ANALYZING WEB LOGS OF AN ASTROLOGICAL WEBSITE USING KEY INFLUENCERS

With the growth of Internet, websites have become a dynamic tool in the market for every business to both acquire and service their customers. Online presence through websites has given them a global and wider reach. Good design and well management are the two key aspects of the effective website to catch the attention of visitors. Nowadays, the analysis and behavior of website visitors can be used to convert them into customers and serve them in a better way. This can be carried out on the weblogs which are generated as a result of user’s access to a website. This paper aims to study the log files of an astrological website. An astrological website has not gained much attention in the internet community as people prefer going to the astrologers in person to resolve their problems. The log files of an astrology website were taken, preprocessed and analyzed using Analyze Key Influencers technique a feature of Microsoft SQL Server Data Mining Add-ins for Microsoft Office 2007. The information obtained can be used to enhance the effectiveness of the website

Sequential Pattern Mining

Tato diplomová práce je zaměřena na problematiku získávání znalostí z databází, především pak na metody dolování sekvenčních vzorů. Jednotlivé metody dolování sekvenčních vzorů jsou zde popsány detailně. Dále se práce zabývá rozšířením analytických služeb platformy Microsoft SQL Server o nové dolovací algoritmy. V praktické části této práce jsou implementovány rozšíření pro dolování sekvenčních vzorů na platformě MS SQL Server. V poslední části jsou vytvořené algoritmy porovnány nad různými datovými sadami. This master's thesis is focused on knowledge discovery from databases, especially on methods of mining sequential patterns. Individual methods of mining sequential patterns are described in detail. Further, this work deals with extending the platform Microsoft SQL Server Analysis Services of new mining algorithms. In the practical part of this thesis, plugins for mining sequential patterns are implemented into MS SQL Server. In the last part, these algorithms are compared on different data sets.