ProCKSI

= Data Management and Representation =

== Definitions ==

 * '''User''' (to be implemented in the future):

   * Represented by a unique email-address

   * Authentication to gain access to user data (requests, personalised settings)

   * Manage multiple requests

 * '''Request''':

   * Unique handle for the combination of a dataset, tasks, and request parameters

   * Request parameters: e.g. request description, settings for notification by email

 * '''Task''':

   * Something to be performed with the given dataset[[br]]

     e.g. calculation of PDB structure pictures (1D), or comparison of pairs of proteins with a given similarity method (2D)

   * Task parameters: e.g. parameters for each comparison method, output parameters for picture generation, ...

 * '''Job''':

   * Everything that lives in a queue[[br]]

     e.g. local queue (ProCKSI cluster), remote queue (University cluster), external queue (web service, grid)

   * Currently, a job is equal to a task:[[br]]

     e.g. task = pairwise comparison of the proteins in the ''entire'' dataset with ''several'' given similarity methods[[br]]

     jobs = ''separate'' jobs calculating all pairwise comparisons of the entire dataset with ''one'' similarity method

   * Future plans:[[br]]

     Divide 3D problem space into subsets of datasets and methods, each subset being an independent job[[br]]

     See next section for further details on the ''3D Problem Space''

 * '''Dataset''':

   * Currently: Collection of PDB structures, previously calculated similarity matrices 

   * Future plans: Previously calculated similarity matrices should be uploaded in a post-processing step, not in a pre-processing step (ticket:28)

 * '''Results''':

   * Currently, entire similarity matrices of different sources 

   * Future plans: Generate similarity matrices directly from single pairwise comparison results stored in the database

== The 3D Problem and Solution Spaces ==

 * '''Problem Space''':[[br]]

   The problem space for an all-against-all comparison of a dataset of P protein structures using M different similarity comparison methods can be represented a 3D cube: [[br]]

   x: Dataset: list of proteins[[br]]

   y: Dataset: list of proteins[[br]]

   z: Tasks: list of similarity comparison methods

 * '''Partitionig the Problem Space''':[[br]]

   For a most efficient calculation of all cells in the 3D problem space, it can be subdivided into sub-cubes, which are called jobs when placed into the queue of a queing system. Examples:[[br]]

   a. Comparison of ''one pair of proteins'' using ''one method'' in the task list => PxPxM jobs, each performing 1 comparison

   b. All-against-all comparison of the ''entire dataset'' with ''one one method'' => M jobs, each performing PxP comparisons

   c. Comparison of ''one pair of proteins'' using ''all methods'' in the task list => PxP jobs, each performing M comparisons

   d. Intelligent partitioning of the 3D problem space, comparing a subset of proteins with a subset of methods

 * '''Solution Space''':[[br]]

   Each similarity comparison ''methods'' can provide several similarity ''measures''[[br]]

   For one slice in the 3D problems space using one particular method, we might get several slices in the 3D solution space providing several measures