1,343 research outputs found
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
Doctor of Philosophy
dissertationLinked data are the de-facto standard in publishing and sharing data on the web. To date, we have been inundated with large amounts of ever-increasing linked data in constantly evolving structures. The proliferation of the data and the need to access and harvest knowledge from distributed data sources motivate us to revisit several classic problems in query processing and query optimization. The problem of answering queries over views is commonly encountered in a number of settings, including while enforcing security policies to access linked data, or when integrating data from disparate sources. We approach this problem by efficiently rewriting queries over the views to equivalent queries over the underlying linked data, thus avoiding the costs entailed by view materialization and maintenance. An outstanding problem of query rewriting is the number of rewritten queries is exponential to the size of the query and the views, which motivates us to study problem of multiquery optimization in the context of linked data. Our solutions are declarative and make no assumption for the underlying storage, i.e., being store-independent. Unlike relational and XML data, linked data are schema-less. While tracking the evolution of schema for linked data is hard, keyword search is an ideal tool to perform data integration. Existing works make crippling assumptions for the data and hence fall short in handling massive linked data with tens to hundreds of millions of facts. Our study for keyword search on linked data brought together the classical techniques in the literature and our novel ideas, which leads to much better query efficiency and quality of the results. Linked data also contain rich temporal semantics. To cope with the ever-increasing data, we have investigated how to partition and store large temporal or multiversion linked data for distributed and parallel computation, in an effort to achieve load-balancing to support scalable data analytics for massive linked data
Kaskade: Graph Views for Efficient Graph Analytics
Graphs are an increasingly popular way to model real-world entities and
relationships between them, ranging from social networks to data lineage graphs
and biological datasets. Queries over these large graphs often involve
expensive subgraph traversals and complex analytical computations. These
real-world graphs are often substantially more structured than a generic
vertex-and-edge model would suggest, but this insight has remained mostly
unexplored by existing graph engines for graph query optimization purposes.
Therefore, in this work, we focus on leveraging structural properties of graphs
and queries to automatically derive materialized graph views that can
dramatically speed up query evaluation. We present KASKADE, the first graph
query optimization framework to exploit materialized graph views for query
optimization purposes. KASKADE employs a novel constraint-based view
enumeration technique that mines constraints from query workloads and graph
schemas, and injects them during view enumeration to significantly reduce the
search space of views to be considered. Moreover, it introduces a graph view
size estimator to pick the most beneficial views to materialize given a query
set and to select the best query evaluation plan given a set of materialized
views. We evaluate its performance over real-world graphs, including the
provenance graph that we maintain at Microsoft to enable auditing, service
analytics, and advanced system optimizations. Our results show that KASKADE
substantially reduces the effective graph size and yields significant
performance speedups (up to 50X), in some cases making otherwise intractable
queries possible
DescribeX: A Framework for Exploring and Querying XML Web Collections
This thesis introduces DescribeX, a powerful framework that is capable of
describing arbitrarily complex XML summaries of web collections, providing
support for more efficient evaluation of XPath workloads. DescribeX permits the
declarative description of document structure using all axes and language
constructs in XPath, and generalizes many of the XML indexing and summarization
approaches in the literature. DescribeX supports the construction of
heterogeneous summaries where different document elements sharing a common
structure can be declaratively defined and refined by means of path regular
expressions on axes, or axis path regular expression (AxPREs). DescribeX can
significantly help in the understanding of both the structure of complex,
heterogeneous XML collections and the behaviour of XPath queries evaluated on
them.
Experimental results demonstrate the scalability of DescribeX summary
refinements and stabilizations (the key enablers for tailoring summaries) with
multi-gigabyte web collections. A comparative study suggests that using a
DescribeX summary created from a given workload can produce query evaluation
times orders of magnitude better than using existing summaries. DescribeX's
light-weight approach of combining summaries with a file-at-a-time XPath
processor can be a very competitive alternative, in terms of performance, to
conventional fully-fledged XML query engines that provide DB-like functionality
such as security, transaction processing, and native storage.Comment: PhD thesis, University of Toronto, 2008, 163 page
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