Neoklis Polyzotis mainly focuses on Database, Theoretical computer science, Machine learning, Artificial intelligence and Crowdsourcing. His Database study integrates concerns from other disciplines, such as Collaborative filtering and Big data. His research integrates issues of Path expression, Xml data, XML, Graph and XQuery in his study of Theoretical computer science.
His Machine learning research is multidisciplinary, incorporating perspectives in App store, Algorithm and Set. The concepts of his Artificial intelligence study are interwoven with issues in Software deployment, Index and Bloom filter. His Crowdsourcing research is multidisciplinary, incorporating perspectives in Query language, SQL, Data management and Data model.
His scientific interests lie mostly in Data mining, Artificial intelligence, Machine learning, Theoretical computer science and Database. Neoklis Polyzotis has included themes like XML, Distributed computing and Database administrator in his Data mining study. His research integrates issues of Data validation, Crowdsourcing, Data management and Big data in his study of Artificial intelligence.
His biological study spans a wide range of topics, including Rank, Set, Joins and Query optimization. Neoklis Polyzotis combines subjects such as Variety, Overhead and Heuristics with his study of Set. His Database research integrates issues from World Wide Web and Information retrieval.
Artificial intelligence, Machine learning, Data management, Production and Data validation are his primary areas of study. His work deals with themes such as Crowdsourcing and Index, which intersect with Artificial intelligence. His work in the fields of Machine learning, such as Cluster analysis, intersects with other areas such as Sorted array.
His Data management research includes themes of End-to-end principle, Computation and Data science. His Data validation research is multidisciplinary, relying on both Data lifecycle, Open research and Training set. In his study, which falls under the umbrella issue of Debugging, Slicing is strongly linked to Data mining.
Neoklis Polyzotis spends much of his time researching Artificial intelligence, Machine learning, Data validation, Data management and Production. His studies deal with areas such as Orchestration, Software deployment and Scripting language as well as Artificial intelligence. Neoklis Polyzotis interconnects Use case and App store in the investigation of issues within Machine learning.
His Data management research is multidisciplinary, incorporating elements of Bloom filter, Hash table and Index, B-tree. His Production investigation overlaps with Focus, Set, Data preparation, Data lifecycle and Context. His Focus research spans across into subjects like Training set, Open research, Work and Data science.
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.
The Case for Learned Index Structures
Tim Kraska;Alex Beutel;Ed H. Chi;Jeffrey Dean.
international conference on management of data (2018)
CrowdScreen: algorithms for filtering data with humans
Aditya G. Parameswaran;Hector Garcia-Molina;Hyunjung Park;Neoklis Polyzotis.
international conference on management of data (2012)
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
Denis Baylor;Eric Breck;Heng-Tze Cheng;Noah Fiedel.
knowledge discovery and data mining (2017)
SeeDB: efficient data-driven visualization recommendations to support visual analytics
Manasi Vartak;Sajjadur Rahman;Samuel Madden;Aditya Parameswaran.
very large data bases (2015)
Statistical synopses for graph-structured XML databases
Neoklis Polyzotis;Minos Garofalakis.
international conference on management of data (2002)
SciHadoop: array-based query processing in Hadoop
Joe B. Buck;Noah Watkins;Jeff LeFevre;Kleoni Ioannidou.
ieee international conference on high performance computing data and analytics (2011)
Approximate XML query answers
Neoklis Polyzotis;Minos Garofalakis;Yannis Ioannidis.
international conference on management of data (2004)
Query Recommendations for Interactive Database Exploration
Gloria Chatzopoulou;Magdalini Eirinaki;Neoklis Polyzotis.
statistical and scientific database management (2009)
Human-assisted graph search: it's okay to ask questions
Aditya Parameswaran;Anish Das Sarma;Hector Garcia-Molina;Neoklis Polyzotis.
very large data bases (2011)
Answering Queries using Humans, Algorithms and Databases
Aditya G. Parameswaran;Neoklis Polyzotis.
conference on innovative data systems research (2011)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: