Enterprise information systems : 19th International Conference, ICEIS 2017, Porto, Portugal, April 26-29, 2017, revised selected papers /
This book constitutes extended and revised papers from the 19th International Conference on Enterprise Information Systems, ICEIS 2017, held in Porto, Portugal, in April 2017. The 28 papers presented in this volume were carefully reviewed and selected for inclusion in this book from a total of 318 s...
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Corporate Authors: | |
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Group Author: | ; ; ; |
Published: |
Springer,
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Publisher Address: | Cham, Switzerland : |
Publication Dates: | [2018] |
Literature type: | Book |
Language: | English |
Series: |
Lecture notes in business information processing,
321 |
Subjects: | |
Summary: |
This book constitutes extended and revised papers from the 19th International Conference on Enterprise Information Systems, ICEIS 2017, held in Porto, Portugal, in April 2017. The 28 papers presented in this volume were carefully reviewed and selected for inclusion in this book from a total of 318 submissions. They were organized in topical sections named: databases and information systems integration; artificial intelligence and decision support systems; information systems analysis and specification; software agents and internet computing; human-computer interaction; and enterprise archite |
Carrier Form: | xvii, 632 pages : illustrations ; 24 cm. |
Bibliography: | Includes bibliographical references and author index. |
ISBN: |
9783319933740 3319933744 |
Index Number: | T58 |
CLC: |
C931.6-532 F270.7-532 |
Call Number: | F270.7-532/I613-2/2017 |
Contents: | Databases and Information Systems Integration -- Towards a Framework for Aiding the Collaborative Management of Informal Projects -- Using a Time-based Weighting Criterion to Enhance Link Prediction in Social Networks -- Professional Competence Identification through Formal Concept Analysis -- Data Quality Problems in TPC-DI based Data Integration Processes -- Efficient Filter-based Algorithms for Exact Set Similarity Join on GPUs -- Experimenting and Assessing a Probabilistic Business Process Deviance Mining Framework based on Ensemble Learning -- Artificial Intelligence and Decision Suppor |