NG SYNTHESIS LECTURES ON DATA MANAGEMENT
dandEricEricEricYuYuYuPERSPECTIVES ONON BUSINESSBUSINESS INTELLIGENCEINTELLIGENCEPERSPECTIVESPERSPECTIVESON gangangan ClClClaypoolaypoolaypool 1627781627781627050937050937050937MOR GANGAN && CLCL AYPOOLAYPOOLMORMORGAN &CL aypool spectives ononBusinessBusiness esEditorEditorEditor
Perspectives onBusiness Intelligence
Synthesis Lectures on DataManagementEditorM. Tamer Özsu, University of WaterlooSynthesis Lectures on Data Management is edited by Tamer Özsu of the University of Waterloo.The series will publish 50- to 125 page publications on topics pertaining to data management. Thescope will largely follow the purview of premier information and computer science conferences,such as ACM SIGMOD, VLDB, ICDE, PODS, ICDT, and ACM KDD. Potential topicsinclude, but not are limited to: query languages, database system architectures, transactionmanagement, data warehousing, XML and databases, data stream systems, wide scale datadistribution, multimedia data management, data mining, and related subjects.Perspectives on Business IntelligenceRaymond T. Ng, Patricia C. Arocena, Denilson Barbosa, Giuseppe Carenini, Luiz Gomes, Jr., StephanJou, Rock Anthony Leung, Evangelos Milios, Renée J. Miller, John Mylopoulos, Rachel A. Pottinger,Frank Tompa, and Eric Yu2013Semantics Empowered Web 3.0: Managing Enterprise, Social, Sensor, and Cloud-based Dataand Services for Advanced ApplicationsAmit Sheth and Krishnaprasad Thirunarayan2012Data Management in the Cloud: Challenges and OpportunitiesDivyakant Agrawal, Sudipto Das, and Amr El Abbadi2012Query Processing over Uncertain DatabasesLei Chen and Xiang Lian2012Foundations of Data Quality ManagementWenfei Fan and Floris Geerts2012
iiiIncomplete Data and Data Dependencies in Relational DatabasesSergio Greco, Cristian Molinaro, and Francesca Spezzano2012Business Processes: A Database PerspectiveDaniel Deutch and Tova Milo2012Data Protection from Insider ThreatsElisa Bertino2012Deep Web Query Interface Understanding and IntegrationEduard C. Dragut, Weiyi Meng, and Clement T. Yu2012P2P Techniques for Decentralized ApplicationsEsther Pacitti, Reza Akbarinia, and Manal El-Dick2012Query Answer AuthenticationHweeHwa Pang and Kian-Lee Tan2012Declarative NetworkingBoon Thau Loo and Wenchao Zhou2012Full-Text (Substring) Indexes in External MemoryMarina Barsky, Ulrike Stege, and Alex Thomo2011Spatial Data ManagementNikos Mamoulis2011Database Repairing and Consistent Query AnsweringLeopoldo Bertossi2011Managing Event Information: Modeling, Retrieval, and ApplicationsAmarnath Gupta and Ramesh Jain2011
ivFundamentals of Physical Design and Query CompilationDavid Toman and Grant Weddell2011Methods for Mining and Summarizing Text ConversationsGiuseppe Carenini, Gabriel Murray, and Raymond Ng2011Probabilistic DatabasesDan Suciu, Dan Olteanu, Christopher Ré, and Christoph Koch2011Peer-to-Peer Data ManagementKarl Aberer2011Probabilistic Ranking Techniques in Relational DatabasesIhab F. Ilyas and Mohamed A. Soliman2011Uncertain Schema MatchingAvigdor Gal2011Fundamentals of Object Databases: Object-Oriented and Object-Relational DesignSuzanne W. Dietrich and Susan D. Urban2010Advanced Metasearch Engine TechnologyWeiyi Meng and Clement T. Yu2010Web Page Recommendation Models: Theory and AlgorithmsSule Gündüz-Ögüdücü2010Multidimensional Databases and Data WarehousingChristian S. Jensen, Torben Bach Pedersen, and Christian Thomsen2010Database ReplicationBettina Kemme, Ricardo Jimenez-Peris, and Marta Patino-Martinez2010
vRelational and XML Data ExchangeMarcelo Arenas, Pablo Barcelo, Leonid Libkin, and Filip Murlak2010User-Centered Data ManagementTiziana Catarci, Alan Dix, Stephen Kimani, and Giuseppe Santucci2010Data Stream ManagementLukasz Golab and M. Tamer Özsu2010Access Control in Data Management SystemsElena Ferrari2010An Introduction to Duplicate DetectionFelix Naumann and Melanie Herschel2010Privacy-Preserving Data Publishing: An OverviewRaymond Chi-Wing Wong and Ada Wai-Chee Fu2010Keyword Search in DatabasesJeffrey Xu Yu, Lu Qin, and Lijun Chang2009
Copyright 2013 by Morgan & ClaypoolAll rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted inany form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations inprinted reviews, without the prior permission of the publisher.Perspectives on Business IntelligenceRaymond T. Ng, Patricia C. Arocena, Denilson Barbosa, Giuseppe Carenini, Luiz Gomes, Jr., Stephan Jou,Rock Anthony Leung, Evangelos Milios, Renée J. Miller, John Mylopoulos, Rachel A. Pottinger, Frank Tompa, andEric Yuwww.morganclaypool.comISBN: 9781627050937ISBN: 9781627050944paperbackebookDOI 10.2200/S00491ED1V01Y201303DTM034A Publication in the Morgan & Claypool Publishers seriesSYNTHESIS LECTURES ON DATA MANAGEMENTLecture #32Series Editor: M. Tamer Özsu, University of WaterlooSeries ISSNSynthesis Lectures on Data ManagementPrint 2153-5418 Electronic 2153-5426
