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/
43402198
Mining Closed Relational Graphs with Connectivity Constraints
Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In a relational graph, each node represents a distinct entity while each edge represents a relationship between entities. Various algorithms were developed to discover interesting patterns from a single relational graph (Z. Wu et al., 1993). However, little attention has been paid to the patterns that are hidden in multiple relational graphs. One interesting pattern in relational graphs is frequent highly connected subgraph which can identify recurrent groups and clusters. In social networks, this kind of pattern corresponds to communities where people are strongly associated. For example, if several researchers co-author some papers, attend the same conferences, and refer their works from each other, it strongly indicates that they are studying the same research theme.
7180776
A survey of architecture design rationale
Many claims have been made about the consequences of not documenting design rationale. The general perception is that designers and architects usually do not fully understand the critical role of systematic use and capture of design rationale. However, there is to date little empirical evidence available on what design rationale mean to practitioners, how valuable they consider it, and how they use and document it during the design process. This paper reports a survey of practitioners to probe their perception of the value of design rationale and how they use and document the background knowledge related to their design decisions. Based on 81 valid responses, this study has discovered that practitioners recognize the importance of documenting design rationale and frequently use them to reason about their design choices. However, they have indicated barriers to the use and documentation of design rationale. Based on the findings, we conclude that further research is needed to develop methodology and tool support for design rationale capture and usage. Furthermore, we put forward some specific research questions about design rationale that could be further investigated to benefit industry practice.
1645447
A framework for an active interface to characterise compositional security contracts of software components
This paper presents a framework for constructing compositional security contracts (CsC) based on the security property exposed by the atomic component. The framework uses interface structure of components in order to determine the CsC of software components. An active interface provides the component a basis for reasoning and assessing a component's suitability to meet certain security requirements of a particular application. Based on the security information available from the component interface, an active interface can reason whether the candidate component meets the security requirements for an envisaged systemwide application. Any security mismatches or discrepancies between components can be identified by the participating components before an actual composition takes place. Exposing the security properties of software components can be the basis for a trust relationship among components, and the exposed security could affect the underlying security of the enclosing system.
6059661
Mining frequent patterns without candidate generation
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 prolific 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 highly condensed, much smaller data structure, 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.
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.
1978753
A security characterisation framework for trustworthy component based software systems
This paper explores how to characterize security properties of software components, and how to reason about their suitability for a trustworthy compositional contract. Our framework provides an explicit opportunity for software composers as well as software components to test a priori security properties of software components in a system composition. The proposed framework uses logic programming as a tool to represent security properties of atomic components and reason about their compositional matching with other components. This enables software components as well as composers to "test" possible matches and mismatches between the security properties of the candidate components and the security requirements of the enclosing applications systems.