43532686
AIM: Approximate Intelligent Matching for Time Series Data
Time-series data mining presents many challenges due to the intrinsic large scale and high dimensionality of the data sets. Subsequence similarity matching has been an active research area driven by the need to analyse large data sets in the financial, biomedical and scientific databases. In this paper, we investigate an intelligent subsequence similarity matching of time series queries based on efficient graph traversal. We introduce a new problem, the approximate partial matching of a query sequence in a time series database. Our system can address such queries with high specificity and minimal time and space overhead. The performance bottleneck of the current methods were analysed and we show our method can improve the performance of the time series queries significantly. It is general and flexible enough to find the best approximate match query without specifying a tolerance Ɛ parameter.
21192
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.
15976095
Operational management contracts for adaptive software organisation
As modern computing environments become more open, distributed and pervasive, the software we build for those dynamic environments need to become more adaptable and adaptive. We have previously introduced the ROAD framework for creating flexible and adaptive software structures. This framework is built on a distinction between functional and management roles. Management roles participate in contracts that regulate the global-flow of control through a structure of objects and roles. This paper shows how these operational-management contracts can be defined. Such contracts specify the permissible interactions between objects playing functional roles within an organisational structure. Association aspects are shown to have the expressiveness needed to represent such management contracts.
7273329
Object Organisation in Software Environments for Formal Methods
Software development by formal methods involves an overwhelming amount of technical and managerial detail. Systematic organisation of this information in a method's support environment is an important engineering concern. In this paper, we introduce a model for object organisation in software environments for formal methods, with particular emphasis on easy construction, modification, review and reuse of software objects. We demonstrate how the model can be instantiated to individual methodologies to obtain the object organisation architectures for their support environments.
15978422
TruGrid: a self-sustaining trustworthy grid
In typical distributed environments, there are two parties: consumers and providers. Consumers have computational jobs while may lack of computational resources. Providers have relatively underutilized resources. With the rapid advancement in high-speed networks, how to enable the effective and secure interaction between consumers and providers has become increasingly important. Because of the distributed ownership of resources, however, it is a very challenging problem. We propose the TruGrid, a self-sustaining trustworthy grid, to solve it. The TruGrid is a novel loosely connected grid system, which guarantees the incentive of every participant and therefore it is a self-sustaining system. We also identify two security challenges arising in the TruGrid, i.e., free-rider and boaster. The proposed trustworthiness management and the penalty model successfully overcome the challenges. Simulation results demonstrate that the TruGrid is a promising actuator to enable the interaction between autonomous consumers and providers.
17959434
Graph indexing based on discriminative frequent structure analysis
Graphs have become increasingly important in modelling complicated structures and schemaless data such as chemical compounds, proteins, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via indices. In this article, we investigate the issues of indexing graphs and propose a novel indexing model based on discriminative frequent structures that are identified through a graph mining process. We show that the compact index built under this model can achieve better performance in processing graph queries. Since discriminative frequent structures capture the intrinsic characteristics of the data, they are relatively stable to database updates, thus facilitating sampling-based feature extraction and incremental index maintenance. Our approach not only provides an elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit from data mining, especially frequent pattern mining. Furthermore, the concepts developed here can be generalized and applied to indexing sequences, trees, and other complicated structures as well.
101070
Mining coherent dense subgraphs across massive biological networks for functional discovery
MOTIVATION
The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks.


RESULTS
We developed a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. Compared with previous methods, our approach is scalable in the number and size of the input graphs and adjustable in terms of exact or approximate pattern mining. Applying CODENSE to 39 co-expression networks derived from microarray datasets, we discovered a large number of functionally homogeneous clusters and made functional predictions for 169 uncharacterized yeast genes.


AVAILABILITY
http://zhoulab.usc.edu/CODENSE/