## 2 The Tendency to Increase the Volume and Complexity of Data and Program Collection of the DSS
Frequently, when the transport observatories cover exclusively the needs of a single country, they are called \"national models\" [31; 32; 33]. Early Design Errors and Serious Difficulties Due to the Large Data and Program Collection Characteristics  
In this Subsection, we present some serious difficulties/problems, inherent to large data and program collection characteristics. We will describe below some of the most severe difficulties of this type:  
1. A prevailing design mentality in connection with the first transport DS systems was the following: \"Get all related or even loosely related data first and then organize them\". Practice, though, proved that this design mentality leads to disasters. In fact, it turned out that:This way of data acquisition does not guarantee a precise/consistent, methodological collection of data. On the contrary, the random, heuristic and ad hoc data collection, as a rule, results in a core with data and programs that are non-interoperable, internally inconsistent, practically impossible to organize or update properly and impossible to upgrade. * Due to the aforementioned problems concerning the selected data, the core of the corresponding DSS practically collapses under the weight of the non-organizable data collections. Consequently, this design methodology has been rather quickly abandoned. 2. It must be pointed out that, after this early approach, the designers of a large-scale decision support system apply carefully designed data acquisition methodologies. However, it turns out that in practical cases, there are several characteristics, inherent to the elements of large data collections, which create severe difficulties and problems to the designers of the information system to be used as a platform of the DSS. Some of these characteristics will be presented immediately below. 1. As a rule, in a very large DS system, the data are multi-thematic; hence, it is logical to expect a great variability of definitions, meanings and contents associated with the data. 2. The aforementioned variability is frequently accompanied by a great diversity of forms and formats of the elements of the data collection. This diversity practically always asks for a different handling of each subset of similar data. 3. (Complexity is another serious problem associated with large-scale data collections and the corresponding DS systems. The term \"complexity\" is used in order to describe data with a particularly great number of interconnections, inter-relations and interdependencies. 4. Data polymorphism can easily destroy an arbitrary DS system. For example, the most common computer object in transport, the \"transport link\", may literally have tenths or even hundreds of different definitions, meanings and data contents. Thus, the link between two cities A and B may refer to: * Various road connections, where each road has its own characteristics (e.g., a highway with a given number of lanes with or without tolls, a national road, a secondary street, etc. ); * Various train connections, each one frequently with its own characteristics, such as high-velocity trains (TGV), intercity trains, local trains, commercial trains, etc. ; * Airplane connections, with airports having different connections with the city center; * Seaways, where each way frequently includes ferries, container carriers, bulk carriers, tankers, cruisers, etc. ; * Inland navigation, etc. In addition, the information concerning a single object in the DSS drastically depends on the purpose of the use of this object. This fact makes large-scale decision support system designers employ ad hoc solutions, which usually make upgrades and thematical shifts much more difficult or even impossible. An efficient solution to this problem will be presented later in Sections 5 and 6 of the present work. 1. In many cases, large data and program collections must be distributed in several systems, which may be geographically dispersed.