Information Transmission Security

The data transactions over a web based supply chain are prone to security threats as the internet is involved all the way. The internet happens to be an open forum easily accessible to the general public. It is quite possible that the data gets hacked or faked resulting in financial losses. Worse, it may not reach the intended recipient at all, defeating the purpose of the usage of the web. However, it should not be a cause of concern. With appropriate pre processing of the information getting uploaded on to the web, it should be possible to see that the data does not fall in to the wrong hands and reaches the intended recipients. The required tools and techniques are introduced in this chapter.

Information Transmission with Quality of Service

The data being transferred over the supply chain has to compete with the increasing applications around the web, throwing open the challenge of meeting the constraint of in-time data transfers with the available resources. It often leads to flooding of resources, resulting in the wastage of time and loss of data. Most of the applications around the customer require real time data transfer over the web to enable right decisions. To make it happen, stringent constraints are required to be imposed on the quality of the transfer. This chapter provides the mechanism for shaping of traffic flows towards sharing the existing infrastructure.

Information Transfer

The transfer of live data over the supply chain is challenging. The problem is compounded if multimedia data is involved. The delay in the transmission, packet loss etc will be the cause for concern. In this chapter hierarchical data representation is introduced towards data streaming and better performance.

Information Mining

The data in its raw form may not be of much use for the end customer. In the attempt to extract the knowledge from the data, the concept of data mining is extremely useful. This chapter explains how the data is to be filtered out to extract useful information. Often, exactly this information is requested by the players of the supply chain towards decision making. They are not interested in the binary data.

Information Archiving

In order to keep the information accessible for the customers over the supply chain, the data in demand is to be archived and rendered with attraction. One of the major issues with the data archives is the access time. The same is addressed in this chapter with new archival methodologies.

Information Storage Security

The primary goal of the information supply chain is to provide access to the right users to share the data. So, a framework is required to define the rightful users. This chapter provides the mechanism and algorithms to do the same. The hierarchical representation of data introduced here, map on to the different degrees of permissions enjoyed by the customers throwing open new business opportunities.

Information Retrieval

The demand of the end user for the information is to be fulfilled by the supporting supply chain. The search queries for the data are to be appropriately handled to supply the content seamlessly. The users finally have to get what they want. This chapter explains how the quality of search results can be improved with a little processing on the queries.

Knowledge Base Systems

A dumb information system in the information supply chain can provide data that is often difficult for the customer to interpret and use. To help in this regard, machine intelligence based on some learning rules is introduced in this chapter. Architecture of the knowledge base and the rule base are explained. The acquired knowledge from the different sources is to be consolidated.

Intelligent Information Processing

The data stored in a database exhibits a certain pattern at different degrees of abstraction although by nature it is random. The availability of patterns in the data would be useful in classifying the same with a tag and clustering the same. Query over such a data cluster would provide a quick response. Ina huge database, it is difficult to come out with exact patterns. The result of classification and clustering is often probabilistic. As a result, the estimation would turn statistical calling for the merger of the responses. In this chapter, the clustering and classifier algorithms are explained along with the decision process to merge the results.

Information Storage

The success of information transfer from the suppliers depends largely up on the organization of the data to cater for different categories of the users. It calls for quick, competitive and cost effective solutions. To meet the same, hierarchical data representation is introduced in this chapter. The example of Data warehouse is considered to explain the concept.