8 research outputs found
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Local clustering in provenance graphs
Systems that capture and store data provenance, the record of how an object has arrived at its current state, accumulate historical metadata over time, forming a large graph. Local clustering in these graphs, in which we start with a seed vertex and grow a cluster around it, is of paramount importance because it supports critical provenance applications such as identifying semantically meaningful tasks in an object's history. However, generic graph clustering algorithms are not effective at these tasks. We identify three key properties of provenance graphs and exploit them to justify two new centrality metrics we developed for use in performing local clustering on provenance graphs.Engineering and Applied Science
Sciunits: Reusable Research Objects
Science is conducted collaboratively, often requiring knowledge sharing about
computational experiments. When experiments include only datasets, they can be
shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers
(DOIs). An experiment, however, seldom includes only datasets, but more often
includes software, its past execution, provenance, and associated
documentation. The Research Object has recently emerged as a comprehensive and
systematic method for aggregation and identification of diverse elements of
computational experiments. While a necessary method, mere aggregation is not
sufficient for the sharing of computational experiments. Other users must be
able to easily recompute on these shared research objects. In this paper, we
present the sciunit, a reusable research object in which aggregated content is
recomputable. We describe a Git-like client that efficiently creates, stores,
and repeats sciunits. We show through analysis that sciunits repeat
computational experiments with minimal storage and processing overhead.
Finally, we provide an overview of sharing and reproducible cyberinfrastructure
based on sciunits gaining adoption in the domain of geosciences
NeuroProv: Provenance data visualisation for neuroimaging analyses
Ā© 2019 Elsevier Ltd Visualisation underpins the understanding of scientific data both through exploration and explanation of analysed data. Provenance strengthens the understanding of data by showing the process of how a result has been achieved. With the significant increase in data volumes and algorithm complexity, clinical researchers are struggling with information tracking, analysis reproducibility and the verification of scientific output. In addition, data coming from various heterogeneous sources with varying levels of trust in a collaborative environment adds to the uncertainty of the scientific outputs. This provides the motivation for provenance data capture and visualisation support for analyses. In this paper a system, NeuroProv is presented, to visualise provenance data in order to aid in the process of verification of scientific outputs, comparison of analyses, progression and evolution of results for neuroimaging analyses. The experimental results show the effectiveness of visualising provenance data for neuroimaging analyses
Towards Specificationless Monitoring of Provenance-Emitting Systems
Monitoring often requires insight into the monitored system as well as concrete specifications of expected behavior. More and more systems, however, provide information about their inner procedures by emitting provenance information in a W3C-standardized graph format.
In this work, we present an approach to monitor such provenance data for anomalous behavior by performing spectral graph analysis on slices of the constructed provenance graph and by comparing the characteristics of each slice with those of a sliding window over recently seen slices. We argue that this approach not only simplifies the monitoring of heterogeneous distributed systems, but also enables applying a host of well-studied techniques to monitor such systems
Utilizing Provenance in Reusable Research Objects
Science is conducted collaboratively, often requiring the sharing of
knowledge about computational experiments. When experiments include only
datasets, they can be shared using Uniform Resource Identifiers (URIs) or
Digital Object Identifiers (DOIs). An experiment, however, seldom includes only
datasets, but more often includes software, its past execution, provenance, and
associated documentation. The Research Object has recently emerged as a
comprehensive and systematic method for aggregation and identification of
diverse elements of computational experiments. While a necessary method, mere
aggregation is not sufficient for the sharing of computational experiments.
Other users must be able to easily recompute on these shared research objects.
Computational provenance is often the key to enable such reuse. In this paper,
we show how reusable research objects can utilize provenance to correctly
repeat a previous reference execution, to construct a subset of a research
object for partial reuse, and to reuse existing contents of a research object
for modified reuse. We describe two methods to summarize provenance that aid in
understanding the contents and past executions of a research object. The first
method obtains a process-view by collapsing low-level system information, and
the second method obtains a summary graph by grouping related nodes and edges
with the goal to obtain a graph view similar to application workflow. Through
detailed experiments, we show the efficacy and efficiency of our algorithms.Comment: 25 page
Graph Analysis and Applications in Clustering and Content-based Image Retrieval
About 300 years ago, when studying Seven Bridges of Kƶnigsberg problem - a famous problem concerning paths on graphs - the great mathematician Leonhard Euler said, āThis question is very banal, but seems to me worthy of attentionā. Since then, graph theory and graph analysis have not only become one of the most important branches of mathematics, but have also found an enormous range of important applications in many other areas. A graph is a mathematical model that abstracts entities and the relationships between them as nodes and edges. Many types of interactions between the entities can be modeled by graphs, for example, social interactions between people, the communications between the entities in computer networks and relations between biological species. Although not appearing to be a graph, many other types of data can be converted into graphs by cer- tain operations, for example, the k-nearest neighborhood graph built from pixels in an image.
Cluster structure is a common phenomenon in many real-world graphs, for example, social networks. Finding the clusters in a large graph is important to understand the underlying relationships between the nodes. Graph clustering is a technique that partitions nodes into clus- ters such that connections among nodes in a cluster are dense and connections between nodes in diļ¬erent clusters are sparse. Various approaches have been proposed to solve graph clustering problems. A common approach is to optimize a predeļ¬ned clustering metric using diļ¬erent optimization methods. However, most of these optimization problems are NP-hard due to the discrete set-up of the hard-clustering. These optimization problems can be relaxed, and a sub-optimal solu- tion can be found. A diļ¬erent approach is to apply data clustering
algorithms in solving graph clustering problems. With this approach, one must ļ¬rst ļ¬nd appropriate features for each node that represent the local structure of the graph. Limited Random Walk algorithm uses the random walk procedure to explore the graph and extracts ef- ļ¬cient features for the nodes. It incorporates the embarrassing parallel paradigm, thus, it can process large graph data eļ¬ciently using mod- ern high-performance computing facilities. This thesis gives the details of this algorithm and analyzes the stability issues of the algorithm.
Based on the study of the cluster structures in a graph, we deļ¬ne the authenticity score of an edge as the diļ¬erence between the actual and the expected number of edges that connect the two groups of the neighboring nodes of the two end nodes. Authenticity score can be used in many important applications, such as graph clustering, outlier detection, and graph data preprocessing. In particular, a data clus- tering algorithm that uses the authenticity scores on mutual k-nearest neighborhood graph achieves more reliable and superior performance comparing to other popular algorithms. This thesis also theoretically proves that this algorithm can asymptotically ļ¬nd the complete re- covery of the ground truth of the graphs that were generated by a stochastic r-block model.
Content-based image retrieval (CBIR) is an important application in computer vision, media information retrieval, and data mining. Given a query image, a CBIR system ranks the images in a large image database by their āsimilaritiesā to the query image. However, because of the ambiguities of the deļ¬nition of the āsimilarityā, it is very diļ¬- cult for a CBIR system to select the optimal feature set and ranking algorithm to satisfy the purpose of the query. Graph technologies have been used to improve the performance of CBIR systems in var- ious ways. In this thesis, a novel method is proposed to construct a visual-semantic graphāa graph where nodes represent semantic concepts and edges represent visual associations between concepts. The constructed visual-semantic graph not only helps the user to locate the target images quickly but also helps answer the questions related to the query image. Experiments show that the eļ¬orts of locating the target image are reduced by 25% with the help of visual-semantic graphs.
Graph analysis will continue to play an important role in future data analysis. In particular, the visual-semantic graph that captures important and interesting visual associations between the concepts is worthyof further attention