Edits

sections/6-pocketdata.tex

@@ -22,7 +22,7 @@ The second benchmark, MobiGen has little to do with data management directly.  R
 
 \subsubsection{TPC-C} 
 
-One macro-benchmark suite that bears a close resemblance to the trace workload is TPC-C~\cite{tpcc}, which simulates a supply-chain management system.  It includes a variety of transactional tasks ranging from low-latency user interactions for placing and querying orders, to longer-running batch processes that simulate order fulfilment.  A key feature of this benchmark workload is the level of concurrency expected and required of the system.  Much of the data is neatly partitioned, but the workload is designed to force a non-trivial level of cross-talk between partitions, making concurrency a bottleneck at higher throughputs.  Conversely, mobile SQLite databases are isolated into specialized app-specific silos.  In our experiments, throughput remained at very manageable levels from a concurrency standpoint.  The most intensive database user, \textit{Google Play services} had 14.8 million statements attributable to it, just under half of which were writes.  This equates to about one write every 3 seconds, which is substantial from a power management and latency perspective, but not from the standpoint of concurrency.
+One macro-benchmark suite that bears a close resemblance to the trace workload is TPC-C~\cite{tpcc}, which simulates a supply-chain management system.  It includes a variety of transactional tasks ranging from low-latency user interactions for placing and querying orders, to longer-running batch processes that simulate order fulfillment.  A key feature of this benchmark workload is the level of concurrency expected and required of the system.  Much of the data is neatly partitioned, but the workload is designed to force a non-trivial level of cross-talk between partitions, making concurrency a bottleneck at higher throughput.  Conversely, mobile SQLite databases are isolated into specialized app-specific silos.  In our experiments, throughput remained at very manageable levels from a concurrency standpoint.  The most intensive database user, \textit{Google Play services} had 14.8 million statements attributable to it, just under half of which were writes.  This equates to about one write every 3 seconds, which is substantial from a power management and latency perspective, but not from the standpoint of concurrency.
 
 \subsubsection{YCSB}
 
@@ -36,3 +36,7 @@ These more complex queries include multiple levels of query nesting, wide joins,
 \subsubsection{TPC-E}
 
 The TPC-E benchmark emulates a brokerage firm, and includes a mix of reporting and data mining queries alongside stream-monitoring queries.  It models decision support systems that involve a high level of CPU and IO load, and that examine large volumes of rapidly changing data.  SQLite does not presently target or support streaming or active database applications, although such functionality may become available as personal sensing becomes more prevalent.
+
+%%  LocalWords:  Kang al FTL SQLite's TPC Jeong Facebook apps MobiGen
+%%  LocalWords:  smartphone SQLite TinyDB AndroBench app YCSB Yahoo
+%%  LocalWords:  DS