Zobrazeno 1 - 10
of 12
pro vyhledávání: '"André Petermann"'
Autor:
Max Kießling, Niklas Teichmann, Erhard Rahm, André Petermann, Martin Junghanns, Gomez Kevin A
Publikováno v:
Proceedings of the VLDB Endowment. 11:2006-2009
We demonstrate G radoop , an open source framework that combines and extends features of graph database systems with the benefits of distributed graph processing. Using a rich graph data model and powerful graph operators, users can declaratively exp
Autor:
Martin Junghanns, André Petermann
Publikováno v:
it - Information Technology. 58:166-175
Using graph data models for business intelligence applications is a novel and promising approach. In contrast to traditional data warehouse models, graph models enable the mining of relationship patterns. In our prior work, we introduced an approach
Publikováno v:
GRADES/NDA@SIGMOD/PODS
Despite the growing popularity of techniques related to graph summarization, a general operator for the flexible nesting of graphs is still missing. We propose a novel nested graph data model and a powerful graph nesting operator. In contrast to exis
Publikováno v:
GRADES@SIGMOD/PODS
Graph pattern matching is an important and challenging operation on graph data. Typical use cases are related to graph analytics. Since analysts are often non-programmers, a graph system will only gain acceptance, if there is a comprehensible way to
Publikováno v:
ICDIM
Frequent pattern mining is an important research field and can be applied to different labeled data structures ranging from itemsets to graphs. There are scenarios where a label can be assigned to a taxonomy and generalized patterns can be mined by r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be0547fed26dd522abd3b0f264c47908
http://hdl.handle.net/20.500.11769/317541
http://hdl.handle.net/20.500.11769/317541
Publikováno v:
Handbook of Big Data Technologies ISBN: 9783319493398
Handbook of Big Data Technologies
Handbook of Big Data Technologies
Many big data applications in business and science require the management and analysis of huge amounts of graph data. Suitable systems to manage and to analyze such graph data should meet a number of challenging requirements including support for an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa581d415267a19fbf5d18c99a5214a9
https://doi.org/10.1007/978-3-319-49340-4_14
https://doi.org/10.1007/978-3-319-49340-4_14
Publikováno v:
Proceedings of the VLDB Endowment. 7:1577-1580
We demonstrate BIIIG (Business Intelligence with Integrated Instance Graphs), a new system for graph-based data integration and analysis. It aims at improving business analytics compared to traditional OLAP approaches by comprehensively tracking rela
Autor:
Gomez Kevin A, André Petermann, Stephan Kemper, Erhard Rahm, Martin Junghanns, Niklas Teichmann
Publikováno v:
ICDM Workshops
Complex data analytics that involve data mining often comprise not only a single algorithm but also further data processing steps, for example, to restrict the search space or to filter the result. We demonstrate graph mining with Gradoop, the first
Publikováno v:
NDA@SIGMOD
Graphs are an intuitive way to model complex relationships between real-world data objects. Thus, graph analytics plays an important role in research and industry. As graphs often reflect heterogeneous domain data, their representation requires an ex
Publikováno v:
Big Data Benchmarking ISBN: 9783319202327
WBDB
WBDB
We present FoodBroker, a new data generator for benchmarking graph-based business intelligence systems and approaches. It covers two realistic business processes and their involved master and transactional data objects. The interactions are correlate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e81eeda189fed9db0379ca468798d384
https://doi.org/10.1007/978-3-319-20233-4_13
https://doi.org/10.1007/978-3-319-20233-4_13