دانلود کتاب داده کاوی کلان داده و پیچیدگی

As the title of our book suggests – which our literary colleagues Tim and Mary Fowler suggested to us – the first theme concerns thinking about big data and data mining from a complexity science and critical perspective. The worlds of data and method have changed, expanding far beyond the confines of conventional research (Burrows & Savage, 2014; Veltri, 2017). But, because of this expansion, data and method have also broadened in their usage, becoming part and parcel of the daily life of most companies and public-sector organisations (Raghavan, 2014). In other words, data and method are no longer under the strict purview of academia. In fact, one is more likely to read about the cutting-edge of big data in Wired than most journals in the social sciences (Cukier & Mayer-Schoenberger, 2013). As a result, knowledge of big data and data mining within academia varies considerably (Castellani, 2014). For example, while the social sciences continue to study relatively static data sets using conventional linear statistics, other fields, such as physics, regularly study highly dynamic temporal/spatial data sets using the latest advances in data mining (Castellani et al., 2016). And, it is here, with this imbalance in awareness, particularly amongst the social sciences, that we arrive at our first major challenge. In fact, some go so far as to call it a crisis (Burrows & Savage, 2014; Savage & Burrows, 2007), which they articulate as follows: the significant variance in knowledge of the tools of data mining and computational modelling and big data leaves many within the social sciences disadvantaged and discredited when it comes to the complex and critical discussions surrounding our currently data-saturated globalised world(s) – which is a problem for all involved, as social science is critical to such discussions (Burrows & Savage, 2014).