10019293
Substructure similarity search in graph databases
Advanced database systems face a great challenge raised by the emergence of massive, complex structural data in bioinformatics, chem-informatics, and many other applications. The most fundamental support needed in these applications is the efficient search of complex structured data. Since exact matching is often too restrictive, similarity search of complex structures becomes a vital operation that must be supported efficiently.In this paper, we investigate the issues of substructure similarity search using indexed features in graph databases. By transforming the edge relaxation ratio of a query graph into the maximum allowed missing features, our structural filtering algorithm, called Grafil, can filter many graphs without performing pairwise similarity computations. It is further shown that using either too few or too many features can result in poor filtering performance. Thus the challenge is to design an effective feature set selection strategy for filtering. By examining the effect of different feature selection mechanisms, we develop a multi-filter composition strategy, where each filter uses a distinct and complementary subset of the features. We identify the criteria to form effective feature sets for filtering, and demonstrate that combining features with similar size and selectivity can improve the filtering and search performance significantly. Moreover, the concept presented in Grafil can be applied to searching approximate non-consecutive sequences, trees, and other complicated structures as well.
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.
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/
3443494
Mining Compressed Frequent-Pattern Sets
A major challenge in frequent-pattern mining is the sheer size of its mining results. In many cases, a high min_sup threshold may discover only commonsense patterns but a low one may generate an explosive number of output patterns, which severely restricts its usage.In this paper, we study the problem of compressing frequent-pattern sets. Typically, frequent patterns can be clustered with a tightness measure δ (called δ-cluster), and a representative pattern can be selected for each cluster. Unfortunately, finding a minimum set of representative patterns is NP-Hard. We develop two greedy methods, RPglobal and RPlocal. The former has the guaranteed compression bound but higher computational complexity. The latter sacrifices the theoretical bounds but is far more efficient. Our performance study shows that the compression quality using RPlocal is very close to RPglobal, and both can reduce the number of closed frequent patterns by almost two orders of magnitude. Furthermore, RPlocal mines even faster than FPClose[11], a very fast closed frequent-pattern mining method. We also show that RPglobal and RPlocal can be combined together to balance the quality and efficiency.
2286606
Scene-Level Analysis for Tennis Sports Video using Weighted Linear Combination of Visual Cues
We present a scene-level analysis system that takes TV broadcasted tennis footage as input and produces a behavior analysis of the moving-players in the scene. To achieve this functionality, our algorithm relies on two modular blocks. The first one detects and tracks a number of key objects in the image domain, like moving-players and the playing-field. Afterwards, a camera-calibration algorithm is applied that uses the lines of the court as a reference and transforms image coordinates to physical positions to compute the camera parameters. The second block firstly models several important events of a tennis game, such as service and net-approach, based on four real-world visual features provided by the first block. This paper proposes a new improved model, since it weights the importance of each visual cue to different events, rather than providing a simple combination of these four visual cues as was done in previous work. Based on the new model, we can accurately determine what kind of event the current input frame belongs to. Furthermore, we detect the start time and end time of each event using a simple but efficient temporal filter. Our proposed system is capable of classifying each tennis play into three semantic categories, which are popular and familiar to most viewers. This paper presents details of the system, together with results on a number of tennis video sequences.
14723563
Assessing security properties of software components: a software engineer's perspective
The paper proposes an assessment scheme for the security properties of software components. The proposed scheme consists of three stages: (i) a system-specific security requirement specification of the enclosing application; (ii) a component-specific security rating; and (iii) an evaluation method for the scored security properties of the candidate component. The assessment scheme ultimately provides a numeric score indicating a relative strength of the security properties of the candidate component. The scheme is partially based on ISO/IEC 15408, the Common Criteria for Information Technology Security Evaluation (CC) and the Multi-Element Component Comparison and Analysis (MECCA) model. The scheme is flexible enough for software engineers to use in order to get a first-hand preliminary assessment of the security posture of candidate components
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.
18395453
Content-Based Model Template Adaptation and Real-Time System for Behavior Interpretation in Sports Video
In this paper, we present a real-time sports analysis system, which not only recognizes the semantic events, but also concludes the behavior, like player's tactics. To this end, we propose an advanced multiple-player tracking algorithm, which addresses two improvements on practical problems: (1) updating of the player template so that it remains a good model over time, and (2) adaptive scaling of the template size depending on the player motion. In this algorithm, we obtain the initial locations of players in the first frame. The tracking is performed by considering both the kinematic constraints of the player and the color distribution of appearance, thereby achieving promising results. We demonstrate the performance of the proposed system by evaluating it for double tennis matches where the player count and the resulting occlusions are challenging.
17887061
Popularity Biased Hybrid Search in P2P Systems
Hybrid search, which combines the flooding and DHT techniques, has been employed for efficient content-locating in peer-to-peer (P2P) systems. Choosing a flooding or a DHT subjects to the population of desired data. However, existing studies summarizes the popularity only based on local information, or does not take into account the dynamic factor of P2P systems. Thus, the users suffer from long search latency when performing hybrid search due to inaccurate estimations of the resource popularity. In this paper, we propose a popularity biased hybrid search method in systems. Our model allows the P2P systems dynamically detect the content popularity, and accurately make a decision of search methods. In this way, our work effectively reduces the query traffic cost and response time. The result our trace-driven simulations report shows that our model outperforms existing approaches in search efficiency