Information Acquisition and Presentation

Information Acquisition and Presentation

Manjunath Ramachandra (MSR School of Advanced Studies, Philips, India)
DOI: 10.4018/978-1-60566-888-8.ch006
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Acquisition of information is the first step in the journey of information along the supply chain. The usage of internet has made it possible to get the information on the fly from a variety of sources at different physical locations which otherwise is impossible. This chapter provides the technology behind the acquisition of information and the usage of signal processing algorithms for the same. Finally, the acquired information is to be rendered to the customer in the best possible form. The different data rendering methods are outlined here.
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In the previous chapter, the different steps involved in the management of the information are explained. The lifecycle of the information management starts with the data acquisition. Here, the different steps and techniques involved in the acquisition of the data in the supply chain are discussed.

Data acquisition is the process of meaningful gathering of data from different data sources to be consumed by different players of the supply chain. The raw data available in various forms are acquired and rendered in machine readable form with the help of transducers. The transducer output required translations before being read (John D. Lenk, 1997). This chapter explains the involvement of signal processing activities in the software that drives the input and the output devices.

Visualization of the data demands the appropriate tools for rendering the data appealing and meaningful for the customers. It should provide interactivity with the database as well as control the acquisition of the required data. Under or over acquisition of the data will not be useful for decision making. It will have cost implications for acquisition and subsequent handling. The different tools and techniques for the data visualization are provided in John R. Cowell, 1997, Richard Cravens, 1996, Jerry Joyce and Marianne Moon, 1997).

The optimal acquisition of data is coupled with the way it gets processed, stored, communicated and rendered apart from the complexity of the decision process) based on the data) and the market value. Thus, and information value (M. Harrison, 2003) associated with the data is to be considered during the acquisition. However acquisition is not always feasible due to the distributed nature (Pradeep K. Sinha, 1996) of the data sources and the complexity of synchronization among these sources.

In practice, information is acquired from the distributed information sources that are heterogeneous and autonomous. In this chapter the example of RFID is taken to discuss about the issue with the data acquisition. It is expected to provide useful information on RFID and the transducer design, issues and solution in the usage with supply chain etc.

Business Intelligence (BI) is the paradigm used with the distinct components of the distributed and global organizations connected through information supply chain. It is required to provide vital information on business trends required for decision making and to carryout the transactions of the organization. Data warehouses and Data marts are required to support the BI. The data in these storage systems are populated through the ETL (extract, transform, and load) process.

Acquisition of the required data is the first step in the business intelligence process. One technique for the data acquisition is the capture the bulk data and updates the databases periodically. However the enormous transactions in the present organizations can not afford to capture the complete data. Only the incremental changes in the data need to be captured and updated in the database accordingly. The acquired data is organized in to multiple levels of hierarchy as shown in the figure 1. Before storage, the data would be sieved in to multiple levels of hierarchy as shown in figure 2.

Figure 1.

Hierarchical representations of data

Figure 2.

Data sieve and storage



Data acquisition is the automatic collection of a large volume of raw data and the conversion (Martin S. Roden, 1995) of the same in to useful information. Data acquisition basically involves mustering data from various sources (Barlevy, G., and P. Veronesi, 2000) and converting the raw data in to bits and bytes for storage. On the output side, the rendering operation outputs the information in to a form the user can understand. It makes use of the appropriate input and output devices along with the tools supported by the protocols and software.

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