Perspectives onBusiness IntelligenceRaymond T. Ng, Patricia C. Arocena, Denilson Barbosa, Giuseppe Carenini, LuizGomes, Jr., Stephan Jou, Rock Anthony Leung, Evangelos Milios, Renée J. Miller,John Mylopoulos, Rachel A. Pottinger, Frank Tompa, and Eric YuSYNTHESIS LECTURES ON DATA MANAGEMENT #32M&CMorgan& cLaypool publishers
ABSTRACTIn the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports thatdescribe the state of business activities in the past, such as for questions like “How did our salesperform during the last quarter?” A decade later, there was a shift to more interactive content thatpresented how the business was performing at the present time, answering questions like “How arewe doing right now?” Today the focus of BI users are looking into the future. “Given what I didbefore and how I am currently doing this quarter, how will I do next quarter?”Furthermore, fuelled by the demands of Big Data, BI systems are going through a time ofincredible change. Predictive analytics, high volume data, unstructured data, social data, mobile,consumable analytics, and data visualization are all examples of demands and capabilities that havebecome critical within just the past few years, and are growing at an unprecedented pace.This book introduces research problems and solutions on various aspects central to nextgeneration BI systems. It begins with a chapter on an industry perspective on how BI has evolved,and discusses how game-changing trends have drastically reshaped the landscape of BI. One ofthe game changers is the shift toward the consumerization of BI tools. As a result, for BI toolsto be successfully used by business users (rather than IT departments), the tools need a businessmodel, rather than a data model. One chapter of the book surveys four different types of businessmodeling. However, even with the existence of a business model for users to express queries, thedata that can meet the needs are still captured within a data model. The next chapter on vivificationaddresses the problem of closing the gap, which is often significant, between the business and thedata models. Moreover, Big Data forces BI systems to integrate and consolidate multiple, and oftenwildly different, data sources. One chapter gives an overview of several integration architectures fordealing with the challenges that need to be overcome.While the book so far focuses on the usual structured relational data, the remaining chaptersturn to unstructured data, an ever-increasing and important component of Big Data. One chapteron information extraction describes methods for dealing with the extraction of relations from freetext and the web. Finally, BI users need tools to visualize and interpret new and complex types ofinformation in a way that is compelling, intuitive, but accurate. The last chapter gives an overviewof information visualization for decision support and text.KEYWORDSbusiness intelligence, big data, business modeling, vivification, data integration, information extraction, information visualization
ixContents1Introduction and the Changing Landscape of Business Intelligence . . . . . . . . . . . . . 1Stephan Jou and Raymond Ng21.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2The Role of Research and This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3BI Game Changers: an Industry Viewpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Rock Leung, Chahab Nastar, Frederic Vanborre, Christophe Favart, Gregor Hackenbroich,Philip Taylor, and David Trastour32.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2Defining Business Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3Early Days of BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.4Classic BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.5Game-changing Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.5.1 Faster Business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.5.2 Bigger Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.5.3 Better Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.6Next-generation BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.7Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1710111314Business Modeling for BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Eric Yu, Jennifer Horkoff, John Mylopoulos, Gregory Richards, and Daniel Amyot3.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2Modeling Business Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.3Strategic Business Modeling for Performance Management . . . . . . . . . . . . . . . . . . 223.4Modeling Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.5Toward Modeling for BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.5.1 BIM Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.5.2 Reasoning with BIM Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.6Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
