Disseminating Query Results in Wireless Systems
. This document describes the planned activities in WINNER II to disseminate and exploit the knowledge gained during the current project runtime. This description is not seen as final and comprehensive, in contrary, a continuous process of opportunity analysis will assure the best possible result exploitation. Keyword list: dissemination, know-how

title: VDBMS: A Solution to Integrating Heterogeneous Data Sources

title: Efficient Group Pattern Mining Using Data Summarization
abstract: In group pattern mining, we discover group patterns from a given user movement database based on their spatio-temporal distances. When both the number of users and the logging duration are large, group pattern mining algorithms become very inefficient. In this paper, we therefore propose a spherical location summarization method to reduce the overhead of mining valid 2-groups. In our experiments, we show that our group mining algorithm using summarized data may require much less execution time than that using non-summarized data.

title: Mining Mobile Group Patterns: A Trajectory-Based Approach
abstract: In this paper, we present a group pattern mining approach to derive the grouping information of mobile device users based on a trajectory model. Group patterns of users are determined by distance threshold and minimum time duration. A trajectory model of user movement is adopted to save storage space and to cope with untracked or disconnected location data. To discover group patterns, we propose ATGP algorithm and TVG-growth that are derived from the Apriori and VG-growth algorithms respectively.

title: On the discovery of process models from their instances
abstract: A thorough understanding of the way in which existing business processes currently practice is essential from the perspectives of both process reengineering and workflow management. In this paper, we present a framework and algorithms that derive the underlying process model from past executions. The process model employs a directed graph for representing the control dependencies among activities and associates a Boolean function on each edge to indicate the condition under which the edge is to be enabled. By modeling the execution of an activity as an interval, we have developed an algorithm that derives the directed graph in a faster, more accurate manner. This algorithm is further enhanced with a noise handling mechanism to tolerate noise, which frequently occur in the real world. Experimental results show that the proposed algorithm outperforms the existing ones in terms of efficiency and quality.

title: Consulting past exceptions to facilitate workflow exception handling
abstract: In this paper, we propose an architecture model that deals with both expected and unexpected exceptions in the context of workflow management. Expected exceptions and their handling approaches are specified by ECA rules, while cases of unexpected exceptions are characterized by their features and resolution approaches. The handling of unexpected exceptions is then assisted by the system providing information about how recent similar cases were resolved. The ways in which the previous exception cases were handled provides useful information in determining how to handle the current one. Quantifying the similarity of exception cases is described, and three algorithms for efficiently searching for similar exception cases are proposed and evaluated both theoretically and by experimenting with synthetic data sets. D 2002 Elsevier B.V. All rights reserved.

title: Clustering High Dimensional Massive Scientific Datasets
abstract: Many scientific applications can benefit from an efficient clustering algorithm of massively large high dimensional datasets. However most of the developed algorithms are impractical to use when the amount of data is very large. Given N objects each defined by an M-dimensional feature vector, any clustering technique for handling very large datasets in high dimensional space should run in time O(MN) at best, and O(MN log N) in the worst case, using no more than O(MN) storage, for it to be practical. We introduce a hybrid algorithm, called HyCeltyc, for clustering massively large high dimensional datasets in O(MN) time which is linear in the size of the data. HyCeltyc, which stands for HybridCell Density Clustering method, combines a cell-density based algorithm with a hierarchical agglomerative method to identify clusters in linear time. The main steps of the algorithm involve sampling, dimensionality reduction, selection of significant features on which to cluster the data and a grid-based clustering algorihm that is linear in the data size.

title: Minimum spanners of butterfly graphs
abstract: Given a connected graph G, a spanning subgraph G′ of G is called a t-spanner if every pair of two adjacent vertices in G has a distance of at most t in G′. A t-spanner of a graph G is minimum if it contains minimum number of edges among all t-spanners of G. Finding minimum spanners for general graphs is rather difficult. Most of previous results were obtained for some particular graphs, for example, butterfly graphs, cube-connected cycles, de Bruijn graphs, Kautz graphs, complete bipartite graphs, and permutation graphs. The butterfly graphs were originally introduced as the underlying graphs of FFT networks which can perform the fast Fourier transform (FFT) very efficiently. In this paper, we successfully construct most of the minimum t-spanners for the k-ary r-dimensional butterfly graphs for 2 ≤ t ≤ 6 and t = 8.