I've usually found, at least with batch classes, performance issues when we get into the area of 150-200 characteristics in a class. Things have improved with newer databases and faster machines but I think you really need to rethink the need of this.
With batch classification, when a batch is classified, you get a record created in your database for each characteristic in the batch. That means potentially 5000 records for every batch, even if there is no value maintained for a characteristic. If you record values in only 8 of the 5000 characteristics, you still get 5000 records created.
When you use batch determination, batch deviation, etc.. the system usually retrieves all characteristic records regardless of which ones are being searched against or used. So to do batch determination, if you have 20 batches in inventory, the system first retrieves the 100,000 characteristic records for these 20 batches.
So I'd really suggest looking at the need for this. Unfortunately I have seen several implementations where they create one batch class and just add ever characteristic they create to it. Simply because they don't want to do the work to do a proper classification design. Then years later, they get to this type of situation and have a major cluster$%@& to unravel.
Class characteristics are measurable features of a specimen that indicate a restricted group source. On bullets, the class characteristics are the rifling specifications of the barrel from which the bullet was fired.
All individual objects possess three basic characteristics -- identity, state and behavior. Understanding these characteristics is crucial to knowing how objects and object-oriented logic work. Identity means that each object has its own object identifier and can be differentiated from all other objects.
A class describes the variables, properties, procedures, and events of an object. Objects are instances of classes; you can create as many objects as you need once you have defined a class. To understand the relationship between an object and its class, think of cookie cutters and cookies.
Class characteristics are properties of evidence that can be associated only with a group and never with a single source. Examples include paint chips, random glass fragments, blood, and artificial fibers.
Class characteristics set specific norms and standards for the events' subsequent development and aid to launch an investigation. Several important advantages and supporters come from individual traits.
A characteristic is a property that serves to describe and differentiate between objects. Examples: length, color, weight, and so on. A class groups together similar objects described by the same characteristics.
A class can be thought of as a "type", with the objects being a "variable" of that type. Multiple objects, or instances of a class can be created in a single HLU program, just as you declare multiple variables of the same type in any program.
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