussed web data
mining with respect to web structure, web content and web
usage. An important part of our warehousing project is to
design the tools and techniques for web data mining to generate
some useful knowledge from the WWW data. Currently we are
further exploring the ideas discussed in this research paper.
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