Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Ashwin Lall"'
Publikováno v:
The VLDB Journal. 29:147-175
When faced with a database containing millions of tuples, a user may be only interested in a (typically much) smaller representative subset. Recently, a query called the regret minimization query was proposed toward this purpose to create such a subs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f885cb30ee18267aa77f05fde1bd916
https://doi.org/10.1007/s00778-019-00570-z
https://doi.org/10.1007/s00778-019-00570-z
Autor:
Ashwin Lall
Mathematical Foundations of Computer Science introduces students to the discrete mathematics needed later in their Computer Science coursework with theory of computation topics interleaved throughout. Students learn about mathematical concepts just i
Publikováno v:
SSDBM
We present space-efficient algorithms for performing Pearson’s chi-square goodness-of-fit test in a streaming setting. Since the chi-square test is one of the most well known and commonly used tests in statistics, it is surprising that there has be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c0228ba24e4023ed77c46cca1fea510
https://doi.org/10.1145/3400903.3400905
https://doi.org/10.1145/3400903.3400905
Publikováno v:
SIGMOD Conference
When faced with a database containing millions of tuples, an end user might be only interested in finding his/her (close to) favorite tuple in the database. Recently, a regret minimization query was proposed to obtain a small subset from the database
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56bbd563d2c6dfd632d2b1e74f9dc032
https://doi.org/10.1145/3299869.3300068
https://doi.org/10.1145/3299869.3300068
Publikováno v:
Information Sciences. 345:156-176
Measuring or estimating the number of errors in (i.e., violations to) a functional dependency (FD) offers valuable information about data semantics and quality. Most existing work focuses on FD error estimation in a centralized environment, where dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c060c8c084d7de6996d9ab55baef322a
https://doi.org/10.1016/j.ins.2016.01.051
https://doi.org/10.1016/j.ins.2016.01.051
Publikováno v:
SIGMOD Conference
Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e592dedb658b57e20a8f3c2f7bf5c84e
https://doi.org/10.1145/3183713.3196903
https://doi.org/10.1145/3183713.3196903
Publikováno v:
Proceedings of the VLDB Endowment. 8:2098-2109
In exploring representative databases, a primary issue has been finding accurate models of user preferences. Given this, our work generalizes the method of regret minimization as proposed by Nanongkai et al. to include nonlinear utility functions. Re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55781946167d14a658ed75ebc09abbf3
https://doi.org/10.14778/2831360.2831364
https://doi.org/10.14778/2831360.2831364
Publikováno v:
Proceedings of the VLDB Endowment. 3:1114-1124
We propose the k -representative regret minimization query ( k -regret) as an operation to support multi-criteria decision making. Like top- k , the k -regret query assumes that users have some utility or scoring functions; however, it never asks the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0dadfabdd5dd843f4e1046fc15d2df0
https://doi.org/10.14778/1920841.1920980
https://doi.org/10.14778/1920841.1920980
Publikováno v:
Proceedings of the VLDB Endowment. 2:85-96
We consider external algorithms for skyline computation without pre-processing. Our goal is to develop an algorithm with a good worst case guarantee while performing well on average. Due to the nature of disks, it is desirable that such algorithms ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6383adfdfc6254d0e1f30a2547b1e561
https://doi.org/10.14778/1687627.1687638
https://doi.org/10.14778/1687627.1687638
Autor:
Ashwin Lall
Publikováno v:
IEEE BigData
We propose space-efficient algorithms for performing the Kolmogorov-Smirnov test on streaming data. The Kolmogorov-Smirnov test is a non-parametric test for measuring the strength of a hypothesis that some data is drawn from a fixed distribution (one
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16d062d0869ef1aaade5874ae8b63a8c
https://doi.org/10.1109/bigdata.2015.7363746
https://doi.org/10.1109/bigdata.2015.7363746