In this book, we proposed database solutions for some of the major challenges in mining and managing time-series data. In particular, a framework for mining heterogeneous time-series, and a framework for online summarization and analysis of dynamic time- series data. A general framework, Information Mining to acquire information from heteregenous and potentially high dimensional time-series data is proposed. The framework consists of two major steps: first, significant, clean, and homogeneous subsets of data are identified and analyzed, then the information gathered in the first step is further refined by identifying common (or distinct) patterns over the results of mining of the subsets. In a multiple data stream application, a new element for each data sequence is periodically inserted into the database. The data is usually compressed due to storage limitations, and it is reconstructed at the time of query. An online technique, PQ-Stream, which provides a high quality reconstruction is presented. It is significantly outperforms the current techniques for a wide variety of query types on both synthetic and real data sets.