Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Martin Junghanns"'
Autor:
Martin Junghanns, Erhard Rahm, Matthias Täschner, Lucas Schons, Timo Adameit, Gomez Kevin A, Philip Fritzsche, Christopher Rost, Lukas Christ
Publikováno v:
The VLDB Journal. 31:375-401
Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99d54a8b441578696eca1a339a92230f
https://doi.org/10.1007/s00778-021-00667-4
https://doi.org/10.1007/s00778-021-00667-4
Publikováno v:
GRADES-NDA@SIGMOD
Analyzing connected data in forms of graphs is more relevant than ever. To allow users to write their own custom graph algorithms, graph computation models such as GraphBLAS have been developed. Unfortunately, the popular Java programming language wa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d4e1ea2db14a8640caec6c34ee8e68c
https://doi.org/10.1145/3461837.3464627
https://doi.org/10.1145/3461837.3464627
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ef4b84dd97bf846f01801d36fa926e5
https://doi.org/10.14778/3229863.3236246
https://doi.org/10.14778/3229863.3236246
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9af7d735b9c01856cb08ef7d7219ee7
https://doi.org/10.1515/itit-2016-0006
https://doi.org/10.1515/itit-2016-0006
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7db4f17a3e112270b2d9b9899fde3c5b
https://doi.org/10.1145/3078447.3078450
https://doi.org/10.1145/3078447.3078450
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::486b923e9b8eed4e303738e3ab756844
https://doi.org/10.14778/2733004.2733034
https://doi.org/10.14778/2733004.2733034
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7f5a5f1df554aaa8971ab8e87cc9303
https://doi.org/10.1109/icdmw.2016.0193
https://doi.org/10.1109/icdmw.2016.0193
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4acbc83b62da49de4fedf08fb43af34
https://doi.org/10.1145/2980523.2980527
https://doi.org/10.1145/2980523.2980527
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