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  Distributed Querying of Large Labeled Graphs

Gurajada, S. (2017). Distributed Querying of Large Labeled Graphs. PhD Thesis, Universität des Saarlandes, Saarbrücken.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-8202-E Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-8203-C
Genre: Thesis

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http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de (Copyright transfer agreement)
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 Creators:
Gurajada, Sairam1, 2, Author              
Theobald, Martin1, Advisor              
Weikum, Gerhard1, Referee              
Özsu, M. Tamer3, Referee
Michel, Sebastian1, Referee              
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, escidoc:24018              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, Campus E1 4, 66123 Saarbrücken, DE, escidoc:1116551              
3External Organizations, escidoc:persistent22              

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 Abstract: Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the “Labeled Graph”, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. • Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. • Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. • Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined “TriAD” (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets.

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Language(s): eng - English
 Dates: 20162017-02-062017-02-102017
 Publication Status: Published in print
 Pages: x, 167 p.
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: guraphd2017
URN: urn:nbn:de:bsz:291-scidok-67738
 Degree: PhD

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