Finding a Needle in a Haystack

Structuring Digital Options Towards Reducing the Struggle of Finding the Needle in the Digital Haystack

Can Tree Adjunct Grammar Guided Genetic Programming be Good at Finding a Needle In a Haystack? A Case Study

In this paper we experiment TAG3P on the even parity problems in order to investigate the robustness of tree-adjunct grammar guided genetic programming [3] (TAG3P) on the problems classified as “finding a needle in a haystack” [9]. We compare the result with grammar guided genetic programming [15] (GGGP) and genetic programming [7] (GP). The results show that TAG3P does not work well on the pro...

Finding a Needle in Haystack: Facebook's Photo Storage

This paper describes Haystack, an object storage system optimized for Facebook’s Photos application. Facebook currently stores over 260 billion images, which translates to over 20 petabytes of data. Users upload one billion new photos (∼60 terabytes) each week and Facebook serves over one million images per second at peak. Haystack provides a less expensive and higher performing solution than o...

The Needles-in-Haystack Problem

We consider a new data mining problem of detecting the members of a rare class of data, the needles, that have been hidden in a set of records, the haystack. Besides the haystack, a single instance of a needle is given. It is assumed that members of the needle class are similar according to an unknown needle characterization. The goal is to find the needle records hidden in the haystack. This p...

Finding the needle in the haystack. The dereplication of natural product extracts*

Dereplication is an integral part of the early stages of the drug discovery process, where there is a focus on the rapid identi®cation of novel, biologically active metabolites from natural sources. We describe a strategy and a system for dereplication and offer several examples of its successful appplication.

Finding a Needle in a Haystack: An Image Processing Approach

Image segmentation (also known as object/edge detection) is the process of dividing an image into its constituent parts using information about the boundaries between objects, edges within objects, variations in intensity, et cetera. Often, the human eye can easily recognize salient information from an image; however, background variations in intensity, noise and other degradations, and other h...