Temporal Probabilistic Concepts from Heterogeneous Data Sequences [chapter]

Sally McClean, Bryan Scotney, Fiona Palmer
2002 Lecture Notes in Computer Science  
We consider the problem of characterisation of sequences of heterogeneous symbolic data that arise from a common underlying temporal pattern. The data, which are subject to imprecision and uncertainty, are heterogeneous with respect to classification schemes, where the class values differ between sequences. However, because the sequences relate to the same underlying concept, the mappings between values, which are not known ab initio, may be learned. Such mappings relate local ontologies, in
more » ... form of classification schemes, to a global ontology (the underlying pattern). On the basis of these mappings we use maximum likelihood techniques to handle uncertainty in the data and learn local probabilistic concepts represented by individual temporal instances of the sequences. These local concepts are then combined, thus enabling us to learn the overall temporal probabilistic concept that describes the underlying pattern. Such an approach provides an intuitive way of describing the temporal pattern while allowing us to take account of inherent uncertainty using probabilistic semantics. Examples of possible mappings are presented in Table 3 , where L, M and N are the global labels that we are learning. The global sequence (L, L, L, M, M, M, M, N, N) could therefore characterise the temporal behaviour of the global ontology underpinning these data. We note that these mappings are not exact in all cases; e.g., in Sequence 1 the ontology is coarser than the underlying global ontology, and neither Sequence 1 nor Sequence 4 exactly map onto the global sequence. This highlights the
doi:10.1007/3-540-46019-5_15 fatcat:c3ovf7ul3jaqthittonjwkecy4