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Geoffrey Challen 2015-06-22 11:32:55 -04:00
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paper.tex
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@ -34,7 +34,7 @@ Website: \texttt{http://odin.cse.buffalo.edu/research/}
\begin{abstract}
\input{sections/0-abstract}
\keywords{sqlite, client-side, android, smart-phone, embedded database}
\keywords{sqlite, client-side, android, smartphone, embedded database}
\end{abstract}
\section{Introduction}

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sections/3-experimental.tex
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@ -1,18 +1,15 @@
\subsection{PhoneLab}
\PhoneLab{}~\cite{phonelab} is a public smartphone platform testbed operated
at the University at Buffalo (UB). Approximately 200~UB students, faculty,
and staff use instrumented LG Nexus~5 smartphones as their
primary device and receive discounted service in return for providing data to
smartphone experiments. \PhoneLab{} participants are balanced between genders
and distributed across ages, and thus representative of the broader
smartphone user population. \PhoneLab{} smartphones run a modified version of
the Android Open Source Platform (AOSP) 4.4.4 "KitKat" including
instrumentation and logging developed in collaboration with the mobile
systems community. Participating smartphones log experimental results which
are uploaded to a centralized server when the device is charging.
\subsection{Data Collection and Analysis}
To collect and analyze SQLite queries generated by Android, we utilized the
unique capabilities of the \PhoneLab{} smartphone platform testbed located at
the University at Buffalo (UB). Approximately 200~UB students, faculty, and
staff use instrumented LG Nexus~5 smartphones as their primary device and
receive discounted service in return for providing data to smartphone
experiments. \PhoneLab{} participants are balanced between genders and
distributed across ages, and thus representative of the broader smartphone
user population. \PhoneLab{} smartphones run a modified version of the
Android Open Source Platform (AOSP) 4.4.4 "KitKat" including instrumentation
and logging developed in collaboration with the mobile systems community.
Participating smartphones log experimental results which are uploaded to a
centralized server when the device is charging.
We instrumented the \PhoneLab{} AOSP platform image to log SQLite activity by
modifying the SQLite source code and distributing the updated binary library
@ -31,15 +28,15 @@ statement arguments only as hash values.
Our trace data-set is drawn from publicly-available data provided by
11~\PhoneLab{} developers who willingly
released\footnote{\url{https://phone-lab.org/static/experiment/sample_dataset.tgz}}
complete trace data for their phones for March, 2015. Of the eleven
complete trace data for their phones for March 2015. Of the eleven
participants, seven had phones that were participating in the SQLite
experiment every day for the full month, with the remaining phones were
active for 1, 3, 14, and 19 days. A total of 254 phone/days of data were
collected including 45,399,550 SQL statements. Of these, we were unable to
interpret 308,752 statements (representing slightly over half a percent of
the trace) due to a combination of data corruption and the use of unusual SQL
syntax. Results presented in this paper that include SQL interpretation are
based on the 45,090,798 queries that we were able to successfully parse.
interpret 308,752 statements (~0.5\%) due to a combination of data corruption
and the use of unusual SQL syntax. Results presented in this paper that
include SQL interpretation are based on the 45,090,798 queries that were
successfully parsed.
%% LocalWords: PhoneLab smartphone testbed UB LG smartphones AOSP
%% LocalWords: Android KitKat SQLite OTA

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sections/6-pocketdata.tex
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@ -32,7 +32,7 @@ The Yahoo Cloud Services benchmark~\cite{ycsb} is designed to capture a variety
\subsubsection{Analytics}
These more complex queries include multiple levels of query nesting, wide joins, and extensive use of aggregation. As such, they more closely resemble analytics workload benchmarks such as TPC-H~\cite{tpch}, The Star-Schema Benchmark~\cite{ssb}, and TPC-DS~\cite{tpcds}. This resemblance is more than passing; many of the more complex queries we encountered appeared to be preparing application run-time state for presentation to the user. For example the \textit{Google Play Games} service tracks so-called \textit{events} and \textit{quests}, and participating \textit{apps}. One of the most complex queries that we encountered appeared to be linking and summarizing these features together for presentation in a list view. We note that the presence of analytics queries in pocket data management is likely to increase further, as interest grows in smart phones as a platform for personal sensing~\cite{campbell2008peoplesensing,klasnja2009using,lam2009healthmonitoring}.
These more complex queries include multiple levels of query nesting, wide joins, and extensive use of aggregation. As such, they more closely resemble analytics workload benchmarks such as TPC-H~\cite{tpch}, The Star-Schema Benchmark~\cite{ssb}, and TPC-DS~\cite{tpcds}. This resemblance is more than passing; many of the more complex queries we encountered appeared to be preparing application runtime state for presentation to the user. For example the \textit{Google Play Games} service tracks so-called \textit{events} and \textit{quests}, and participating \textit{apps}. One of the most complex queries that we encountered appeared to be linking and summarizing these features together for presentation in a list view. We note that the presence of analytics queries in pocket data management is likely to increase further, as interest grows in smartphones as a platform for personal sensing~\cite{campbell2008peoplesensing,klasnja2009using,lam2009healthmonitoring}.
\subsubsection{TPC-E}