- 《Datenbank-Spektrum》
- 语言：外文
- ISSN：1618-2162
- 学科分类：建筑
- 简介：Seit dem 10. Jahrgang bei Springer!Datenbank-Spektrum ist das offizielle Organ der Fachgruppe Datenb...
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- 简介：Seit dem 10. Jahrgang bei Springer!Datenbank-Spektrum ist das offizielle Organ der Fachgruppe Datenbanken und Information Retrieval der Gesellschaft für Informatik (GI) e.V. Die Zeitschrift widmet sich den Themen Datenbanken, Datenbankanwendungen und Information Retrieval. Sie vermittelt fundiertes Wissen über die aktuellen Standards und Technologien, deren Einsatz und ihre kommerzielle Relevanz. Neben Grundlagenbeitr?gen, Tutorials, wissenschaftlichen Fachbeitr?gen, aktuellen Forschungsergebnissen finden sich in jeder Ausgabe auch Informationen über die Aktivit?ten der Fachgruppen, zu Konferenzen und Workshops und über neue Produkte und Bücher. Ein renommiertes Herausgebergremium aus Hochschule und Industrie gew?hrleistet die Qualit?t und fachliche Kompetenz der Beitr?ge. Künftige Schwerpunktthemen: Big Graph Data ManagementA graph is an intuitive mathematical abstraction to capture how things are connected. In the past decade, the focal point in many data management applications has shifted from individual entities and aggregations thereof toward the connection between entities. Hence today, the graph abstraction is appealing as a natural data model foundation for an increasing range of use cases in interactive as well as analytical graph data management scenarios. Graph-specific use cases can be found in various domains, such as social network analysis, product recommendations, and knowledge graphs. Graph oriented scenarios also emerge in more traditional enterprise scenarios, such as supply chain management or business process analysis. Therefore, the database community reacts to this newly sparked interest in graph data management with a vast number of projects in research as well as in industry.Graph management use cases pose novel and unique challenges to data management systems. On the operational side, typical interactive queries involve transitive closure computation along paths. Common analytical measures, such as page rank and other vertex centrality measures are also significantly more complex than traditional group by/ aggregate queries. From a data structure perspective, the irregular and skewed structure of graphs makes it challenging to achieve a good distribution over non-uniform memory access or cluster nodes for efficient parallelization – particularly, if the graph is large and changing over time.Further challenges among others are declarative graph analytics abstractions for static as well as for dynamic graphs, graph-query-aware optimization strategies, topology indexing, temporal topology indexing, topology estimation, materialized view usage, and maintenance for graph analytical measures.Graph data management is an exciting research field, now and for the years to come. This special issue aims at exhibiting our community’s current work in the field. We therefore welcome contributions from research and industry that provide original research on the problems mentioned above or that are generally related to big graph data management and processing. We also welcome case studies that showcase the challenges of graph management and graph query processing from a practical perspective, point out particular research questions, and potentially outline novel research directions.We are looking for contributions from researchers and practitioners in the above described context, which may be submitted in German or in English.Important dates:Notice of intent for a contribution: December 15th, 2016Deadline for submissions: February 1st, 2017Issue delivery: DASP-2-2017 (July 2017)Paper format: 8–10 pages, double column (cf. the author guidelines at www.datenbank-spektrum.de).Guest editors:Hannes Voigt, TU Dresdenhannes.voigt@tu-dresden.deMarcus Paradies, SAPm.paradies@sap.com Best Workshop Papers of BTW 2017 This special issue of the “Datenbank-Spektrum” is dedicated to the Best Papers of the Workshops running at the BTW 2017 at the University of Stuttgart. The selected Workshop contributions should be extended to match the format of regular DASP papers. Paper format: 8–10 pages, double columnSelection of the Best Papers by the Workshop chairs and the guest editor: April 15th, 2017Deadline for submissions: June 1st, 2017 Guest editor:Theo H?rder, University of Kaiserslauternhaerder@cs.uni-kl.de Data Processing in Industrie 4.0The Data Processing paradigm undergoes several changes recently. In the vision of Industrie 4.0 assets become smart and more autonomous. This leads to new application areas for analytics and data processing in general. We invite submissions on original research as well as overview articles covering topics from the following non-exclusive list:● Industrie 4.0 Reference Architectures ● Sensor Data Streaming ● Sensor Data Management ● Digital Twin Technology ● Analytics in Industrie 4.0 ● Edge Analytics/Fog Computing ● Sensor Data Analytics ● Advanced Analytics Expected size of the paper: 8–10 pages (double-column).Contributions either in German or in English are welcome.Deadline for submissions: Oct. 1st, 2017Issue delivery: DASP-1-2018 (March 2018)Guest editors:Bernhard Mitschang, Universit?t StuttgartBernhard.Mitschang@ipvs.uni-stuttgart.de
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- 来源数据库：
- SpringerLink电子期刊及电子图书 (1997－，部分从创刊/首卷开始 多出版类)
- Springer Online Archive Collections(OAC)--Springer回溯数据库
