Introduction to the Web Mining Research Paper

Background

Today World Wide Web has become an increasingly popular platform for storing, retrieving, and disseminating information as a result of the significant and rapid growth of Web data – the knowledge available over the Internet. However, the huge, diverse, dynamic, and unstructured nature of Web data has raised a number of problems for Web data researches and applications – finding relevant information, finding needed information, learning useful knowledge, personalization of information, and social networking.

Although many advanced techniques and algorithms from different research domains, such as data mining, information retrieval and knowledge management, database, and machine learning, etc., have been developed to deal with these problems, there is still a lot of challenges, such as distributed residence, scalability issues, and heterogeneous structure, etc., that has been put forward to Web data researchers by the ongoing evolution of Web (Xu, Zhang, & Li, 2010).

• Web mining was invented by Sir Tim Berners-Lee in 1980 for URL schemes and HTML. He was able to determine it in a useful manner through writing the first server and first browser in 1990 (Peek, 1999).
• In 1993, Marc Andreessen and Eric Bina developed the MOSAIC browser at NCSA (National Center for Super Computing Applications) which played a key role in spreading the web worldwide rapidly (Gilder, 1996).

Description

Web mining consists of multiple tasks comprising of different levels of work, process, activities, information, solutions of different issues, researches in different organizations, gives knowledge to institutions from worldwide issues, and accomplishment of different tasks within a short period of time.

Tree roots are tracked back for the web mining family:

• Classical statistics: This is the longest root that has developed many of the key concepts, such as cluster analysis, standard deviation, regression analysis, standard variance, confidence intervals, and standard distribution. Without statistics, there is no concept of data mining generated. As statistics is the main founder of most of the technologies of web mining. The key concepts developed by this root are the building blocks with which more advanced technologies are used to expand web mining in all over the world.
• Artificial Intelligence: The second major root of Web Mining is artificial intelligence (Boullosa & Xexéo, 2002). This technique is built to solve the statistical problems arising during the work or process in large organizations and firms. This approach requires vast computer processing power.
• Machine Learning: The third root of working through web mining is machine learning, which is more accurately defined by statistical terminologies. Machine learning operates a lot of data, to let computers learn programs to solve data, use fundamental statistical concepts and more efficient artificial learning to achieve the desired goals (Perner & Imiya, 2005).

Applications

Web mining is applied to the whole world in different fields. Different fields use it to let their work easier. Organizations are using it for more improvement in their present status, while different institutions use it as a knowledge gainer because, through web mining, we travel the whole world from sitting at one place. Students get much more advantages and acknowledge the future world through the use of web mining.

Following are some of the major applications of web mining:

• Applications in E-Commerce and E-Services: Applications of web mining in E-commerce and E-services are the new research directions in the area of web mining. Web mining techniques can play a vital role in e-commerce and e-services.
• Applications in international organizations: Different organizations use web mining to modify their processes, their way of working, their time management, and their relations with other international organizations. Web engines let them keep in touch with all the activities, with all the newly approved rules and regulations changed in the whole world.
• Applications in the banking sector: The banking sector also enjoys numerous advantages of web mining. Most of the work or mathematical calculations of the banking industry are done through web mining. Web mining gives a vast vision to reduce their problems and to link in with the banks of the international level. Because of the use of web engines, banks are resolving most of their high committed issues within no time.
• Applications in the fashion industry: Web mining is also applicable in the fashion industry now a day. The fashion industry has achieved a vast area of business in Pakistan and most fashion designers use web mining techniques to improve their work. They although know that what is in, what is out in fashion in the UK as well as in other countries through the web mining usage.
• Applications in institutions: In many small and big institutions, the study of web mining is delivered to the students such that the new generations must be aware of web mining, its techniques, and its use in different fields. At present, there would be nothing possible without the usage of web mining.

Successful Projects

The successful project under the web mining system is WENCO. As defined on the Wenco site, “Mine management systems focus on monitoring and controlling a mine’s mobile assets to ensure effective utilization of all departments including operations, maintenance, engineering, resource planning, management and financial reporting. Mine management systems have become a necessity in modern mining and a key ingredient to a successful mine” (Wenco Mine, 2010).

WENCO International Mining System Limited has secured the contract for the installation and supply of Wenco’s Fleet Management System (FMS) to four of INDIA Limited’s (CIL) mines (Wenco Mine, 2010).

