Database types, examples

A strict separation of these three types of databases is difficult hence most databases contain a mixture of data types. Therefore the classification given here is based on the predominating data type. For example, the major emphasis of a patent database is on hterature, whereas it also comprises numeric and structural data. Another type is the integrated database, which provides a supplement of additional information, especially bibhographic data. Thus, different database types are merged, a textual database and one or more factual databases. 

At this moment, the NIST Chemistry WebBook and the Beilstein are the two most useful search tools for molecular structure and property relations. There are many more types of properties available in the research literature of interest only to a smaller set of readers these will not be listed in the general-purpose handbooks, but are to be found in specialized books and journals. Our ability to search for such properties in the research literature on the Internet is increasing rapidly, and students should practice doing such searches. Many of the required properties have not been tabulated in a searchable electronic database for example, flammability, toxicity, ozone hole potential, and greenhouse gas potential. Information on health, safety, and the environment that is publicly available on websites is listed in the references. 

The ways mentioned are only the common and obvious ones. Due to the very diverse subject matter of environmental science, there are many other possible ways to classify these databases. For example, environmental databases can be specialized to a specific type of environmental information like ecotoxicity data on chemical substances, or concentration data in environmental media. On the other hand, several databases focus on a specific use of chemical substances, like databases on pesticides or on solvents. ... 

Most of the examples in Table 2 are discussed in the general notes on database types (Sections 5.2 and 5.3). Those not mentioned there are briefly described below ... 

A database management system (DBMS) is used by most LIMS systems for storing data. Examples of commercially available DBMS are DB2, DBASE, Informix, INGRES, ORACLE, and RDB. AH of these DBMS conform to the "relational" model developed by Codd (19). Eigure 3 demonstrates the use of a relational DBMS for storing LIMS data. Here data is grouped by type so customer and analysis requests are stored separately from sets of sample information which are, in turn, stored separately from sets of analysis results. Individual records are linked or related by unique identification data. 

Chemical Abstracts Service. The Chemical Abstracts Service (CAS), a division of the American Chemical Society, has produced Chemical Abstracts (CA) since 1907. Since the demise of Chemisches Zentralblatt and British Chemical Abstracts CA has been the preeminent medium for documenting new pubhcations in the field of chemistry and chemical engineering. CA documents chemical pubHcations of all types. It is not a patent database per se, but its patent component is larger than most databases devoted entirely to patents. Thus, for example, the number of patent references in CA for the years 1991—1993 ranged from 95,500—99,400 per year. 

To allow flexibility, the database manager must also perform point addition or deletion. However, the abihty to create a point type or to add or delete attributes of a point type is not normally required because, unlike other data processing systems, a process control system normally involves a fixed number of point types and related attributes. For example, analog and binary input and output types are required for process I/O points. Related attributes for these point types include tag names, values, and hardware addresses. Different system manufacturers may define different point types using different data structures. We will discuss other commonly used point types and attributes as they appear. 

Figure 2.10 Examples of schematic diagrams of the type pioneered by Jane Richardson. Diagram (a) illustrates the structure of myoglobin in the same orientation as the computer-drawn diagrams of Figures 2.9b-d. Diagram (b), which is adapted from J. Richardson, illustrates the structure of the enzyme triosephosphate isomerase, determined to 2.5 A resolution in the laboratory of David Phillips, Oxford University. Such diagrams can easily be obtained from databases of protein structures, such as PDB, SCOP or CATH, available on the World Wide Web.

Peptidases have been classified by the MEROPS system since 1993 [2], which has been available viatheMEROPS database since 1996 [3]. The classification is based on sequence and structural similarities. Because peptidases are often multidomain proteins, only the domain directly involved in catalysis, and which beais the active site residues, is used in comparisons. This domain is known as the peptidase unit. Peptidases with statistically significant peptidase unit sequence similarities are included in the same family. To date 186 families of peptidase have been detected. Examples from 86 of these families are known in humans. A family is named from a letter representing the catalytic type ( A for aspartic, G for glutamic, M for metallo, C for cysteine, S for serine and T for threonine) plus a number. Examples of family names are shown in Table 1. There are 53 families of metallopeptidases (24 in human), 14 of aspartic peptidases (three of which are found in human), 62 of cysteine peptidases (19 in human), 42 of serine peptidases (17 in human), four of threonine peptidases (three in human), one of ghitamicpeptidases and nine families for which the catalytic type is unknown (one in human). It should be noted that within a family not all of the members will be peptidases. Usually non-peptidase homologues are a minority and can be easily detected because not all of the active site residues are conserved. 

The key is being able to calculate and return the same type of data for a formula as is normally retrieved from a raw material database. A simple example is material cost knowing the cost and concentration of each raw material in a formula, the material cost of the formula is easily calculated. 

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