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• W4285242557 abstract "Abstract Insights of social development, presented in various forms, such as metrics, figures, text summaries, whose purpose is to summarize, explain, and predict the situations and trends of society, is extremely useful to guide organizations and individuals to better realize their own objectives in accordance with the whole society. Deriving these insights accurately and swiftly has become an interest for a range of organizations, including agencies governing districts, city even the whole country, they use these insights to inform policy-makings. Business investors who peak into statistical numbers for estimating current economical situations and future trends. Even for individuals, they could look at some of these insights to better align themselves with macroscopical social trends. There are many challenges to develop these insights in a data-driven approach. First, required data come from a large number of heterogeneous sources in a variety of formats. One single source’s data could be in the size of hundreds of Gigabytes to several TeraBytes, ingesting and governing such huge amount of data is not a small challenge. Second, many complex insights are derived by domain human experts in a trail-and-error fashion, while interacting with data with the aid of computer algorithms. To quickly experiment various algorithms, it asks for software capabilities for infusing human experts and machine intelligence together, this is challenging but critical for success. By designing and implementing a flexible big data stack that could bring in a variety of data components. We address some of the challenges to infuse data, computer algorithm and human together in Zilian Tech company [20]. In this paper we present the architecture of our data stack and articulate some of the important technical choices when building such stack. The stack is designed to be equipped with scalable storage that could scale up to PetaBytes, as well as elastic distributed compute engine with parallel computing algorithms. With these features the data stack enables a) swift data analysis, by human analysts interacting with data and machine algorithms via software support, with on-demand question answering time reduced from days to minutes; b) agile building of data products for end users to interact with, in weeks if not days from months." @default.
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• W4285242557 date "2022-01-01" @default.
• W4285242557 modified "2023-09-25" @default.
• W4285242557 title "Leveraging Modern Big Data Stack for Swift Development of Insights into Social Developments" @default.
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• W4285242557 doi "https://doi.org/10.1007/978-981-19-2456-9_34" @default.
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