Mining closed patterns in multi-sequence time-series databases

In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms.     

Two-stage ensemble paging strategy for cellular communicationsystems

Instead of per user basis, we propose a simple paging strategy based on the consideration of ensemble paging channel resource of each cell. Using only the information of the last known locations of mobile subscribers, the proposed paging scheme under a maximum delay constraint can improve the system performance (discovery rate, blocking rate, and average paging delay) significantly in the heavy load