Download Pervasive Computing for Business: Trends and Applications by Varuna Godara PDF

By Varuna Godara

Pervasive Computing for company: traits and purposes examines implications of pervasive computing to ensure that present and destiny e-business managers to make accountable judgements concerning the place, while, and the way to exploit this expertise. With professional overseas contributions, this assortment advantages researchers, academicians, and practitioners drawn to this starting to be box.

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Customer demand, competitive pressure and improved decisions require timely information. To make the most of BI in today’s ever-accelerating business climate, managers should not be working with last week’s or yesterday’s data. Today, decisionmakers need data that is updated a few times a day or even in real time (Ankorion 2005). As shown in Figure 4, various types of latencies exist in BI based systems. e. the business event to the data warehouse. Analysis latency is the time required for analyzing the data present in the data warehouse to provide appropriate information to the manager.

The cart can also be made capable of guiding the customers to find a specific product at the store and on the way can also suggest and offer discounts on other products as well. This will reduce (if not completely remove) the need of sales staff at the store and hence reduce the recurring (regularly increasing) expense of the store. Automobile Industry Maintenance If various parts of an automobile are capable of sending out information regarding their usage, condition and wear-and-tear, then the car itself can decide about the services it requires including need for replacement of spare parts.

Almasi, G. (2001). Personalization of Supermarket Product Recommendations. Journal of Data Mining and Knowledge Discovery, 5, 11–32. 1023/A:1009835726774 Mark, W. (1999). Turning Pervasive into Mediated Spaces. IBM Systems Journal, 38(4), 677–692. Montgomery, D. , & Weinberg, C. E. (1981). Toward Strategic Intelligence Systems. The McKinsey Quarterly, Spring, 82-102. , et al. (2002). Adaptive and Resource-Aware Mining of Frequent Sets. In V. Kumar, S. Tsumoto, P. S. Yu, & N. ), Proceedings of the 2002 IEEE International Conference on Data Mining (pp.

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