Property Graph Developer's Guide
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Practical Parallel Hypergraph Algorithms
Practical Parallel Hypergraph Algorithms Julian Shun [email protected] MIT CSAIL Abstract v While there has been signicant work on parallel graph pro- 0 cessing, there has been very surprisingly little work on high- e0 performance hypergraph processing. This paper presents v0 v1 v1 a collection of ecient parallel algorithms for hypergraph processing, including algorithms for betweenness central- e1 ity, maximal independent set, k-core decomposition, hyper- v2 trees, hyperpaths, connected components, PageRank, and v2 v3 e single-source shortest paths. For these problems, we either 2 provide new parallel algorithms or more ecient implemen- v3 tations than prior work. Furthermore, our algorithms are theoretically-ecient in terms of work and depth. To imple- (a) Hypergraph (b) Bipartite representation ment our algorithms, we extend the Ligra graph processing Figure 1. An example hypergraph representing the groups framework to support hypergraphs, and our implementa- , , , , , , and , , and its bipartite repre- { 0 1 2} { 1 2 3} { 0 3} tions benet from graph optimizations including switching sentation. between sparse and dense traversals based on the frontier size, edge-aware parallelization, using buckets to prioritize processing of vertices, and compression. Our experiments represented as hyperedges, can contain an arbitrary number on a 72-core machine and show that our algorithms obtain of vertices. Hyperedges correspond to group relationships excellent parallel speedups, and are signicantly faster than among vertices (e.g., a community in a social network). An algorithms in existing hypergraph processing frameworks. example of a hypergraph is shown in Figure 1a. CCS Concepts • Computing methodologies → Paral- Hypergraphs have been shown to enable richer analy- lel algorithms; Shared memory algorithms. -
Scalable Cloud Computing
Scalable Cloud Computing Keijo Heljanko Department of Computer Science and Engineering School of Science Aalto University [email protected] 2.10-2013 Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 1/57 Guest Lecturer I Guest Lecturer: Assoc. Prof. Keijo Heljanko, Department of Computer Science and Engineering, Aalto University, I Email: [email protected] I Homepage: https://people.aalto.fi/keijo_heljanko I For more info into today’s topic, attend the course: “T-79.5308 Scalable Cloud Computing” Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 2/57 Business Drivers of Cloud Computing I Large data centers allow for economics of scale I Cheaper hardware purchases I Cheaper cooling of hardware I Example: Google paid 40 MEur for a Summa paper mill site in Hamina, Finland: Data center cooled with sea water from the Baltic Sea I Cheaper electricity I Cheaper network capacity I Smaller number of administrators / computer I Unreliable commodity hardware is used I Reliability obtained by replication of hardware components and a combined with a fault tolerant software stack Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 3/57 Cloud Computing Technologies A collection of technologies aimed to provide elastic “pay as you go” computing I Virtualization of computing resources: Amazon EC2, Eucalyptus, OpenNebula, Open Stack Compute, . I Scalable file storage: Amazon S3, GFS, HDFS, . I Scalable batch processing: Google MapReduce, Apache Hadoop, PACT, Microsoft Dryad, Google Pregel, Spark, ::: I Scalable datastore: Amazon Dynamo, Apache Cassandra, Google Bigtable, HBase,. I Distributed Coordination: Google Chubby, Apache Zookeeper, . I Scalable Web applications hosting: Google App Engine, Microsoft Azure, Heroku, . -
Reference Guide
