Big data: Scale down, scale up, scale out

Summary form only given, as follows. The Big Data performance challenge arises whenever the volume or velocity of data overwhelms current processing systems and techniques, resulting in performance that falls far short of desired. Three approaches to improving the performance by orders of magnitude are: 1) Scale down the amount of data processed or the resources needed to perform the processing; 2) Scale up the computing resources on a node, via parallel processing and faster memory/storage technologies; and 3) Scale out the computing to distributed nodes in a cluster/cloud or at the edge where the data resides. This talk will highlight our research tackling all three of these approaches, discussing the key challenges, our solutions, and promising future directions.