“…Given a query graph q and a graph database } ,..., , , { = Figure 1, Graph G should be returned as the result since G contains q. There are many algorithms have been proposed for subgraph search problem, such as [6,22,3,21,10,23,9,26,17,29,11].…”
Section: Figure 1: Running Examplementioning
confidence: 99%
“…We evaluate Fast-ON * and compare it with Ullman and Vflib on real and synthetic datasets for GIP, with SMS [27] and GADDI [24] for SLG, and with CT-index [11] and FG-index [3] for SSP.…”
Section: Figure 1: Running Examplementioning
confidence: 99%
“…The major challenge in this scenario is to reduce the number of pairwise subgraph isomorphism checking. A number of graph indexing techniques have been proposed to address this challenge [22,3,23,26,17,11,10,29]. There are two categories of graph indexing techniques: feature-based index and nonfeature-based index.…”
Section: Related Workmentioning
confidence: 99%
“…There are two categories of graph indexing techniques: feature-based index and nonfeature-based index. In feature-based index [22,3,23,26,17,11], some subgraphs are chosen as index features, and an inverted list is built for each feature. Generally, query processing follows the filtering-andverification framework.…”
Section: Related Workmentioning
confidence: 99%
“…Graph query processing [6,22,3,21,10,23,9,26,17,29,11,24,28,27,25] has attracted much attention in recent years thanks to the increasing popularity of graph databases in various application domains. Existing research on graph query processing is conducted mainly on two types of graph databases as follows.…”
“…Given a query graph q and a graph database } ,..., , , { = Figure 1, Graph G should be returned as the result since G contains q. There are many algorithms have been proposed for subgraph search problem, such as [6,22,3,21,10,23,9,26,17,29,11].…”
Section: Figure 1: Running Examplementioning
confidence: 99%
“…We evaluate Fast-ON * and compare it with Ullman and Vflib on real and synthetic datasets for GIP, with SMS [27] and GADDI [24] for SLG, and with CT-index [11] and FG-index [3] for SSP.…”
Section: Figure 1: Running Examplementioning
confidence: 99%
“…The major challenge in this scenario is to reduce the number of pairwise subgraph isomorphism checking. A number of graph indexing techniques have been proposed to address this challenge [22,3,23,26,17,11,10,29]. There are two categories of graph indexing techniques: feature-based index and nonfeature-based index.…”
Section: Related Workmentioning
confidence: 99%
“…There are two categories of graph indexing techniques: feature-based index and nonfeature-based index. In feature-based index [22,3,23,26,17,11], some subgraphs are chosen as index features, and an inverted list is built for each feature. Generally, query processing follows the filtering-andverification framework.…”
Section: Related Workmentioning
confidence: 99%
“…Graph query processing [6,22,3,21,10,23,9,26,17,29,11,24,28,27,25] has attracted much attention in recent years thanks to the increasing popularity of graph databases in various application domains. Existing research on graph query processing is conducted mainly on two types of graph databases as follows.…”
The growing interest in chemogenomics approaches over the last years has led to an increasing amount of data regarding chemical and the corresponding biological activity space. The resulting data, collected in either in-house or public databases, need to be analyzed efficiently to speed-up the increasingly difficult task of drug discovery. Unfortunately, the discovery of new chemical entities or new targets for known drugs ('drug repurposing') is not suitable to a fully automated analysis or a simple drill down process. Visual interactive interfaces that allow to explore chemical space in a systematic manner and facilitate analytical reasoning can help to overcome these problems. Scaffold Hunter is a tool for the visual analysis of chemical compound databases that provides integrated visualization and analysis of biological activity data and fosters the interactive exploration of data imported from a variety of sources. We describe the features and illustrate the use by means of an exemplary analysis workflow.
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