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Semantic Interoperability of Long-Tail Geoscience Resources over the Web
Published in Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser, Large-Scale Machine Learning in the Earth Sciences, 2017
Mostafa M. Elag, Praveen Kumar, Luigi Marini, Scott D. Peckham, Rui Liu
Currently, there are tremendous amounts of scientific data available online [8]. As illustrated in Figure 9.1, the relationship between the volume of resources and their dispersion represents a proxy for the variability and heterogeneity of information flows across geosciences disciplines. The left side of the curve represents the “large” or “big” data, which are usually characterized by being relatively more homogeneous, well-defined, continuously maintained, and easy to reuse, such as remote sensing data produced by NASA. On the right side of the curve, individual researchers and small groups provide a large variety of scientific data. Two data reuse patterns can be identified in this graphic: (1) large scientific agencies produce standardized data that are self-descriptive and easy to reuse and (2) small research groups individually produce a small volume of data, which is often complex and harder to (re)use. Scientists have used the term “long tail” [8] to indicate the lower quantity but higher complexity of available data, which is usually unstructured, uncurated, harder to find, and less frequently reused. “Dark,” “Gray,” and “Wide” are synonyms for long-tail, which reflects that these data cover a broad range of the scientific data production and are currently underused [8].
Product Strategic Focus Area
Published in Daniel M. Bruder, ® System, 2020
Chris Anderson’s book, The Long Tail: Why the Future of Business is Selling Less of More, tells about the cultural, technological, and economic changes where there is a shift away from a relatively small set of product offerings or “hits” to many niches. This creates a “Long Tail” in the relationship between sales and product. Anderson explains that the cost of production and distribution has fallen, and there is less need to put consumers and products into a one-size-fits-all container. He argues that with traditional brick and mortar distribution, there was limited physical space. That constraint has been substantially reduced with online sales. As a result, a company can offer multiple products to fit many niches (Anderson, 2008).
Product development, fashion buying and merchandising
Published in Textile Progress, 2022
Rachel Parker-Strak, Rosy Boardman, Liz Barnes, Stephen Doyle, Rachel Studd
The ‘long tail effect’ is the opportunity to offer much wider product ranges sourced from a wider supplier base. The increase of market segmentation and niche lifestyle consumers has made the concept of long tailing more viable (Anderson, 2006). The long tail effect recognises the falling costs of production and the opportunity to gather data on, and develop product assortments for, the individual. It also offers ways to market these assortments to niche consumers. Hence, a much broader and shallow range assortment is possible. For a merchandiser, this potentially endless range planning process can change the characteristics of a completed range plan (Clark, 2014). Keeping volumes small but increasing product styles allows a retailer to target more consumers by offering a huge variety of styles. This is particularly suitable for retailers that target global customers that require a large variety of products, as the cultural and logistical elements are completely different in different regions and countries. This approach is seen specifically in online fast fashion retailers (Boardman, Parker-Strak, et al., 2020).
Artificial intelligence methods to support the research of destination image in tourism. A systematic review
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Angel Diaz-Pacheco, Miguel Á. Álvarez-Carmona, Rafael Guerrero-Rodríguez, Luz Angélica Ceballos Chávez, Ansel Y. Rodríguez-González, Juan Pablo Ramírez-Silva, Ramón Aranda
The study of Padmaja and Sudha (2019) was focused on surveying the use of Big Data and AI algorithms to forecast demand in the tourism industry. They found that one of the main topics in the literature is the integration of big data with sentiment analysis techniques. Samara et al. (2020) assessed the challenges and benefits of incor- porating Big Data and Artificial Intelligence in the tourism industry. They discovered that the overall travel experience could become richer at every level pre-, post-, and during the trip. It can exploit the long tail distribution of the available (big) data and provide a higher experience, matching or exceeding consumers’ expectations of niche markets.