27,996 research outputs found
Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution
Cloud-based data analysis is nowadays common practice because of the lower
system management overhead as well as the pay-as-you-go pricing model. The
pricing model, however, is not always suitable for query processing as heavy
use results in high costs. For example, in query-as-a-service systems, where
users are charged per processed byte, collections of queries accessing the same
data frequently can become expensive. The problem is compounded by the limited
options for the user to optimize query execution when using declarative
interfaces such as SQL. In this paper, we show how, without modifying existing
systems and without the involvement of the cloud provider, it is possible to
significantly reduce the overhead, and hence the cost, of query-as-a-service
systems. Our approach is based on query rewriting so that multiple concurrent
queries are combined into a single query. Our experiments show the aggregated
amount of work done by the shared execution is smaller than in a
query-at-a-time approach. Since queries are charged per byte processed, the
cost of executing a group of queries is often the same as executing a single
one of them. As an example, we demonstrate how the shared execution of the
TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google
BigQuery than using a query-at-a-time approach while achieving a higher
throughput
How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market
A personal data market is a platform including three participants: data
owners (individuals), data buyers and market maker. Data owners who provide
personal data are compensated according to their privacy loss. Data buyers can
submit a query and pay for the result according to their desired accuracy.
Market maker coordinates between data owner and buyer. This framework has been
previously studied based on differential privacy. However, the previous study
assumes data owners can accept any level of privacy loss and data buyers can
conduct the transaction without regard to the financial budget. In this paper,
we propose a practical personal data trading framework that is able to strike a
balance between money and privacy. In order to gain insights on user
preferences, we first conducted an online survey on human attitude to- ward
privacy and interest in personal data trading. Second, we identify the 5 key
principles of personal data market, which is important for designing a
reasonable trading frame- work and pricing mechanism. Third, we propose a
reason- able trading framework for personal data which provides an overview of
how the data is traded. Fourth, we propose a balanced pricing mechanism which
computes the query price for data buyers and compensation for data owners
(whose data are utilized) as a function of their privacy loss. The main goal is
to ensure a fair trading for both parties. Finally, we will conduct an
experiment to evaluate the output of our proposed pricing mechanism in
comparison with other previously proposed mechanism
PRICE DEMAND MODEL FOR A CLOUD CACHE
Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution
Efficient dictionary compression for processing RDF big data using Google BigQuery
The Resource Description Framework (RDF) data model, is used on the Web to express billions of structured statements in a wide range of topics, including government, publications, life sciences, etc. Consequently, processing and storing this data requires the provision of high specification systems, both in terms of storage and computational capabilities. On the other hand, cloud-based big data services such as Google BigQuery can be used to store and query this data without any upfront investment. Google BigQuery pricing is based on the size of the data being stored or queried, but given that RDF statements contain long Uniform Resource Identifiers (URIs), the cost of query and storage of RDF big data can increase rapidly. In this paper we present and evaluate a novel and efficient dictionary compression algorithm which is faster, generates small dictionaries that can fit in memory and results in better compression rate when compared with other large scale RDF dictionary compression. Consequently, our algorithm also reduces the BigQuery storage and query cos
The Design of Arbitrage-Free Data Pricing Schemes
Motivated by a growing market that involves buying and selling data over the
web, we study pricing schemes that assign value to queries issued over a
database. Previous work studied pricing mechanisms that compute the price of a
query by extending a data seller's explicit prices on certain queries, or
investigated the properties that a pricing function should exhibit without
detailing a generic construction. In this work, we present a formal framework
for pricing queries over data that allows the construction of general families
of pricing functions, with the main goal of avoiding arbitrage. We consider two
types of pricing schemes: instance-independent schemes, where the price depends
only on the structure of the query, and answer-dependent schemes, where the
price also depends on the query output. Our main result is a complete
characterization of the structure of pricing functions in both settings, by
relating it to properties of a function over a lattice. We use our
characterization, together with information-theoretic methods, to construct a
variety of arbitrage-free pricing functions. Finally, we discuss various
tradeoffs in the design space and present techniques for efficient computation
of the proposed pricing functions.Comment: full pape
A Theory of Pricing Private Data
Personal data has value to both its owner and to institutions who would like
to analyze it. Privacy mechanisms protect the owner's data while releasing to
analysts noisy versions of aggregate query results. But such strict protections
of individual's data have not yet found wide use in practice. Instead, Internet
companies, for example, commonly provide free services in return for valuable
sensitive information from users, which they exploit and sometimes sell to
third parties.
As the awareness of the value of the personal data increases, so has the
drive to compensate the end user for her private information. The idea of
monetizing private data can improve over the narrower view of hiding private
data, since it empowers individuals to control their data through financial
means.
In this paper we propose a theoretical framework for assigning prices to
noisy query answers, as a function of their accuracy, and for dividing the
price amongst data owners who deserve compensation for their loss of privacy.
Our framework adopts and extends key principles from both differential privacy
and query pricing in data markets. We identify essential properties of the
price function and micro-payments, and characterize valid solutions.Comment: 25 pages, 2 figures. Best Paper Award, to appear in the 16th
International Conference on Database Theory (ICDT), 201
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