Sequencing geographical data for efficient query processing on air in mobile computing.

Three cost models are derived to measure Data Broadcast Wait (DBW), Data Access Time in the multiplexing scheme (ATDataMul) where both data and indices are broadcast in the same channel, and Data Access Time in the separate channel scheme (ATDataSep) where data and indices are broadcast in two separate channels. Hypergraph representations are used to represent the spatial relationships of both point data and graph data. The broadcast data placement problem is then converted to the graph layout problem. A framework for classifying ordering heuristics for different types of geographical data is presented. A low-polynomial cost approximation graph layout method is used to solve the DBW minimization problem. Based on the proven monotonic relationship between ATData Sep and DBW, the same approximation method is also used for AT DataSep optimization. A novel method is developed to optimize ATDataMul. Experiments using both synthetic and real data are conducted to evaluate the performance of the ordering heuristics and optimization methods. The results show that R-Tree traversal ordering heuristic in conjunction with the optimization methods is effective for sequencing point data for spatial range query processing, while graph partition tree traversal ordering heuristic in conjunction with the optimization methods is suitable for sequencing graph data for network path query processing over air.Geographical data broadcasting is suitable for many large scale dissemination-based applications due to its independence of number of users, and thus it can serve as an important part of intelligent information infrastructures for modern cities. In broadcast systems, query response time is greatly affected by the order in which data items are being broadcast. However, existing broadcast ordering techniques are not suitable for geographical data because of the multi-dimension and rich semantics of geographical data. This research develops cost models and methods for placing geographical data items in a broadcast channel based on their spatial semantics to reduce response time and energy consumption for processing spatial queries on point data and graph data

[[alternative]]Data Allocation on Wireless Broadcast Channels for Efficient Query Processing using Apriori Algorithms

[[abstract]]　　電腦科技進步，行動計算環境技術亦趨成熟，個人行動通訊設備的發展與普及，廣播資料將會被廣泛擴大的應用在網域。在本論文中，將資料庫發展由傳統資料庫、行動資料庫、廣播資料庫，探討資料庫演進以及資料探勘理論中關聯法則最主要的演算法之一Apriori演算法來探討無線廣播資料頻道的有效分配，使用者從廣播資料上執行複雜查詢時，可以呈現最佳查詢處理位置，將查詢的資料庫放在靠近每一個廣播頻道的位置，可以減少處理複雜的查詢存取時間與查詢處理的位置。實驗證明我們的方法與Random比，快10 %。　[[abstract]]　　As computer technology is continue progress, the environment of mobile computing is more mature.The personal mobile communication equipment is development and popular, and the broadcast data will be widespread use on Internet. In this paper, we will probe into the evolution of database form its development: tradition database, mobile database, and broadcast database. And use Apriori algorithms, the one of the algorithms of data mining association rule to research the efficient of data allocation on wireless broadcast channels. When user make a complex query into the broadcast data, we can show the best position in the query processing. Put the query data near to each broadcast channel position, can reduce the access time of a complex query and query processing position. The result of experiment improve that our method is 10% faster than random.