The described infrastructure of a Grid has been designed for a broad variety of services that can be grouped into five different aims:
Computational services have been described as first applications of a Grid. They solve tasks that require high computational power, for example to solve recursive formulas. They are in use for of high energy experiments, or astrophysics. In its simplest manner, one (or several) of the distributed supercomputers take the computational task as long as they are not busy with or overloaded by other tasks. Once this happens, the task and its computational stage are transferred to other included supercomputers, etc. as long as the task is not finished. A priority set of different tasks can stop the computation of an individual task and save its present stage as long as other, more important tasks have not been finished. Examples of computational Grids include: NASA IPG , the World Wide Grid (Buyya R. The World-Wide Grid. http://www.buyya.com/ecogrid/wwg/) , and the NSF Tera-Grid. (http://www.teraGrid.org/) . Computational services would be appropriate for detection of regions of interest, image segmentation and object identification tools as well as for image comparison (block comparison) .
Data services are implemented in several search machines, and offer secure access to distributed datasets. They manage all functions that are used in conventional libraries such as data access, retrieval, storage, replication, or search for data in catalogues of individual or distributed libraries. A more simple structure has been implemented by so-called links, or data-Grids, that are used in the area of high-energy physics [18, 19] or drug design . http://www.buyya.com/vlab/. Data services would be appropriate to set up classification of diseases, image labeling, or identification of objects, structures, and textures in virtual microscopy.
Application services represent the next higher level and give access to remote software, libraries and Web services. They provide the adequate formulas to be applied on implemented data sets, for example a databank of parameters etc. to fulfill this task. In tissue–based diagnosis, the EAMUS™ [12, 13, 20] can be considered as a simple, one node implementation of this service. A well known Grid application service is, for example, created by NetSolve . In virtual microscopy several tasks could be performed with application services, especially diagnosis oriented computations of image standardization, features, and regions of interest.
Information services are at an advanced level of application services. They put into relationship data of computational information, and/or application services and present the obtained information. In virtual microscopy, a simple implementation could be created by combining image measurements (for example provided by EAMUS™ services) with an existing telepathology information system such as UICC-TPCC, or iPATH. Another more common example of low-level information services are Meta Data, i.e. a context oriented manner to present, store, access, share, and maintain information. Information services provides also the EU–sponsored Virolab Grid, a project that addresses the problem of HIV drug resistance and offers the integration of biomedical information, advanced applications, patients’ data, and intelligent literature access (http://www.gridwisetech.com/virolab).
Knowledge services are the most advanced Grid services from the viewpoint of informatics. They are designed to improving with the algorithms of acquiring, using, retrieving, publishing, or maintaining knowledge. Knowledge is considered as information applied to achieve a goal, solve a problem, or execute a decision. A characteristic example is data mining for automatically building a new knowledge. In virtual microscopy it would be an appropriate tool in automated screening and analyzing virtual slides prior to be viewed by the pathologists, or to automatically inform the pathology laboratory about additional investigations in data needed to evaluate a definite diagnosis (immunohistochemical stains, gene analysis, etc.) [8, 22–25].