String alignment for automated document versioning

Woon, Wei Lee; Wong, K. Daniel

doi:10.1007/s10115-008-0130-x

Cited by 6 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Multimedia : Multimodal data mining in a multimedia database is a challenging topic in data mining research [16,5,22,12,3,36,15]. Multimedia data may consist of data in different modalities, such as digital images, audio, video, and text data.…”

Section: Example Domains and Applicationsmentioning

confidence: 99%

D-Search: an efficient and exact search algorithm for large distribution sets

2010

View full text Add to dashboard Cite

Distribution data naturally arise in countless domains, such as meteorology, biology, geology, industry and economics. However, relatively little attention has been paid to data mining for large distribution sets. Given n distributions of multiple categories and a query distribution Q, we want to find similar clouds (i.e., distributions), to discover patterns, rules and outlier clouds. For example, consider the numerical case of sales of items, where, for each item sold, we record the unit price and quantity; then, each customer is represented as a distribution of 2-d points (one for each item he/she bought). We want to find similar users, e.g., for market segmentation or anomaly/fraud detection. We propose to address this problem and present D-Search, which includes fast and effective algorithms for similarity search in large distribution datasets. Our main contributions are (1) approximate KL divergence, which can speed up cloud-similarity computations, (2) multi-step sequential scan, which efficiently prunes a significant number of search candidates and leads to a direct reduction in the search cost. We also introduce an extended version of D-Search : (3) time-series distribution mining, which finds similar subsequences in time-series distribution datasets. Extensive experiments on real multi-dimensional datasets show that our solution achieves a wall clock time up to 2,300 times faster than the naive implementation without sacrificing accuracy.

show abstract

Section: Example Domains and Applicationsmentioning

confidence: 99%

D-Search: an efficient and exact search algorithm for large distribution sets

2010

View full text Add to dashboard Cite

show abstract

“…For example, it may be used to identify identical concepts of two ontologies if they have a similar name. The reader can see (Navarro, 2001) for a classical approach to the problem or (Woon and Wong, 2009) for a more modern point of view. Fig.…”

Section: Problem Statementmentioning

confidence: 99%

Evaluation of two heuristic approaches to solve the ontology meta-matching problem

Martinez‐Gil¹,

Aldana‐Montes²

2017

Preprint

View full text Add to dashboard Cite

. Evaluation of two heuristic approaches to solve the ontology meta-matching problem. Knowledge and Information Systems (KAIS), Springer, 2011, 26 (2) Abstract. Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies: Ontology Meta-Matching which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the Ontology Meta-Matching problem. The first one is called MaSiMe, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called GOAL and is based on a Genetic Algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

show abstract