We present an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure where each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence in that each event has a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed in this paper enables us to efficiently retrieve from the database all subsequences, possibly non-contiguous, that match a given query sequence both by events and by weights. The index method also takes into consideration the non-uniform frequency distribution of events in the sequence data. In addition, our method finds a broad range of applications in indexing scientific data consisting of multiple numerical columns for discovery of correlations among these columns. For instance, indexing a DNA micro-array that records expression levels of genes under different conditions enables us to search for genes whose responses to various experimental perturbations follow a given pattern. We demonstrate, using real-world data sets, that our method is effective and efficient.