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Toward Reference Models of Requirements Traceability
TLDR
Four kinds of traceability link types are identified and critical issues that must be resolved for implementing each type and potential solutions are discussed, and implications for the design of next-generation traceability methods and tools are discussed and illustrated.
Telos: representing knowledge about information systems
TLDR
Telos is a language intended to support the development of information systems based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge.
The future of e-learning: a shift to knowledge networking and social software
TLDR
This paper argues that LM and KM can be viewed as two sides of the same coin, and explores how Web 2.0 technologies can leverage knowledge sharing and learning and enhance individual performance whereas previous models ofLM and KM have failed.
The Web 2.0 Driven SECI Model Based Learning Process
TLDR
An extended view of blended learning is presented which includes the combination of formal and informal learning, knowledge management, and Web 2.0 concepts into one integrated solution, by discussing what is called the Web 1.0 driven SECI model based learning process.
Query Optimization in Database Systems
TLDR
These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries, and nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed.
Fundamentals of Data Warehouses
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Scenario Management: An Interdisciplinary Approach
TLDR
An interdisciplinary definition of scenarios, frameworks for scenario development, use and evaluation, and directions for future research are proposed.
A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis
TLDR
This article proposes a clustering approach based on the social information of users to derive the recommendations and shows that this clustering technique based CF performs better than traditional CF algorithms.
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