Building Secure Data Warehouse Schemas from Federated Information Systems

2002 ◽  
pp. 123-134 ◽  
Author(s):  
Fèlix Saltor ◽  
Marta Oliva ◽  
Alberto Abelló ◽  
José Samos
Keyword(s):  
Information Systems ◽  
Data Warehouse ◽  
Secure Data ◽  
Federated Information Systems

Enterprisewide Spatial Data Integration of Legacy Systems for Asset Management

2005 ◽  
Vol 1917 (1) ◽  
pp. 11-17
Author(s):  
James P. Hall ◽  
Rob Robinson ◽  
Mary Ann Paulis
Keyword(s):  
Information System ◽  
Information Systems ◽  
Data Warehouse ◽  
Spatial Data ◽  
Asset Management ◽  
Spatial Information ◽  
Management Decision ◽  
Related Data ◽  
Direct Linkage ◽  
Spatial Data Warehouse

This paper describes the spatial information system infrastructure implemented by the Illinois Department of Transportation (IDOT) to enable delivery of information to management decision makers in asset management applications. This spatial data warehouse infrastructure makes extensive use of geographic information system (GIS) technologies to integrate information from a variety of database structures and formats. GIS products and tools have been developed to portray and analyze these data in useful combinations focused on practitioner needs. In June 1999 the Governmental Accounting Standards Board issued Statement 34 requiring governments to have a systematic approach to managing their assets. As a result, transportation agencies have placed an increased emphasis on developing mechanisms to integrate information from disparate management information systems and legacy databases. IDOT has used GIS to develop a spatial data warehouse to enable integration. A valuable characteristic of the department's information systems infrastructure is the embedding of the underlying link–node structure into roadway inventory databases to enable the direct linkage of data through various system identifiers, including differing milepost referencing and project numbering schemes. This direct linkage enables the complex integration of asset management–related data files across the enterprise and provides access to historical asset information. Changes to route referencing systems are readily accommodated, without loss of integrative capabilities. Outputs include a variety of user-developed analyses and output products with accessibility through networks, intranets, and the Internet.

A Conceptual Model of a Knowledge Warehouse

2010 ◽  
pp. 148-161 ◽  
Author(s):  
Meira Levy
Keyword(s):  
Information Systems ◽  
Conceptual Model ◽  
Data Warehouse ◽  
Business Processes ◽  
Business Environment ◽  
Controversial Issue ◽  
Global Business ◽  
Product Usage ◽  
Organizational Information ◽  
Existing Data

A firm’s capability to transfer its existing knowledge to various stakeholders and translate knowledge into action determines its success in today‘s volatile global business environment. However, while many firms systematically manage data and information, managing knowledge remains a controversial issue. One of the reasons for this is inconclusiveness about what knowledge is and whether it can be managed. In order to more precisely define knowledge and its management, a knowledge warehouse conceptual model (KW-CM) is proposed for practically and systematically assimilating of knowledge within organizational business processes. This conceptual model integrates aspects of knowledge that encompass business processes, stakeholders and other organizational information systems within the existing data warehouse (DW) conceptual model. In addition, the paper presents a formal architecture, definitions and guidelines that describe the KW components and processes for leveraging data and information into knowledge. The proposed KW-CM is demonstrated with an example of a DW which handles information regarding customer product usage.

Towards Convergence in Information Systems Design

2020 ◽  
pp. 247-263
Author(s):  
Deepika Prakash
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information Systems ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Systems Design ◽  
Information Model ◽  
Information Systems Design ◽  
Nosql Databases ◽  
Nosql Database

Three technologies—business intelligence, big data, and machine learning—developed independently and address different types of problems. Data warehouses have been used as systems for business intelligence, and NoSQL databases are used for big data. In this chapter, the authors explore the convergence of business intelligence and big data. Traditionally, a data warehouse is implemented on a ROLAP or MOLAP platform. Whereas MOLAP suffers from having propriety architecture, ROLAP suffers from the inherent disadvantages of RDBMS. In order to mitigate the drawbacks of ROLAP, the authors propose implementing a data warehouse on a NoSQL database. They choose Cassandra as their database. For this they start by identifying a generic information model that captures the requirements of the system to-be. They propose mapping rules that map the components of the information model to the Cassandra data model. They finally show a small implementation using an example.

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