x4Vivification in BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Patricia C. Arocena, Renée J. Miller, and John Mylopoulos4.14.24.34.44.54.64.74.85Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .A Motivating Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .The Vivification Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.3.1 Knowledge Base Vivification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.3.2 Data Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Formal Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Current Vivification Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.5.1 Strategies for Dealing with Incompleteness . . . . . . . . . . . . . . . . . . . . . . . . . .4.5.2 Strategies for Dealing with Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.5.3 Summary of Other Relevant Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Toward Adaptive Vivification Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.6.1 Vivification by Acceptance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.6.2 Vivification by Default . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.6.3 Vivification by Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Directions for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33343737394043434445464647484951Information Integration in BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Rachel A. Pottinger5.15.25.35.45.55.66Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Information Integration Goals and Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Challenges and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.3.1 Schemas and Semantic Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.3.2 Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Overview of Different Information Integration Architectures . . . . . . . . . . . . . . . .5.4.1 Data Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.4.2 Data Warehousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.4.3 Peer Data Management Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Information Integration Tools in Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5354565657575761646566Information Extraction for BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Denilson Barbosa, Luiz Gomes, Jr., and Frank Wm. Tompa6.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
xi6.26.36.46.576.1.1 Levels of Structuredness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.1.2 The Role of IE for BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .IE From Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2.1 Patterns in Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2.2 Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2.3 Ontology Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2.4 Relation Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2.5 Factoid Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Data Extraction from the web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.3.1 Wrapper Induction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.3.2 Schema Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .BI over Raw Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .68697172747980848585868889Information Visualization for BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Giuseppe Carenini and Evangelos Milios7.17.27.37.4Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Information Visualization for Decision Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937.2.1 Information Visualization in the Performance Management Cycle:Information Dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 947.2.2 Visualization for Preferential Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997.2.3 Current and Future Trends in Information Visualization for DecisionSupport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Visualizing Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057.3.1 Text Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057.3.2 Topic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1087.3.3 Text Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1097.3.4 Sentiment Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137.3.5 Multiview Systems for Document Collections . . . . . . . . . . . . . . . . . . . . . . 117Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Authors’ Biographies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
