Optimizing the Execution of Batches of Data Analysis Queries

Data analysis applications such as Kronos, a remote sensing application, and the Virtual Microscope, a telepathology application, require operating on and processing large datasets. In such situations, it is important that the storage, retrieval, and manipulation of these datasets is efficiently handled. Past research focused on the creation of database systems that abstract the data analysis process into a framework facilitating the design of algorithms to optimize the execution of scientific queries and batches of queries. These optimizations occur at different levels in the query processing chain in the database system. The present research deals with the optimizations performed by the database system when processing batches of queries. Various algorithms to optimize the memory utilization of multiple data analysis queries are presented and the effect of each on query processing performance as well as their performance are investigated

Type 2 diabetes: postprandial hyperglycemia and increased cardiovascular risk

Hyperglycemia is a major risk factor for both the microvascular and macrovascular complications in patients with type 2 diabetes. This review summarizes the cardiovascular results of large outcomes trials in diabetes and presents new evidence on the role of hyperglycemia, with particular emphasis on postprandial hyperglycemia, in adverse cardiovascular outcomes in patients with type 2 diabetes. Treatment options, including the new dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 mimetics that primarily target postprandial hyperglycemia, are also discussed. Hyperglycemia increases cardiovascular mortality, and reducing hyperglycemia lowers cardiovascular risk parameters. Control of both fasting and postprandial hyperglycemia is necessary to achieve optimal glycated hemoglobin control. Therefore, anti-hyperglycemic agents that preferentially target postprandial hyperglycemia, along with those that preferentially target fasting hyperglycemia, are strongly suggested to optimize individual diabetes treatment strategies and reduce complications

Time and Space Optimization for Processing Groups of Multi-Dimensional Scientific Queries

Data analysis applications in areas as diverse as remote sensing and telepathology require operating on and processing very large datasets. For such applications to execute efficiently, careful attention must be paid to the storage, retrieval, and manipulation of the datasets. This paper addresses the optimizations performed by a high performance database system that processes groups of data analysis requests for these applications, which we call queries. The system performs end-to-end processing of the requests, formulated as PostgreSQL declarative queries. The queries are converted into imperative descriptions, multiple imperative descriptions are merged into a single execution plan, the plan is optimized to decrease execution time via common compiler optimization techniques, and, finally, the plan is optimized to decrease memory consumption. The last two steps effectively reduce both the time and space to execute query groups, as shown in the experimental results. (UMIACS-TR-2004-14