Webco is generally also applicable in universities and research centers to solve their academic and regular issues. It has a vast and expanded area that covers all the problems arising and solutions to these problems in an organization. It also helps to analyze what is running in national and international markets, so we have stepped forward according to this specific market area. However, Webco designs a framework so that due to this framework, all the countries run their economies in sharp and reliable ways that they also find some of their weaknesses and move according to the principles given to them by WEBCO ENGINE.

Current Potential Technologies & Future Development

There are some of the current potential technologies given below.

• Web-usage mining: Web usage mining has been used and defined as database mining techniques for large data tasks in order to extract useful patterns. From a deployment perspective, there are two application keys of data usage, first to derive data through web mining and second is to apply in user’s web data in order to identify the correct work done under web mining.
• Web measurement: Web measurement comprises the use of web mining in order to understand the value that the web channel is generated for the business. This includes the measurement of the success of various marketing efforts and promotions.
• Generation of knowledge: During the process of web mining, knowledge must be generated by classical techniques and use statistical data. The aim here is to understand visitors’ behavior with the aim of servicing them better whether by using its techniques or measuring its calculation.
• A model of Online Customer Interaction: From the usage of web mining perspective, the interactions of common people can be viewed in the process of web mining. This concept may be related through the specific domains in which different people give their different points of view about the re-development of their respective fields.
• Scenario development: In this section, we propose a new approach to deploying web mining for web measurement and development. This section is based on customer interaction scenarios and the monitoring of customer behaviors against these scenarios.
• Provide a base for flexible personalization: The aim of this section is to highlight architectural issues associated with the deployment of web mining. This section gives a platform of knowledge of the results of web mining and their applicability in different fields. This is the key to developing such a foundation is the scalability of the system as delivering personalized content at the cost of delivery content.

Future Challenges

Web mining is yet failed to have an impact on the industry due to the failings of the deployment stage of web mining projects and hence the delivery of a return on investment in the technology. We discussed the two key applications in web usage mining to which a healthy result can be achieved by us within a short period of time.

• To improve web mining, one has to use the web techniques following the correct and useful path. A waste of it will also waste our entire working efficiency and this will cause collapse effectiveness of the knowledge we gained from it. Better usage gives better results; more knowledge gives more suggestions about the field in which it has been used.
• The second main key is the relation and behavior of customers. It is very important to us that we must keep in mind during the web mining process what should a customer want and need from us. Another thing is that the relations of customers are peacefully handled inside the field as well as outside the field.

The following three graphs should be kept in mind during usage of web mining;

• Single Node: The study of a single node is useful which defines behavior across different time periods.
• Sub Graphs: Set of nodes that generate sub-graphs structures that generate web structures graphs. The sequences of subgraphs required different techniques.
• Whole Graph: The variation of the whole graph comprises the properties such as size, order, nature, word length. They all are useful for web mining usage.

Conclusion

Hence web mining generates a new world in it, which is about the marvelous authentications in all aspects of life. We can’t deny any of its tremendous performance that takes this world to the stage where one can easily find the solutions to our small as well as big problems. It acknowledges to a great extent. Students also get numerous advantages from web mining as they can search any of their academic tasks and topic related to their departments.

Web mining is quite beneficial for the banking sectors including the large national and international banking industries. Web mining brings all the national and international banking industries on one common platform where all the banks discuss their major issues and problems and find reliable solutions to their problems by mutual understanding.

The Internet has brought many positive changes in our lives. Along with the effectiveness of the Internet, the concept of web mining has also become popular. Several descriptions can easily be found in detail that addresses the people and gives more and more knowledge about web mining and guide the people the true usage of it. Web Mining helps understand the core working prospectus and guiding principles to work.

Consequently, some disasters like earthquakes, floods, and volcanoes can be fore-casted up to a certain extent, and effective disasters planning can be enhanced. It also encourages and aims at providing assistants to building a common data warehouse which gives better opportunities to record the data, data manipulation, data sharing, data standardization and data analysis between different societies, communities and institutions.

Future directions of data mining include the improvement of the quality and delivery of data and analysis of data.

References

Boullosa, J.R., & Xexeo, G. (2002). An architecture for web usage mining. Web.

Carey, B., Grusy, E., Marjaniemi, C., & Sautter, D. (1998). A Perspective on Data Mining. CDI

Gilder, G. (1996). The Coming Software Shift. Web.

Peek, R. (1999). Internet Technology. Wiley Encyclopedia of Electrical and Electronics Engineering.

Perner, P. & Imiya, A., (2005). Machine learning and data mining in pattern recognition. New York: Springer.

Wenco Mine (2010). Wenco. Web.

Xu, G., Zhang, Y., & Li L. (2010). Web mining and social networking: Techniques and applications. New York: Springer.