starting to store their data in the cloud,
it makes sense to think about data
service integration in the cloud when
there are many, many small data services available. For example, how can
Clark Cloudlover integrate his Google
calendar with his wife’s Apple iCal
calendar? At this point, there is no homogeneity in data representation and
querying, and the number of cloud
service providers is rapidly increasing.
Google Fusion Tables27 is one example
of a system that follows this trend and
allows its users to upload tabular data
sets (spreadsheets), to store them in
the cloud, and subsequently to integrate and query them. Users are able
to integrate their data with other publicly available datasets by performing
left outer joins on primary keys, called
table merges. Fusion Tables also visualize users’ data using maps, graphs,
and other techniques. Work is needed
on many aspects of cloud data sharing
Data summaries. As the number
of data services increases to a “
consumer scale,” it will be difficult even
to find the data services of interest and
to differentiate among data services
whose output schemas are similar.
One approach to easing this problem
is to offer data summaries that can be
searched and that can give data service
consumers an idea of what lies behind
a given data service. Data sampling and
summarization techniques that have
been traditionally employed for query
optimization can serve as a basis for
work on large-scale data service characterization and discovery.
Cloud data service security. Stor-
ing proprietary or confidential data in
the cloud obviously creates new secu-
rity problems. Currently, there are two
broad choices. Data owners can either
encrypt their data, but this means that
all but exact-match queries have to be
processed on the client, moving large
volumes of data across the cloud, or
they must trust cloud providers with
their data, hoping there are enough
security mechanisms in the cloud to
guard against malicious applications
and services that might try to access
data that does not belong to them.
There is early ongoing work24 that may
help to bridge this gap by enabling que-
ries and updates over encrypted data,
but much more work is needed to see
if practical (for example, efficient) ap-
proaches and techniques can indeed
We would to thank Divyakant Agrawal
(UC Santa Barbara), Pablo Castro (
Microsoft), Alon Halevy (Google), James
Hamilton (Amazon) and Joshua Spiegel (Oracle) for their detailed comments on an earlier version of this
article. We also thank the associate
editor and anonymous reviewers for
feedback that improved the quality of
this article. This work was supported
in part by NSF IIS awards 0910989,
0713672, and 1018961.
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Michael J. Carey ( firstname.lastname@example.org) is a professor in
the bren school of Information and Computer sciences at
the university of California, Irvine.
nicola Onose ( email@example.com) is a software enginer
at Google. He conducted this work as a postdoctoral
research fellow in the bren school of Information and
Computer sciences at the university of California, Irvine.
Michalis Petropoulos ( firstname.lastname@example.org) is an
architect at Greenplum/eMC. He conducted this work
while a research scientist in the Computer sceince and
engineering department at the university of California,
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