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 and integration.

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
be developed.

Acknowledgments

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 ( [e-mail]) is a professor in the bren school of Information and Computer sciences at the university of California, Irvine. nicola Onose ( [e-mail]) 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 ( [e-mail]) 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, san diego.

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