Information Integration

2010 ◽  
pp. 125-136
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Supply Chain ◽  
Data Integration ◽  
Information Integration ◽  
Use Case ◽  
Data Warehouses ◽  
Information Supply Chain ◽  
Different Sources ◽  
The Web

In an information supply chain, data gets pumped over the web from several distinct sources in various forms calling for efforts towards data integration. The integration happens at various levels by means of different algorithms. This chapter provides a framework for the interoperability of the data from different sources. The use case of integration in data warehouses is provided.

Knowledge Base Systems

2010 ◽  
pp. 208-222
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Information System ◽  
Supply Chain ◽  
Knowledge Base ◽  
Machine Intelligence ◽  
Rule Base ◽  
Learning Rules ◽  
Knowledge Base Systems ◽  
Information Supply Chain ◽  
Different Sources

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.

Commercial and Open-Source Web Portal Solutions

2011 ◽  
pp. 146-151
Author(s):  
Américo Sampaio
Keyword(s):  
Data Integration ◽  
Open Source ◽  
Business Processes ◽  
Web Portals ◽  
Unique Point ◽  
Web Portal ◽  
Business Partners ◽  
Business Operations ◽  
Different Sources ◽  
The Web

Web portals present an effective way to integrate applications, people, and business by offering a unique point of access to these resources within an organization and also with external business partners. Moreover, the integration of business processes, automation of daily tasks, and data integration contribute to cut down costs and accelerate business operations. However, Web portal development and maintenance imposes many challenges to developers, such as how to provide personalization features to users (organizations and individuals), how to control access from different users, how to integrate and present data from different sources, and how to maintain the content of the Web portal.

Web Based Model for Information Supply Chain

2010 ◽  
pp. 1-17
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Signal Processing ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Web Based ◽  
Chain Management ◽  
Web Technologies ◽  
Information Supply Chain ◽  
The Web

Advances in the web technologies and signal processing have resulted in their extensive usage in all walks of life. Although independent, these great technologies often cross each other when it comes to the realization of applications suitable for the current requirements. The present day requirements for information have changed drastically in terms of time, quality and quantity. In this chapter, the usage of these technologies for information supply chain management is explored. It forms the foundation for the rest of the chapters in this book.

scholarly journals DATA INTEGRATION IN DATA WAREHOUSING

2001 ◽  
Vol 10 (03) ◽  
pp. 237-271 ◽  
Author(s):  
DIEGO CALVANESE ◽  
GIUSEPPE DE GIACOMO ◽  
MAURIZIO LENZERINI ◽  
DANIELE NARDI ◽  
RICCARDO ROSATI
Keyword(s):  
Data Integration ◽  
Conceptual Model ◽  
Data Warehouse ◽  
Information Integration ◽  
Data Warehousing ◽  
Conceptual Representation ◽  
Operational Environment ◽  
Materialized View ◽  
Novel Approach ◽  
Different Sources

Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

scholarly journals A Privacy Preserving Approach to Collaborative Systemic Risk Identification: The Use-Case of Supply Chain Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Tirazheh Zare-Garizy ◽  
Gilbert Fridgen ◽  
Lars Wederhake
Keyword(s):  
Supply Chain ◽  
Structural Information ◽  
Risk Identification ◽  
Use Case ◽  
Supply Chain Network ◽  
Strategic Positioning ◽  
Supply Chain Networks ◽  
Structural Risk ◽  
Main Factors ◽  
Network Risk

Globalization and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network, it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of network’s structure can endanger the whole supply chain network’s robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms’ competitive advantage.

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