Author image not provided
 Georgia Koutrika

Authors:
Add personal information
  Affiliation history
· HP Labs
Bibliometrics: publication history
Average citations per article1.40
Citation Count7
Publication count5
Publication years2012-2017
Available for download3
Average downloads per article160.33
Downloads (cumulative)481
Downloads (12 Months)409
Downloads (6 Weeks)58
SEARCH
ROLE
Arrow RightAuthor only
· Editor only
· Other only
· All roles


AUTHOR'S COLLEAGUES
See all colleagues of this author




BOOKMARK & SHARE


6 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 6 of 6
Sort by:

1 published by ACM
May 2018 SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 50,   Downloads (12 Months): 249,   Downloads (Overall): 249

Full text available: PDFPDF
Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: ...

2 published by ACM
July 2017 ACM Transactions on the Web (TWEB): Volume 11 Issue 4, September 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 5,   Downloads (12 Months): 63,   Downloads (Overall): 97

Full text available: PDFPDF
This article analyzes a proprietary log of printed web pages and aims at answering questions regarding the content people print (what), the reasons they print (why), as well as attributes of their print profile (who). We present a classification of pages printed based on their print intent and we describe ...
Keywords: Web print study, user log analysis, print intent

3 published by ACM
July 2017 SummerSchool '17: 1st Europe Summer School: Data Science
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 4,   Downloads (12 Months): 96,   Downloads (Overall): 96

Full text available: Mp4 Part 1Mp4 Part 1  Mp4 Part 2Mp4 Part 2  Mp4 Part 3Mp4 Part 3  Mp4 Part 4Mp4 Part 4  Mp4 Part 5Mp4 Part 5
We increasingly get our news, entertainment, and information via computers, tablets, and phones. The proliferation of digital content in a plurality of forms (including e-news, movies, and online courses), along with the popularity of portable devices has created immense opportunities as well as challenges for systems in order to provide ...

4
June 2014 IEEE Transactions on Knowledge and Data Engineering: Volume 26 Issue 6, June 2014
Publisher: IEEE Educational Activities Department
Bibliometrics:
Citation Count: 2

In this paper, we argue that preference-aware query processing needs to be pushed closer to the DBMS. We introduce a preference-aware relational data model that extends database tuples with preferences and an extended algebra that captures the essence of processing queries with preferences. Based on a set of algebraic properties ...

5
August 2013 Proceedings of the VLDB Endowment: Volume 6 Issue 12, August 2013
Publisher: VLDB Endowment
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 4,   Downloads (12 Months): 6,   Downloads (Overall): 39

Full text available: PDFPDF
As web and mobile applications become more sensitive to the user context, there is a shift from purely off-line processing of user actions (log analysis) to real-time user analytics that can generate information about the user context to be instantly leveraged by the application. Ubeone is a system that enables ...

6
April 2012 ICDE '12: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 5

In implementing preference-aware query processing, a straightforward option is to build a plug-in on top of the database engine. However, treating the DBMS as a black box affects both the expressivity and performance of queries with preferences. In this paper, we argue that preference-aware query processing needs to be pushed ...



The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us