26,172 research outputs found
Measuring and Managing Answer Quality for Online Data-Intensive Services
Online data-intensive services parallelize query execution across distributed
software components. Interactive response time is a priority, so online query
executions return answers without waiting for slow running components to
finish. However, data from these slow components could lead to better answers.
We propose Ubora, an approach to measure the effect of slow running components
on the quality of answers. Ubora randomly samples online queries and executes
them twice. The first execution elides data from slow components and provides
fast online answers; the second execution waits for all components to complete.
Ubora uses memoization to speed up mature executions by replaying network
messages exchanged between components. Our systems-level implementation works
for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the
EasyRec Recommendation Engine, and the OpenEphyra question answering system.
Ubora computes answer quality much faster than competing approaches that do not
use memoization. With Ubora, we show that answer quality can and should be used
to guide online admission control. Our adaptive controller processed 37% more
queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
Database Learning: Toward a Database that Becomes Smarter Every Time
In today's databases, previous query answers rarely benefit answering future
queries. For the first time, to the best of our knowledge, we change this
paradigm in an approximate query processing (AQP) context. We make the
following observation: the answer to each query reveals some degree of
knowledge about the answer to another query because their answers stem from the
same underlying distribution that has produced the entire dataset. Exploiting
and refining this knowledge should allow us to answer queries more
analytically, rather than by reading enormous amounts of raw data. Also,
processing more queries should continuously enhance our knowledge of the
underlying distribution, and hence lead to increasingly faster response times
for future queries.
We call this novel idea---learning from past query answers---Database
Learning. We exploit the principle of maximum entropy to produce answers, which
are in expectation guaranteed to be more accurate than existing sample-based
approximations. Empowered by this idea, we build a query engine on top of Spark
SQL, called Verdict. We conduct extensive experiments on real-world query
traces from a large customer of a major database vendor. Our results
demonstrate that Verdict supports 73.7% of these queries, speeding them up by
up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM
SIGMOD conference 201
Efficient Scalable Accurate Regression Queries in In-DBMS Analytics
Recent trends aim to incorporate advanced data analytics capabilities within DBMSs. Linear regression queries are fundamental to exploratory analytics and predictive modeling. However, computing their exact answers leaves a lot to be desired in terms of efficiency and scalability. We contribute a novel predictive analytics model and associated regression query processing algorithms, which are efficient, scalable and accurate. We focus on predicting the answers to two key query types that reveal dependencies between the values of different attributes: (i) mean-value queries and (ii) multivariate linear regression queries, both within specific data subspaces defined based on the values of other attributes. Our algorithms achieve many orders of magnitude improvement in query processing efficiency and nearperfect approximations of the underlying relationships among data attributes
Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy
Differential privacy is a promising privacy-preserving paradigm for
statistical query processing over sensitive data. It works by injecting random
noise into each query result, such that it is provably hard for the adversary
to infer the presence or absence of any individual record from the published
noisy results. The main objective in differentially private query processing is
to maximize the accuracy of the query results, while satisfying the privacy
guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an
appropriate strategy, processing a batch of correlated queries as a whole
achieves considerably higher accuracy than answering them individually.
However, to our knowledge there is currently no practical solution to find such
a strategy for an arbitrary query batch; existing methods either return
strategies of poor quality (often worse than naive methods) or require
prohibitively expensive computations for even moderately large domains.
Motivated by this, we propose low-rank mechanism (LRM), the first practical
differentially private technique for answering batch linear queries with high
accuracy. LRM works for both exact (i.e., -) and approximate (i.e.,
(, )-) differential privacy definitions. We derive the
utility guarantees of LRM, and provide guidance on how to set the privacy
parameters given the user's utility expectation. Extensive experiments using
real data demonstrate that our proposed method consistently outperforms
state-of-the-art query processing solutions under differential privacy, by
large margins.Comment: ACM Transactions on Database Systems (ACM TODS). arXiv admin note:
text overlap with arXiv:1212.230
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