Quickr

Kandula, Srikanth; Shanbhag, Anil; Vitorovic, Aleksandar; Olma, Matthaios; Grandl, Robert; Chaudhuri, Surajit; Ding, Bolin

doi:10.1145/2882903.2882940

Cited by 95 publications

(22 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For queries that are not boundedly evaluable, an approach is to compute approximate answers under available re- sources. Approximation techniques have been extensively studied, based on synopsis (e.g., [35][36][37][38][39]) or dynamic sampling (e.g., [40][41][42]). We have proposed a data-driven approximation scheme [17] that computes approximate answers to an RA aggr query Q in a dataset , by identifying a fraction of under an extension of the access schema of [5].…”

Section: Related Workmentioning

confidence: 99%

Bounded Evaluation: Querying Big Data with Bounded Resources

CaoYang

Fan

Yuan

2020

Int. J. Autom. Comput.

View full text Add to dashboard Cite

This work aims to reduce queries on big data to computations on small data, and hence make querying big data possible under bounded resources. A query is boundedly evaluable when posed on any big dataset , there exists a fraction of such that , and the cost of identifying is independent of the size of. It has been shown that with an auxiliary structure known as access schema, many queries in relational algebra (RA) are boundedly evaluable under the set semantics of RA. This paper extends the theory of bounded evaluation to RA aggr , i.e., RA extended with aggregation, under the bag semantics. (1) We extend access schema to bag access schema, to help us identify for RA aggr queries. (2) While it is undecidable to determine whether an RA aggr query is boundedly evaluable under a bag access schema, we identify special cases that are decidable and practical. (3) In addition, we develop an effective syntax for bounded RA aggr queries, i.e., a core subclass of boundedly evaluable RA aggr queries without sacrificing their expressive power. (4) Based on the effective syntax, we provide efficient algorithms to check the bounded evaluability of RA aggr queries and to generate query plans for bounded RA aggr queries. (5) As proof of concept, we extend PostgreSQL to support bounded evaluation. We experimentally verify that the extended system improves performance by orders of magnitude.

show abstract

Section: Related Workmentioning

confidence: 99%

Bounded Evaluation: Querying Big Data with Bounded Resources

CaoYang

Fan

Yuan

2020

Int. J. Autom. Comput.

View full text Add to dashboard Cite

show abstract

“…In this case, the selected blocks are considered as random samples of D. However, statistics built from blocklevel samples may not be as good as those from record-level sampling. Several solutions were proposed to reduce data bias in block-level samples in databases [19] and big data clusters [43]- [45]. The problem occurs due to the inconsistent probability distributions in the distributed data blocks of a data set.…”

Section: B Random Samplingmentioning

confidence: 99%

An Asymptotic Ensemble Learning Framework for Big Data Analysis

Salloum

Huang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In order to enable big data analysis when data volume goes beyond the available computing resources, we propose a new method for big data analysis. This method uses only a few random sample data blocks of a big data set to obtain approximate results for the entire data set. The random sample partition (RSP) distributed data model is used to represent a big data set as a set of non-overlapping random sample data blocks. Each block is saved as an RSP data block file that can be used directly to estimate the statistical properties of the entire data set. A subset of RSP data blocks is randomly selected and analyzed with existing sequential algorithms in parallel. Then, the results from these blocks are combined to obtain ensemble estimates and models which can be improved gradually by appending new results from the newly analyzed RSP data blocks. To this end, we propose a distributed data-parallel framework (Alpha framework) and develop a prototype of this framework using Microsoft R Server packages and Hadoop distributed file system. The experimental results of three real data sets show that a subset of RSP data blocks of a data set is sufficient to obtain estimates and models which are equivalent to those computed from the entire data set. INDEX TERMS Big data analysis, cluster computing, random sample partition, block-level sampling, distributed and parallel computing, approximate computing, random sampling, ensemble methods.

show abstract

“…This may take a long time particularly for candidate networks with some base relations. There are several join sampling methods that compute a sample of a join by joining only samples the input tables and avoid computing the full join [14,37,50]. To sample the results of join R 1 ▷◁ R 2 , most of these methods must know some statistics, such as the number of tuples in R 2 that join with each tuple in R 1 , before performing the join.…”

Section: Poissonmentioning

confidence: 99%

A Signaling Game Approach to Databases Querying and Interaction

Termehchy

Touri

2015

Proceedings of the 2015 International Conference on the Theory of Information Retrieval

View full text Add to dashboard Cite

As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way way they express their information needs in form of queries during their interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We propose two efficient implementation of this method over large relational databases. Our extensive empirical studies over real-world query workloads indicate that our algorithms are efficient and effective.

show abstract

Quickr

Cited by 95 publications

References 31 publications

Bounded Evaluation: Querying Big Data with Bounded Resources

Bounded Evaluation: Querying Big Data with Bounded Resources

An Asymptotic Ensemble Learning Framework for Big Data Analysis

A Signaling Game Approach to Databases Querying and Interaction

Contact Info

Product

Resources

About