Author name cluster

PAUL LENG

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

KER Journal 2006 Journal Article

Partitioning strategies for distributed association rule mining

Frans Coenen
PAUL LENG

In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.

Details DOI

TCS Journal 2001 Journal Article

Efficient web searching using temporal factors

Artur Czumaj
Ian Finch
Leszek Ga̧sieniec
Alan Gibbons
PAUL LENG
Wojciech Rytter
Michele Zito

We study the issues involved in the design of algorithms for performing information gathering more efficiently, by taking advantage of anticipated variations in access times in different regions at different times of the day or week. We look at the problem theoretically, as a generalisation of single processor sequencing with release times and deadlines, in which performance times (lengths) of the tasks can change in time. The new problem is called Variable Length Sequencing Problem (VLSP). We show that although the decision version of VLSP seems to be intractable in the general case, it can be solved optimally for lengths 1 and 2. This result opens the possibility of practicable algorithms to schedule searches efficiently when expected access times can be categorised as either slow or fast. Some algorithms for more general cases are examined and complexity results derived.

Details DOI