Stratos Idreos focuses on Data mining, Database, Workload, Column and Context. His Data mining research is multidisciplinary, incorporating perspectives in Overhead, Information retrieval and Index. His research in Overhead focuses on subjects like Column, which are connected to Data access.
While the research belongs to areas of Index, he spends his time largely on the problem of Search engine indexing, intersecting his research to questions surrounding Metadata. His Database research integrates issues from Matching, Raw data and Parsing. His research investigates the link between Column and topics such as Analytics that cross with problems in Data management.
His main research concerns Database, Data mining, Distributed computing, Data structure and Search engine indexing. His study in Database is interdisciplinary in nature, drawing from both Overhead, Raw data and Memory bandwidth. Data mining is frequently linked to Data set in his study.
His work investigates the relationship between Distributed computing and topics such as Overlay network that intersect with problems in Download. His biological study spans a wide range of topics, including Query language, Metadata and Index. His View study combines topics in areas such as Query expansion and Data management.
His primary scientific interests are in Data structure, Distributed computing, Filter, Information retrieval and Bloom filter. The Distributed computing study combines topics in areas such as Inference engine and Associative array. His work on Adaptive indexing as part of general Information retrieval research is often related to Point, thus linking different fields of science.
In most of his Bloom filter studies, his work intersects topics such as Scalability. Stratos Idreos performs integrative study on Scalability and Workload in his works. His NoSQL study incorporates themes from Query optimization, Open research and Data science.
The scientist’s investigation covers issues in Workload, Database research, Library science, Industrial engineering and Key. Workload overlaps with fields such as Data structure, Bloom filter, Binary logarithm, Scalability and Optimization problem in his research. A majority of his Industrial engineering research is a blend of other scientific areas, such as Path and Value.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
MonetDB: Two Decades of Research in Column-oriented Database Architectures
Stratos Idreos;Fabian Groffen;Niels Nes;Stefan Manegold.
IEEE Data(base) Engineering Bulletin (2012)
Overview of Data Exploration Techniques
Stratos Idreos;Olga Papaemmanouil;Surajit Chaudhuri.
international conference on management of data (2015)
NoDB: efficient query execution on raw data files
Ioannis Alagiannis;Renata Borovica;Miguel Branco;Stratos Idreos.
international conference on management of data (2012)
Self-organizing tuple reconstruction in column-stores
Stratos Idreos;Martin L. Kersten;Stefan Manegold.
international conference on management of data (2009)
Monkey: Optimal Navigable Key-Value Store
Niv Dayan;Manos Athanassoulis;Stratos Idreos.
international conference on management of data (2017)
H2O: a hands-free adaptive store
Ioannis Alagiannis;Stratos Idreos;Anastasia Ailamaki.
international conference on management of data (2014)
Merging what's cracked, cracking what's merged: adaptive indexing in main-memory column-stores
Stratos Idreos;Stefan Manegold;Harumi Kuno;Goetz Graefe.
very large data bases (2011)
The researcher's guide to the data deluge: querying a scientific database in just a few seconds
Martin L. Kersten;Stratos Idreos;Stefan Manegold;Erietta Liarou.
very large data bases (2011)
Here are my Data Files. Here are my Queries. Where are my Results
Stratos Idreos;Ioannis Alagiannis;Ryan Johnson;Anastasia Ailamaki.
conference on innovative data systems research (2011)
Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging
Niv Dayan;Stratos Idreos.
international conference on management of data (2018)
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