LEADER 03836nam 22006735 450 001 9910746093603321 005 20230626102200.0 010 $a3-031-32503-6 024 7 $a10.1007/978-3-031-32503-8 035 $a(CKB)27240517300041 035 $a(MiAaPQ)EBC30609721 035 $a(Au-PeEL)EBL30609721 035 $a(DE-He213)978-3-031-32503-8 035 $a(PPN)272264423 035 $a(EXLCZ)9927240517300041 100 $a20230626d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalysing Web Traffic $eA Case Study on Artificial and Genuine Advertisement-Related Behaviour /$fby Agnieszka Jastrz?bska, Jan W. Owsi?ski, Karol Opara, Marek Gajewski, Olgierd Hryniewicz, Mariusz Kozakiewicz, S?awomir Zadro?ny, Tomasz Zwierzchowski 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (173 pages) 225 1 $aStudies in Big Data,$x2197-6511 ;$v127 311 $a9783031325021 327 $aThe problem and its key characteristics -- The pragmatics of the data acquisition and assessment -- The proper representation: patterns, variables and their analysis -- Clustering analysis -- Building the classifiers -- The hybrid cluster-and-classify approach -- A summary view of the problem and its solution. 330 $aThis book presents ample, richly illustrated account on results and experience from a project, dealing with the analysis of data concerning behavior patterns on the Web. The advertising on the Web is dealt with, and the ultimate issue is to assess the share of the artificial, automated activity (ads fraud), as opposed to the genuine human activity. After a comprehensive introductory part, a full-fledged report is provided from a wide range of analytic and design efforts, oriented at: the representation of the Web behavior patterns, formation and selection of telling variables, structuring of the populations of behavior patterns, including the use of clustering, classification of these patterns, and devising most effective and efficient techniques to separate the artificial from the genuine traffic. A series of important and useful conclusions is drawn, concerning both the nature of the observed phenomenon, and hence the characteristics of the respective datasets, and the appropriateness of the methodological approaches tried out and devised. Some of these observations and conclusions, both related to data and to methods employed, provide a new insight and are sometimes surprising. The book provides also a rich bibliography on the main problem approached and on the various methodologies tried out. 410 0$aStudies in Big Data,$x2197-6511 ;$v127 606 $aEngineering?Data processing 606 $aComputational intelligence 606 $aBig data 606 $aData Engineering 606 $aComputational Intelligence 606 $aBig Data 615 0$aEngineering?Data processing. 615 0$aComputational intelligence. 615 0$aBig data. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aBig Data. 676 $a659.144 676 $a659.144 700 $aJastrz?bska$b Agnieszka$01427768 701 $aOwsi?ski$b Jan W$0913343 701 $aOpara$b Karol$01427769 701 $aGajewski$b Marek$01427770 701 $aHryniewicz$b Olgierd$01427771 701 $aKozakiewicz$b Mariusz$01427772 701 $aZadro?ny$b S?awomir$0977688 701 $aZwierzchowski$b Tomasz$01427773 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910746093603321 996 $aAnalysing Web Traffic$93562716 997 $aUNINA