Improving Web Recommendations Using Web Usage Mining and Web Semantics

This project addresses the topic of improving web recommendations. With the immense increase in the number of websites and web pages on the internet, the issue of suggesting users with the web pages in the area of their interest needs to be addressed as best as possible. Various approaches have been proposed over the years by many researchers and each of them has taken the solution of creating personalized web recommendations a step ahead. Yet, owing to the large possibilities of further improvement, the system proposed in this report takes generating web recommendations one more step ahead. The proposed system uses the information from web usage mining, web semantics and time spent on web pages to improve the recommendations

Building the Multi-layer Theory of Association Semantic based on the Power-law Distribution of Linking Keywords

Abstract-Web information contain plentiful, significant knowledge which is eager to be explored by users. Effective semantic layered technology not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of the related information system. This paper builds the multi-layer theory of association semantic based on the power-law distribution of linking keywords. First, some experiments of four types of keywords with different linking role are done to discover the possible distribution law. Experiment results show that four types of keywords are all reveal power-law distribution. Then, based on the discovered power-law distribution, the multi-layer theory of association semantic is built. The multi-layer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle size on Association Link Network (ALN). Keywords-Association Link Network, power-law distribution, multi-layer theory of association semantic, knowledge discovery in Web resources

Izgradnja podatkovne mreže senzora u Internetu stvari

In the Internet of Things, wireless sensor networks (WSN) is in charge of gathering and transferring environment data. It is an essential work to mine data semantic in WSN in the data derived from sensors to improve the WSN. This paper proposes the Data Association Network of sensors (DAN) to organize the mined association semantic relations among sensors into an effective form. Because DAN holds the rich data semantic of WSN, it can improve WSN in some aspects, such as detecting the abnormal sensors, simulating the data of faulty sensors, or optimizing the topology of WSN. Experimental results show that the proposed method can mine the associated relations among sensor nodes effectively, and the DAN is helpful in solving some problems of WSN.Govoreći o Internetu stvari, bežična mreža senzora (WSN) ima ulogu prikupljanja i slanja podataka o okolini. Osnovni je zadatak analizirati semantiku podataka u WSN-u u podacima dobivenim sa senzora u svrhu unaprije.enja bežične mreže senzora. U ovom radu predloženo je mrežno udruženje podataka (DAN) sa senzora u svrhu organiziranja analiziranih udruženja semantičkih relacija izme.u senzora u djelotvorne forme. S obzirom da DAN sadrži dosta semantičkih podataka s WSN-a, može unaprijediti WSN u odre.enim aspektima kao npr. detekcija neispravnih senzora, simuliranje podataka sa senzora u kvaru ili optimiziranje topologije WSN-a. Eksperimentalni rezultati pokazuju da predložena metoda može efektivno analizirati udružene relacije izme.u senzorskih čvorova te da je DAN korisno u rješavanju određenih problema WSN-a