If we had the performance information of every application on every SSD, it would be very beneficial to both SSD users and SSD manufacturers. For SSD users, they can buy the SSD that is fastest for the most frequently using applications; for SSD manufacturers, they can figure out the strength and weakness of their SSD for every application. Toward this end, this article proposes a framework named${\sf FORESEE}$FORESEEthat estimates accurately the execution time of a given IO trace (i.e.,query IO trace) of a given application on a target SSDwithout its actual execution.${\sf FORESEE}$is developed based on the observation thatif two IO traces are similar to each other in their IO behavior, their execution times tend to be similar when they are executed on the same SSD. In${\sf FORESEE}$, the execution time of a query IO trace is estimated by using the execution times of the IO traces in a database similar to the query IO trace. Our technical contributions in${\sf FORESEE}$are as follows: (1) we propose a goodness function that efficiently evaluates the quality of sets of features that are used to measure the similarity of IO traces; (2) we propose a DB structure and a searching method for efficiently searching for similar IO traces to a query IO trace; (3) we propose an aggregation method that aggregates the execution times of similar IO traces to a query IO trace for accurately estimating the execution time of the query IO trace; and (4) we verify the effectiveness of${\sf FORESEE}$via extensive experiments by using real-world application IO traces. According to the results, the Pearson correlation coefficient (PCC) of the actual execution time and the estimated execution time by${\sf FORESEE}$is found to be 0.87, indicating${\sf FORESEE}$estimates the execution time accurately.

With the NAND flash memory technology of solid-state drives (SSDs), the usage of SSDs is expanded to various devices. Due to the cost and time limitations of measuring the actual execution time of each application on SSDs, it is difficult for users to determine the best SSD for their most commonly used applications. In this paper, we propose a framework of estimating the execution time of an application IO trace (i.e., a query IO trace) on a target SSD without its real execution. Our framework is based on the observation that if two IO traces are similar in their IO behavior, their execution times tend to be similar when executed on the same SSD. The performance of the framework is evaluated through extensive experiments on real applications. The results show that our framework is accurate in estimating the execution time of an IO trace on SSDs.

Many Geographic Information Systems (GIS) handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of these datasets. In this paper we propose a new interactive framework that allows users to get approximate answers in near instantaneous time, thus allowing for truly interactive data exploration. Our method utilizes two proposed statistical approaches: probabilistic join and sampling based join. Our probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while our sampling based method provides an order of magnitude improvement over the full quad-tree join and also provides running confidence intervals. We propose a framework that combines the two approaches to allow end users to tradeoff speed versus bounded accuracy. The two approaches are evaluated empirically with real and synthetic datasets.