Apache Syncope - Reference Guide Version 2.1.9 Table of Contents 1. Introduction. 2 1.1. Identity Technologies. 2 1.1.1. Identity Stores . 2 1.1.2. Provisioning Engines . 4 1.1.3. Access Managers . 5 1.1.4. The Complete Picture . 5 2. Architecture. 7 2.1. Core . 7 2.1.1. REST . 7 2.1.2. Logic . 8 2.1.3. Provisioning . 8 2.1.4. Workflow. 9 2.1.5. Persistence . 9 2.1.6. Security . 9 2.2. Admin UI. 10 2.2.1. Accessibility . 10 2.3. End-user UI. 12 2.3.1. Password Reset . 12 2.3.2. Accessibility . 13 2.4. CLI . 15 2.5. Third Party Applications. 15 2.5.1. Eclipse IDE Plugin . 15 2.5.2. Netbeans IDE Plugin. 15 3. Concepts . 16 3.1. Users, Groups and Any Objects . 16 3.2. Type Management . 17 3.2.1. Schema . 17 Plain . 17 Derived . 18 Virtual . 18 3.2.2. AnyTypeClass . 19 3.2.3. AnyType . 19 3.2.4. RelationshipType . 21 3.2.5. Type Extensions . 22 3.3. External Resources. 23 3.3.1. Connector Bundles . 24 3.3.2. Connector Instance details . 24 3.3.3. External Resource details . 25 3.3.4. Mapping . 26 3.3.5. Linked Accounts . 29 3.4. Realms . 29 3.4.1. Realm Provisioning . 30 3.4.2. LogicActions . 31 3.5. Entitlements. 31 3.6. Privileges . 31 3.7. Roles. 31 3.7.1. Delegated Administration . 32 3.8. Provisioning. 33 3.8.1. Overview. 33 3.8.2. -
Zookeeper Administrator's Guide
ZooKeeper Administrator's Guide A Guide to Deployment and Administration by Table of contents 1 Deployment........................................................................................................................ 2 1.1 System Requirements....................................................................................................2 1.2 Clustered (Multi-Server) Setup.....................................................................................2 1.3 Single Server and Developer Setup..............................................................................4 2 Administration.................................................................................................................... 4 2.1 Designing a ZooKeeper Deployment........................................................................... 5 2.2 Provisioning.................................................................................................................. 6 2.3 Things to Consider: ZooKeeper Strengths and Limitations..........................................6 2.4 Administering................................................................................................................6 2.5 Maintenance.................................................................................................................. 6 2.6 Supervision....................................................................................................................7 2.7 Monitoring.....................................................................................................................8 -
Practical Parallel Hypergraph Algorithms
Practical Parallel Hypergraph Algorithms Julian Shun [email protected] MIT CSAIL Abstract v0 While there has been significant work on parallel graph pro- e0 cessing, there has been very surprisingly little work on high- v0 v1 v1 performance hypergraph processing. This paper presents a e collection of efficient parallel algorithms for hypergraph pro- 1 v2 cessing, including algorithms for computing hypertrees, hy- v v 2 3 e perpaths, betweenness centrality, maximal independent sets, 2 v k-core decomposition, connected components, PageRank, 3 and single-source shortest paths. For these problems, we ei- (a) Hypergraph (b) Bipartite representation ther provide new parallel algorithms or more efficient imple- mentations than prior work. Furthermore, our algorithms are Figure 1. An example hypergraph representing the groups theoretically-efficient in terms of work and depth. To imple- fv0;v1;v2g, fv1;v2;v3g, and fv0;v3g, and its bipartite repre- ment our algorithms, we extend the Ligra graph processing sentation. framework to support hypergraphs, and our implementations benefit from graph optimizations including switching between improved compared to using a graph representation. Unfor- sparse and dense traversals based on the frontier size, edge- tunately, there is been little research on parallel hypergraph aware parallelization, using buckets to prioritize processing processing. of vertices, and compression. Our experiments on a 72-core The main contribution of this paper is a suite of efficient machine and show that our algorithms obtain excellent paral- parallel hypergraph algorithms, including algorithms for hy- lel speedups, and are significantly faster than algorithms in pertrees, hyperpaths, betweenness centrality, maximal inde- existing hypergraph processing frameworks. -
Unravel Data Systems Version 4.5
UNRAVEL DATA SYSTEMS VERSION 4.5 Component name Component version name License names jQuery 1.8.2 MIT License Apache Tomcat 5.5.23 Apache License 2.0 Tachyon Project POM 0.8.2 Apache License 2.0 Apache Directory LDAP API Model 1.0.0-M20 Apache License 2.0 apache/incubator-heron 0.16.5.1 Apache License 2.0 Maven Plugin API 3.0.4 Apache License 2.0 ApacheDS Authentication Interceptor 2.0.0-M15 Apache License 2.0 Apache Directory LDAP API Extras ACI 1.0.0-M20 Apache License 2.0 Apache HttpComponents Core 4.3.3 Apache License 2.0 Spark Project Tags 2.0.0-preview Apache License 2.0 Curator Testing 3.3.0 Apache License 2.0 Apache HttpComponents Core 4.4.5 Apache License 2.0 Apache Commons Daemon 1.0.15 Apache License 2.0 classworlds 2.4 Apache License 2.0 abego TreeLayout Core 1.0.1 BSD 3-clause "New" or "Revised" License jackson-core 2.8.6 Apache License 2.0 Lucene Join 6.6.1 Apache License 2.0 Apache Commons CLI 1.3-cloudera-pre-r1439998 Apache License 2.0 hive-apache 0.5 Apache License 2.0 scala-parser-combinators 1.0.4 BSD 3-clause "New" or "Revised" License com.springsource.javax.xml.bind 2.1.7 Common Development and Distribution License 1.0 SnakeYAML 1.15 Apache License 2.0 JUnit 4.12 Common Public License 1.0 ApacheDS Protocol Kerberos 2.0.0-M12 Apache License 2.0 Apache Groovy 2.4.6 Apache License 2.0 JGraphT - Core 1.2.0 (GNU Lesser General Public License v2.1 or later AND Eclipse Public License 1.0) chill-java 0.5.0 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 OpenCensus 0.12.3 Apache License 2.0 ApacheDS Protocol -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
A.13 Apache Groovy
Oracle® Big Data Spatial and Graph User©s Guide and Reference Release 1.2 E67958-03 May 2016 Oracle Big Data Spatial and Graph User's Guide and Reference, Release 1.2 E67958-03 Copyright © 2015, 2016, Oracle and/or its affiliates. All rights reserved. Primary Authors: Chuck Murray, Harihara Subramanian, Donna Carver Contributors: Bill Beauregard, Hector Briseno, Hassan Chafi, Zazhil Herena, Sungpack Hong, Roberto Infante, Hugo Labra, Gabriela Montiel-Moreno, Siva Ravada, Carlos Reyes, Korbinian Schmid, Jane Tao, Zhe (Alan) Wu This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, delivered to U.S. Government end users are "commercial computer software" pursuant to the applicable Federal Acquisition Regulation and agency- specific supplemental regulations. -
Open Source Software Licenses, Notices, and Information
Intergraph InPursuit® WebRMS 03.06.1909 Open Source Software Licenses, Notices, and Information This information is provided for Intergraph InPursuit® WebRMS, a software program of Intergraph® Corporation D/B/A Hexagon Safety & Infrastructure® (“Hexagon”). Source Code Access Intergraph InPursuit WebRMS may include components licensed pursuant to open source software licenses with an obligation to offer the recipient source code. Please see below the list of such components and the information needed to access the source code repository for each. In the event the source code is inaccessible using the information below, please email [email protected]. Component, version Link to download repository Hibernate ORM https://github.com/hibernate/hibernate-orm/releases/tag/3.6.10.Final 3.6.10.Final javassist 3.12.0.GA https://github.com/jboss-javassist/javassist/releases/tag/rel_3_12_0_ga wsdl4j 1.6.1 https://sourceforge.net/projects/wsdl4j/files/WSDL4J/1.6.1/ aspectj 1.8.4 http://git.eclipse.org/c/aspectj/org.aspectj.git/tag/?h=V1_8_4 displaytag 1.2.9 https://github.com/hexagonSI-RMS/displaytag-hexagon Open Source Software Components Intergraph InPursuit WebRMS may include the open source software components identified below. This document provides the notices and information regarding any such open source software for informational purposes only. Please see the product license agreement for Intergraph InPursuit WebRMS to determine the terms and conditions that apply to the open source software. Hexagon reserves all other rights. Component, version URL Copyright License link ActiveMQ KahaDB, version http://activemq.apache.org/kahadb.html © 2005-2012 Apache Software Foundation License 1 5.5.1 ActiveMQ, version 5.5.1 http://activemq.apache.org/ © 2005-2012 Apache Software Foundation License 1 Activiti, version 5.14.0 https://www.activiti.org/ © 2010-2016 Alfresco Software, Ltd. -
Adjacency Matrix
CSE 373 Graphs 3: Implementation reading: Weiss Ch. 9 slides created by Marty Stepp http://www.cs.washington.edu/373/ © University of Washington, all rights reserved. 1 Implementing a graph • If we wanted to program an actual data structure to represent a graph, what information would we need to store? for each vertex? for each edge? 1 2 3 • What kinds of questions would we want to be able to answer quickly: 4 5 6 about a vertex? about edges / neighbors? 7 about paths? about what edges exist in the graph? • We'll explore three common graph implementation strategies: edge list , adjacency list , adjacency matrix 2 Edge list • edge list : An unordered list of all edges in the graph. an array, array list, or linked list • advantages : 1 2 3 easy to loop/iterate over all edges 4 5 6 • disadvantages : hard to quickly tell if an edge 7 exists from vertex A to B hard to quickly find the degree of a vertex (how many edges touch it) 0 1 2 3 4 56 7 8 (1, 2) (1, 4) (1, 7) (2, 3) 2, 5) (3, 6)(4, 7) (5, 6) (6, 7) 3 Graph operations • Using an edge list, how would you find: all neighbors of a given vertex? the degree of a given vertex? whether there is an edge from A to B? 1 2 3 whether there are any loops (self-edges)? • What is the Big-Oh of each operation? 4 5 6 7 0 1 2 3 4 56 7 8 (1, 2) (1, 4) (1, 7) (2, 3) 2, 5) (3, 6)(4, 7) (5, 6) (6, 7) 4 Adjacency matrix • adjacency matrix : An N × N matrix where: the non-diagonal entry a[i,j] is the number of edges joining vertex i and vertex j (or the weight of the edge joining vertex i and vertex j). -
Introduction to Graphs
Multidimensional Arrays & Graphs CMSC 420: Lecture 3 Mini-Review • Abstract Data Types: • Implementations: • List • Linked Lists • Stack • Circularly linked lists • Queue • Doubly linked lists • Deque • XOR Doubly linked lists • Dictionary • Ring buffers • Set • Double stacks • Bit vectors Techniques: Sentinels, Zig-zag scan, link inversion, bit twiddling, self- organizing lists, constant-time initialization Constant-Time Initialization • Design problem: - Suppose you have a long array, most values are 0. - Want constant time access and update - Have as much space as you need. • Create a big array: - a = new int[LARGE_N]; - Too slow: for(i=0; i < LARGE_N; i++) a[i] = 0 • Want to somehow implicitly initialize all values to 0 in constant time... Constant-Time Initialization means unchanged 1 2 6 12 13 Data[] = • Access(i): if (0≤ When[i] < count and Where[When[i]] == i) return Where[] = 6 13 12 Count = 3 Count holds # of elements changed Where holds indices of the changed elements. When[] = 1 3 2 When maps from index i to the time step when item i was first changed. Access(i): if 0 ≤ When[i] < Count and Where[When[i]] == i: return Data[i] else: return DEFAULT Multidimensional Arrays • Often it’s more natural to index data items by keys that have several dimensions. E.g.: • (longitude, latitude) • (row, column) of a matrix • (x,y,z) point in 3d space • Aside: why is a plane “2-dimensional”? Row-major vs. Column-major order • 2-dimensional arrays can be mapped to linear memory in two ways: 1 2 3 4 5 1 2 3 4 5 1 1 2 3 4 5 1 1 5 9 13 17 2 6 7 8 9 10 2 2 6 10 14 18 3 11 12 13 14 15 3 3 7 11 15 19 4 16 17 18 19 20 4 4 8 12 16 20 Row-major order Column-major order Addr(i,j) = Base + 5(i-1) + (j-1) Addr(i,j) = Base + (i-1) + 4(j-1) Row-major vs. -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo...........................................................................................................................................................