Complex data queries, because of their need for random accesses, have proven to be slow
unless all the data can be accommodated in DRAM. There are many domains, such as …

Big data applications often incur large costs in I/O, data transfer and copying overhead,
especially when operating in cloud environments. Since most such computations are …

Modern data appliances face severe bandwidth bottlenecks when moving vast amounts of
data from storage to the query processing nodes. A possible solution to mitigate these …

Specialized on-chip accelerators are widely used to improve the energy efficiency of
computing systems. Recent advances in memory technology have enabled near-data …

The ever increasing amount of data being handled in data centers causes an intrinsic
inefficiency: moving data around is expensive in terms of bandwidth, latency, and power …

Taking advantage of recently released hybrid multicore architectures, such as the Intel's
Xeon+ FPGA machine, where the FPGA has coherent access to the main memory through …

Estimating the selectivity of a query is a key step in almost any cost-based query optimizer.
Most of today's databases rely on histograms or samples that are periodically refreshed by …

Analytic workloads on terabyte data-sets are often run in the cloud, where application and
storage servers are separate and connected via network. In order to saturate the storage …

Implementing parallel operators in multi-core machines often involves a data partitioning
step that divides the data into cache-size blocks and arranges them so to allow concurrent …

Big data analytic applications give rise to large-scale extract-transform-load (ETL) as a
fundamental step to transform new data into a native representation. ETL workloads pose …