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typos, and a summary section

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Oliver Kennedy 2016-01-19 23:09:30 -05:00
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main.bib
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Created for Oliver Kennedy at 2016-01-19 21:41:41 -0500
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sections/1-description.tex
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@ -91,7 +91,7 @@ that work on data management systems, real-time and embedded devices, programmin
and operating and mobile systems. We believe research involving \PocketData{} also lies at
the intersection of these communities. Specialized databases systems for embedded devices is
a growing topic in the database community. As embedded processors become more capable, with
larger amounts of main memory available (e.g. Intel Galileo), there is a growing push from the embedded
larger amounts of main memory available (e.g. Intel's Edison platform), there is a growing push from the embedded
and also from the real-time communities to explore larger software capabilities, including database
systems and query processing systems, in embedded deployments. The programming language
community is exploring domain specific languages for specialized query processing.

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sections/2-research.tex
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In this section we present a few concrete projects that would benefit from \PocketData{} and then describe how the proposed infrastructure will enable
reach for the PIs and the broader CISE community.
\subsection{Adaptive Indexes}
\subsection{Adaptive Data Management}
Selecting the correct physical structure for a database under a given workload is an extremely challenging~\cite{Chaudhuri:1997:ECI:645923.673646,Chaudhuri:1998:ALI:276304.276337,Chaudhuri:2007:SDS:1325851.1325856,Agrawal:2000:ASM:645926.671701} part of database management.
The index selection problem becomes even harder when workload characteristics fluctuate rapidly or are not known in advance.
There is currently substantial interest in a breed of self-adapting, adaptive index structures~\cite{idreos2007database,Idreos:2011:MWC:2002938.2002944} that address dynamic index selection by facilitating \textit{incremental, online} changes to the index.
@ -23,7 +23,7 @@ As a result most data management benchmarks evaluate systems under stable, stead
By contrast, \PocketData{} workloads often show extreme variation in both application demands and resource availability.
As a trivial example, an app might demand low-latency, low-power access to data when a user is actively using the phone, while admitting high-latency high-power organizational tasks when the phone is plugged in~\cite{Challen:2015:MWE:2699343.2699361}.
\textbf{Community Interest:} \textit{Stratos Idreos} from the DAS lab at Harvard will use the \PocketData{} metrics and benchmark workloads to evaluate his group's work on adaptive data systems.
\textbf{Community Interest:} \textit{Stratos Idreos}'s DAS lab at Harvard will use the \PocketData{} metrics and benchmark workloads to evaluate their work on adaptive data systems.
\citedquote{Stratos Idreos (Harvard)}{I think work on adaptive data systems could benefit. I assume Pocket Data will capture diverse workloads (from various apps) and so this would be a perfect environment to test adaptive data systems.
I have a new project on easy to design systems out of modules that can be synthesized. The input is workloads. Perhaps PocketData can provide a testing framework for such work for designing data systems for mobile environments.
}
@ -40,7 +40,7 @@ The relatively limited compute and memory resources available on tablets and sma
\textbf{Infrastructure Justification:} Small-data analytics efforts are presently siloed, with most research efforts targeting entire software stacks, from the user interface front-end to the back-end database.
The standard evaluation tools offered by the \PocketData{} benchmar would help to that decouple the research challenges involved in small-data analytics, and allow a broader community of researchers to contribute.
The standard evaluation tools offered by the \PocketData{} benchmark would help to that decouple the research challenges involved in small-data analytics, and allow a broader community of researchers to contribute.
For example, an embedded database benchmark simulating a visual query interface workload would serve as a standard for evaluating novel algorithms, indexes, and data management tools.
\textbf{Community Interest:}

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sections/3-community.tex
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Raghunath Nambiar & \emph{Cisco} & Databases & Performance measurement \\
Reza Taheri & \emph{VMWare} & Databases & Performance measurement \\
Jens Dittrich & \emph{Saarland University}& Databases/Mobile Systems & Small-data analytics \\
Sharad Agarwal & \emph{Microsoft Research}& Mobile Systems/Sensing & Mobile systems privacy \\ \hline
Sharad Agarwal & \emph{Microsoft Research}& Mobile Systems/Sensing & Mobile systems performance \\ \hline
\end{tabular}
\end{center}
\caption{\textbf{Existing Community Interest in} \PocketData{}}

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sections/3-planning.tex
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At these conferences the PIs will network with researchers who work on IoT. In addition, there are many new conferences focusing on IoT that are emerging. The PIs expect to attend
IoTA, IoTDI, and WF-IoT. Towards the end of the first year of the proposal, the PIs will begin to develop a tutorial on embedded databases and plan for a \PocketData{} workshop.
The PIs will submit a \textbf{CI-NEW} proposal for \PocketData{} in Fall of 2017, approximately 14 months after the start of the planning proposal.
The PIs will submit a \textbf{CI-NEW} proposal for \PocketData{} in Fall of 2017, approximately 15 months after the start of the planning proposal.

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---- Overview ----
A common requirement of the 4 million apps running on the world's 2 billion smartphones is persisting structured data. Embedded databases such as SQLite are heavily used for this purpose, with a single typical Android smartphone averaging more than two SQLite queries per second. The fundamental challenges experienced by mobile apps using embedded databases — minimizing energy consumption, latency, and disk utilization — are familiar ground for database researchers. However, in spite of active research in the areas of smartphone query processing and embedded databases, the specific tradoffs introduced by this new domain of pocket-scale data are far less well understood.
Key challenges in this space include the lack of publicly available data regarding smartphone database usage patterns in the real world, concrete high-level optimization targets, and tools and methodologies for reliably measuring database performance along axes relevant to smartphone apps. We propose infrastructure support and community-building efforts that will both improve existing research on embedded databases, and help to encourage new and innovative research in the area. This infrastructure support will take the form of real-world smartphone usage traces, a benchmarking suite for pocket-scale data, visualization tools, and instrumentation for mobile embedded databases.
Keywords: databases, smartphones, benchmarking
---- Intellectual Merit ----
---- Broader Impacts ----
With 2 billion smartphones in the world, people are increasingly relying on smartphones to manage their lives. The proliferation of data-driven smartphone apps is driving a need to create more, better, faster, more user-friendly, and more power-aware techniques for managing their data. It is critical that we begin understand how smartphone apps interact with their data. Our proposal lays the groundwork for research on pocket-scale data management. We have interest from the Transaction Processing Council for our proposed benchmark, and even now several members of the database, systems, and programming language communities have expressed interest in the resources we propose to offer. In addition to supporting research in a critical area, this proposal will support one graduate student during the planning phase and up to two graduate students in later phases, resulting in between one and two PhD Theses.