We investigate patterns over information maps. Such patterns can represent information changes (e.g., in time or space) across information maps. Any map is defined by some transition relation on states. Each state is a pair consisting of a label and information related to the label. Introduced concepts are illustrated by examples. We also discuss searching problems for relevant patterns extracted from data stored in information maps. Some patterns can be expressed by temporal formulas. Then, searching is reduced to searching for relevant temporal formulas. We generalise association rules over information systems to association rules over information maps. Approximate reasoning methods based on information changes are important for many applications (e.g., related to spatio-temporal reasoning). We introduce basic concepts for approximate reasoning about information changes across information maps. We measure degree of changes using information granules. Any rule for reasoning about information changes specifies how changes of information granules from the rule premise influence changes of information granules from the rule conclusion. Changes in information granules can be measured, e.g., using expressions analogous to derivatives. Illustrative examples are also presented.
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