M
Matthias Jarke
Researcher at RWTH Aachen University
Publications - 609
Citations - 16908
Matthias Jarke is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Information system & Requirements engineering. The author has an hindex of 62, co-authored 595 publications receiving 16345 citations. Previous affiliations of Matthias Jarke include Goethe University Frankfurt & University of Passau.
Papers
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Journal ArticleDOI
Toward reference models for requirements traceability
TL;DR: 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.
Journal ArticleDOI
Telos: representing knowledge about information systems
TL;DR: 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.
Journal ArticleDOI
Query Optimization in Database Systems
Matthias Jarke,Jürgen Koch +1 more
TL;DR: 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.
Journal ArticleDOI
Scenarios in system development: current practice
TL;DR: It was found that while many companies express interest in Jacobson's use case approach, actual scenario usage often falls outside what is described in textbooks and standard methodologies, and users face significant scenario management problems not yet addressed adequately in theory or practice.
Book
Fundamentals of Data Warehouses
TL;DR: This book presents a comparative review of the state of the art and best current practice of data warehouses and offers a conceptual framework by which the architecture and quality of data warehouse efforts can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence.