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Big Data Security: Toward a Hashed Big Graph
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
In Pregel all vertex programs run simultaneously through a sequence of supersteps. In each superstep a vertex first receives all messages from the last superstep, then executes and sends new messages to all neighbors in the next superstep. During this process a barrier is used to ensure the synchronicity. The whole program halts while there are no messages remaining and all vertexes “vote” to terminate. The following example [6] shows how a page-rank algorithm works in Pregel. The page-rank algorithm [5] is designed by Google to determine the importance of websites. A vertex program can receive the messages from all of its in-edge neighbors which contain the combination of all page ranks [10]. Then it computes the new page rank and sends it to all neighbors reached via its outgoing edges.
2 into life-cycle decision-making
Published in Jaap Bakker, Dan M. Frangopol, Klaas van Breugel, Life-Cycle of Engineering Systems, 2017
D.W. Davies, L.L. Johnson, T.P. Corigliano, M.P. Young
Flux is an off-shoot from Google X and their attempt to engage in the emerging field of LCA within the built environment. Quartz is the open-source database recently published by Flux, with 100 commonly used building industry materials. Quartz includes input by Thinkstep and their more detailed Gabi database. While still in the early stages of product development, Flux is focusing on the interoperability of tools currently used within the building design industry with an eye toward transferring LCA and other data between modeling platforms. While still limited in use, this initiative holds promise. Google’s mission is to “organize the world’s information and make it universally accessible and useful” (Google, 2016). LCA is that mission in practice, making Google a natural potential leader in the future of LCA development.
NGN Strategies for Capturing the Consumer Market
Published in Nigel Seel, Business Strategies for the Next-Generation Network, 2006
Existing aggregators identify and rank talent by using specialist talent scouts—everyone has heard of the music industry’s A&R men. Amazon’s search engine ranks books both by sales and by customer reviews, both examples of user quality-assessment. Google ranks through a complex weighted page-link algorithm as a proxy for quality. Recommender systems matching your personal buying history with the buying patterns of similar customers have had some success. It would be unwise to bet that a search engine couldn’t create a personalized menu of highly-valued content, whether TV, music, or textual material (a personal newspaper).
Investigating the themes in supply chain finance: the emergence of blockchain as a disruptive technology
Published in International Journal of Production Research, 2022
Berk Kucukaltan, Rifat Kamasak, Baris Yalcinkaya, Zahir Irani
Several studies (e.g. Bar-Ilan et al. 2009; Haas and Unkel 2017) criticised search engines for the production of potentially biased results. Yet, a recent paper by Lewandowski, Sünkler, and Schultheiß (2020) suggested that biased, unrealistic (or at least simplistic) search results might only emerge in small-scale studies where commercial ads were entered into the analysis. The authors suggested that ‘large-scale interaction studies using real user data’ (7) should be the priority of the studies in which ‘cooperation between search engine companies and academia’ (79) is required. In line with this suggestion, commercial ads, traffic forwarding, and items labelled as commercial were excluded from our large-scale analysis, which covered all searches done across the world in the English language to increase the reliability of our findings. By doing so, we aimed to ensure that unrealistic user behaviour was not transferred to real search situations (Lewandowski et al. 2018; Schultheiß, Sünkler, and Lewandowski 2018; Lewandowski, Sünkler, and Schultheiß 2020). Since we were interested in the behaviour of real users, we performed a Google Search Engine Results Page (SERP) analysis which presented the search results where only organic pages were included (and paid ones were excluded). The primary rationale for mainly using Google was based on the fact that Google holds over 90% worldwide market share and dominates global digital (Kemp 2021).
Performance analysis and design of competitive business models
Published in International Journal of Production Research, 2018
Google created an extremely user friendly platform which attracts a wide variety customers for its free services, promoting an advertising environment. Free Services: Google Search; Google News; Google Finance; Google Mail; Google Sites; Google Voice; Google Talk. Google’s Revenue streams are from its Advertising platform AdWords, AdSense, Google Mobile and You tube. Key physical resources of Google are 85 offices in over 40 countries with main office in California. Google’s intellectual properties are patents, trademarks, trade secrets, copyrights.