In this paper, we focus on the issue of the m-closest keywords (mCK) query over spatial data in the Web. The mCK query is a problem to find the optimal set of records in the sense that they are the spatially-closest records that satisfy m user-given keywords. The mCK query was proposed by Zhang et al[1]. They assumed a specialized R*-tree to store all records and proposed an Apriori-based enumeration of MBR-combinations. However, this assumption of the prepared R*-tree is not always applicable; Twitter or Flickr provides only records having position information without any prepared data-partitioning. Many services like Google Maps only provide grid partitioning at most. Thus, in this paper, we do not expect any prepared data-partitioning, but assume that we create a grid partitioning from necessary data only when an mCK query is given. Under this assumption, we propose a new search-strategy termed Diameter Candidate Check (DCC), and show that DCC can efficiently find a better set of grid-cells at an earlier stage of search, thereby reducing search space greatly.

The rapid growth of Internet access from mobile users has emphasised the importance of location specific information on the Web. A unique Web service called Mobile Info Search (MIS) from NTT Laboratories gathers information and provides location aware search facilities. We performed association rule mining and sequence pattern mining against an access log which was accumulated at the MIS site in order to get insight into the behavior of mobile users regarding spatial information on the Web. Details of the Web log mining process and the rules we derived are reported in this paper.

PC clusters have been studied intensively for next-generation large scale parallel computers. ATM technology is a strong candidate as a de facto standard of high speed communication networks. Therefore an ATM connected PC cluster is a very promising platform from the cost/performance point of view, as a future high performance computing environment. An ATM connected PC cluster consisting of 100 PCs is reported, and characteristics of a transport layer protocol for the PC cluster are evaluated. Point-to-point communication performance is measured and discussed when a TCP window size parameter is changed. Retransmission caused by cell loss at the ATM switch is analyzed, and parameters of the retransmission mechanism suitable for parallel processing on the large scale PC cluster are clarified. From the viewpoint of applications, data intensive applications such as data mining and ad-hoc query processing in databases are considered to be very important for massively parallel processors, in addition to conventional scientific calculations. Thus, investigating the feasibility of such applications on an ATM connected PC cluster is quite meaningful. Parallel data mining is implemented and evaluated on the cluster. The default TCP protocol cannot provide good performance, since a lot of collisions happen during all-to-all multicasting executed on the large scale PC cluster. Using TCP parameters according to the proposed optimization, sufficient performance improvement is achieved for parallel data mining on 100 PCs.

Association rule mining recently attracted strong attention. Usually, the classification hierarchy over the data items is available. Users are interested in generalized association rules that span different levels of the hierarchy, since some-times more interesting rules can be derived by taking the hierarchy into account. In this paper, we propose the new parallel algorithms for mining association rules with classification hierarchy on a shared-nothing parallel machine to improve its performance. Our algorithms partition the candidate itemsets over the processors, which exploits the aggregate memory of the sys-tem effectively. If the candidate itemsets are partitioned without considering classification hierarchy, both the items and its all the ancestor items have to be transmitted, that causes prohibitively large amount of communications. Our method minimizes interprocessor communication by consid-ering the hierarchy. Moreover, in our algorithm, the avail-able memory space is fully utilized by identifying the fre-quently occurring candidate itemsets and copying them over all the processors, through which frequent itemsets can be processed locally without any communication. Thus it can effectively reduce the load skew among the processors. Sev-eral experiments are done by changing the granule of copying itemsets, from the whole tree, to the small group of the fre-quent itemsets along the hierarchy. The coarser the grain, the easier the control but it is rather difficult to achieve the sufficient load balance. The finer the grain, the more com-plicated the control is required but it can balance the load quite well. We implemented proposed algorithms on IBM SP-2. Per-formance evaluations show that our algorithms are effective for handling skew and attain sufficient speedup ratio. 1