Web 2. 0 is not exactly a new version of the Web, but instead a way to describe a new generation of interactive websites centred on the user. These are websites that offer
Interactive information sharing, as well as collaboration – a case in point being wikis and personal blogs – and is now expanding to other areas as well. These kind of new sites are the result of new technologies and new ideas and are on the cutting edge of Web development. Due to their novel idea, they create a rather interesting challenge for data mining.
Data mining is simply a process of finding patterns in numerous data. There is such a vast plethora of information out there world wide web that it is necessary to use data mining tools to make good sense of it. Traditional data mining techniques are not very effective if used on these new Web 2. 0 sites because the gui is so varied. Since Web 2. 0 sites are created typically by user-supplied content, there is even more data to quarry for valuable information. Having said that, the additional freedom in the formatting ensures that it is much more difficult to sift through the content to find exactly what is usable. The data available is very valuable, so where there is a brand new platform, there must be new techniques developed for mining the data. The trick is that the data mining methods must themselves possibly be flexible as the sites they are targeting are flexible. Inside initial days of the World Wide Web, which was referred to as Web 1 . 0, data mining programs knew where to look for the desired facts. Web 2. 0 sites lack structure, meaning there is no sole spot for the mining program to target. It must be able to diagnostic scan and sift through all of the user-generated content to find what is desired. The upside is that there is a lot more data out there, which implies more and more accurate results if the data can be properly employed. The downside is that with all that data, if the selection set of guidelines are not specific enough, the results will be meaningless. Too much of good is definitely a bad thing. Wikis and blogs have been around so long now that enough research has been carried out to understand them considerably better. This research can now be used, in turn, to devise ideal data mining methods. New algorithms are being developed that will permit data mining applications to analyse this data in addition to return useful. Another problem is that there are many cul-de-sacs over the internet now, where groups of people share information freely, although only behind walls/barriers that keep it away from the genera results.
The main challenge in developing these algorithms doesn’t lie with finding the data, because there is too much of it. The process is filtering out irrelevant data to get to the purposeful one. At this point none of the techniques are perfected. This leads Web 2. 0 data mining an exciting and frustrating arena, and yet another challenge in the never ending series of technological road blocks that have stemmed from the internet. There are numerous problems to overcome. Some may be the inability to rely on keywords, which used to be the best method searching. This does not allow for an understanding of context or sentiment for this keywords which can drastically vary the meaning of the keyword people. Social networking sites are a good example of this, where you can share information having everyone you know, but it is more difficult for that information to proliferate outside of those circles. This is good in terms of protecting privacy, but it does not add to the collective knowledge basic and it can lead to a skewed understanding of public sentiment determined by what social structures you have entry into. Attempts to apply artificial intelligence have been less than successful because it is not thoroughly focused in its methodology. Data mining depends on the collection of information and sorting the results to create reports on the individual metrics that are the focus of interest. The size of the data sets are simply too large for traditional computational techniques to be able to tackle them. Motive a new answer needs to be found. Data mining is an important basic need for managing the backhaul of the internet. As Web 2 . 0. 0 grows exponentially, it is increasingly hard to keep track of whatever is out there and summarize and synthesize it in a practical way. Data mining is necessary for companies to be able to definitely understand what customers like and want so that they can create solutions to meet these needs. In the increasingly aggressive global sector, companies also need the reports resulting from data mining to competitive. If they are unable to keep track of the market and stay abreast associated with popular trends, they will not survive. The solution has to come from open source with options to scale databases depending on needs. You will discover companies that are now working on these ideas and are spreading the results with others to further improve them. So , just as free and collective information sharing of Web 2. 0 designed these new data mining challenges, it will be the connection effort that solves the problems as well.
It is important to view that as a process of constant improvement, not one where an answer will likely be absolute for all time. Since its advent, the internet has changed quite appreciably as well as the way users interact with it. Data mining are invariably a critical part of corporate internet usage and its methods will probably continue to evolve just as the Web and its content does.
There is also a huge incentive for creating better data mining ways to tackle the complexities of Web 2. 0. For this reason, choices exist just for the purpose of analysing and creating solutions to the information mining problem. They find eager buyers for their purposes in companies which are desperate for information on markets and potential clients. The companies in question do not simply want more data, they demand better data. This requires a system that can classify and set data, and then make sense of the results. While the data exploration process is expensive to start with, it is well worth for a retail price company because it provides insight into the market and thus makes it possible for quick decisions. The speed at which a company which has insightful info on the marketplace can react to changes, gives it a huge advantage in the competition. Not only can the company react quickly, it is likely in order to steer itself in the right direction if its information is based on kept up to date data. Advanced data mining will allow companies not only to produce snap decisions, but also to plan long range strategies, while using direction the marketplace is heading. Data mining brings this company closer to its customers. The real winners here, are the corporations that have now discovered that they can make a living by improving the current data mining techniques. They have filled a niche that was solely created recently, which no one could have foreseen and have performed quite a, good job at it.
Olasto 