A Graph-Based Approach for Semantic Process Model Discovery

One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications or user needs. OWL-S is one of the proposals for describing semantic metadata about Web services, which is based on the OWL ontology language. Majority of current approaches for matching OWL-S processes take into account only the inputs/outputs service profile. This chapter argues that, in many situations the service matchmaking should take into account also the process model. We present matching techniques that operate on OWL-S process models and allow retrieving in a given repository, the processes most similar to the query. To do so, the chapter proposes to reduce the problem of process matching to a graph matching problem and to adapt existing algorithms for this purpose. It proposes a similarity measure used to rank the discovered services. This measure captures differences in process structure and semantic differences between input/outputs used in the processes.

Business Process Graphs

Organizations create collections of hundreds or even thousands of business process models to describe their operations. This chapter explains how graphs can be used as underlying formalism to develop techniques for managing such collections. To this end it defines the business process graph formalism. On this formalism it defines techniques for determining similarity of business process graphs. Such techniques can be used to quickly search through a collection of business process graphs to find the graph that is most relevant to a given query. These techniques can be used by tool builders that develop tools for managing large collections of business process models. The aim of the chapter is to provide an overview of the research area of using graphs to do similarity search and matching of business processes.

Graph Applications in Chemoinformatics and Structural Bioinformatics

The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bioinformatics. There is a long history of chemical graph theory dating back to the 1860’s and Kekule’s structural theory. It is natural to regard the atoms of a molecule as nodes and the bonds as edges (2D representations) of a labeled graph (a molecular graph). This chapter will concentrate on the algorithms developed to exploit the computer representation of such graphs and their extensions in both two and three dimensions (where an edge represents the distance in 3D space between a pair of atoms), together with the algorithms developed to exploit them. The algorithms will generally be summarized rather than detailed. The methods were later extended to larger macromolecules (such as proteins); these will be covered in less detail.

Matrix Decomposition-Based Dimensionality Reduction on Graph Data

Graph is a mathematical framework that allows us to represent and manage many real-world data such as relational data, multimedia data and biomedical data. When each data point is represented as a graph and we are given a number of graphs, a task is to extract a few common patterns that capture the property of each population. A frequent graph mining algorithm such as AGM, gSpan and Gaston can enumerate all the frequent patterns in graph data, however, the number of patterns grows exponentially, therefore it is essential to output only discriminative patterns. There are many existing researches on this topic, but this chapter focus on the use of matrix decomposition techniques, and explains the two general cases where either i) no target label is available, or ii) target label is available for each data point. The reuslting method is a branch and bound pattern mining algorithm with efficient pruning condition, and we evaluate its effectiveness on cheminformatics data.

Graph Representation and Anonymization in Large Survey Rating Data

We study the challenges of protecting privacy of individuals in the large public survey rating data in this chapter. Recent study shows that personal information in supposedly anonymous movie rating records is de-identified. The survey rating data usually contains both ratings of sensitive and non-sensitive issues. The ratings of sensitive issues involve personal privacy. Even though the survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. None of the existing anonymisation principles can effectively prevent such breaches in large survey rating data sets. We tackle the problem by defining a principle called (k, e)-anonymity model to protect privacy. Intuitively, the principle requires that, for each transaction t in the given survey rating data T, at least (k - 1) other transactions in T must have ratings similar to t, where the similarity is controlled by e. The (k, e)-anonymity model is formulated by its graphical representation and a specific graph-anonymisation problem is studied by adopting graph modification with graph theory. Various cases are analyzed and methods are developed to make the updated graph meet (k, e) requirements. The methods are applied to two real-life data sets to demonstrate their efficiency and practical utility.

TEDI

Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size and the query answering.

A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs

Due to rapid growth of the Internet and new scientific/technological advances, there exist many new applications that model data as graphs, because graphs have sufficient expressiveness to model complicated structures. The dominance of graphs in real-world applications demands new graph processing techniques to access and analyze large graph datasets effectively and efficiently. Among those techniques, a graph pattern matching problem receives increasing attention, which is to find all patterns in a large data graph that match a user-given graph pattern. In this survey, we review approaches to process such graph pattern queries with a framework of multi joins, which can be easily implemented in relational databases and requires no specialized native storage for graphs. We also discuss the top-k graph pattern matching problem.

An Overview of Graph Indexing and Querying Techniques

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

The Graph Traversal Pattern

A graph is a structure composed of a set of vertices (i.e. nodes, dots) connected to one another by a set of edges (i.e. links, lines). The concept of a graph has been around since the late 19th century, however, only in recent decades has there been a strong resurgence in both theoretical and applied graph research in mathematics, physics, and computer science. In applied computing, since the late 1960s, the interlinked table structure of the relational database has been the predominant information storage and retrieval model. With the growth of graph/network-based data and the need to efficiently process such data, new data management systems have been developed. In contrast to the index-intensive, set-theoretic operations of relational databases, graph databases make use of index-free, local traversals. This chapter discusses the graph traversal pattern and its use in computing. (Angles & Guiterrez, 2008)

Shortest Path in Transportation Network and Weighted Subdivisions

In this chapter we consider two versions of the problem; the shortest path in a transportation network and the shortest path in a weighted subdivision, sometimes called a terrain.