1CHAPTER1Introduction and the ChangingLandscape of BusinessIntelligenceStephan Jou and Raymond Ng1.1INTRODUCTIONA lot has changed since 1958 when IBM researcher Hans Peter Luhn first coined the term BusinessIntelligence (BI) as “the ability to apprehend the interrelationships of presented facts in such a way asto guide action toward a desired goal” [Luhn, 1958]. In particular, the BI domain has seen dramaticchanges in its use of temporal information, the nature of the data to analyze, cloud computing,user-centric and consumable analytics. All of these changes demand new, enabling research andtechnology capability that are exemplified by this book.BI systems have traditionally focused on the delivery of reports that describe the state of business activities in the past. Questions like “How did our sales perform during the last quarter?” wereanswered through straightforward query generation and execution against structured and multidimensional data warehouses and delivered to end-users in a static report, such as a PDF documentor simple web page.In the 1990s, there was a shift from static reports of past performance to more interactivecontent that presented how the business was performing at the present time, answering questions like“How are we doing right now? This month, this day, this second?” This shift to real-time businessintelligence was supported with new technologies: animated real-time dashboards, interactive filters,prompts, and multidimensional gestures augmented the classical, static content, while in-memorydatabases and other performance-enabling infrastructures surfaced.Now the focus of business intelligence systems has shifted in the time domain yet again. Inaddition to asking questions about the past and the present, BI users are looking into the future.“Given what I did before and how I am currently doing this quarter, how will I do next quarter?How am I predicted to perform, and how does that compare to competitors who are in a similarsituation? How can I tweak what I am doing right now to opti
Morgan Claypool Publishers& w w w. m o r g a n c l a y p o o l . c o m Series Editor: M. Tamer Özsu, University of Waterloo SYNTHESIS LECTURES ON DATA MANAGEMENT &MCMorgan Claypool Publishers& SYNTHESIS LECTURES ON DATA MANAGEMENT About SYNTHESIs This volume is a printed version of a work that appears in th
and this is still in the form of a PID controller but now the settings are: 2Ip W p c W T p c p K K, W W, and W T 1 Dp. . Colorado School of Mines CHEN403 Direct Synthesis Controller Tuning Direct Synthesis - Direct Synthesis - Direct Synthesis - Colorado School of Mines CHEN403 Direct Synthesis Controller Tuning File Size: 822KB
Milli-Q Synthesis/Synthesis A10 1 Chapter 1 INTRODUCTION 1-1 USING THIS MANUAL MATCHING THIS MANUAL WITH YOUR MILLI-Q This manual is intended for use with a Millipore Milli-Q Synthesis or Milli-Q Synthesis A10 Water Purification System. This Owner s Manual is a guide for use during the in
Organic Synthesis What are the Essentials in Synthesis? 5 Since organic synthesis is applied organic chemistry, to stand a realistic chance of succeeding in any synthesis, the student ought to have a good knowledge-base of organic chemistry in the following areas: Protecting group chemistry Asymmetric synthesis
the advantages of microwave assisted synthesis in com-parison to conventional heating. First, we will discuss the microwave synthesis of 2-pyridones. In the second part, microwave assisted synthesis of 2-quionolones will be given. At the end of the review, examples of microwave synthesis of ring fused N-substituted 2-pyri-dones will be discussed.
Technical Terms ! Organic Synthesis-means the same as synthetic organic chemistry ! Total Synthesis: The chemical synthesis of a molecule from a relatively simpler starting materials ! Semisynthesis: the synthesis of
Reaction 9. Anschutz Anthracene Synthesis 10. Appel Reaction 11. Arndt-Eistert Synthesis 12. Aston-Greenburg Rearrangement 13. Aza-Claisen Rearrangement 14. Baeyer Indole Synthesis 15. Baeyer Oxindole Synthesis 16. Baeyer Pyridine Synthesis 17. Baeyer-Villiger Oxidation 18. Baltz-Schiemann Reac
3.1.2.9 Synthesis of Propoxylated Bisphenol A dimethacrylate 57 3.1.2.10 Purification of Propoxylated Bisphenol A dimethacrylate 60 3.1.2.11 Synthesis of Propoxylated 6F dimethacrylate 60 3.1.2.12 Synthesis of Ethoxylated Bisphenol A dimethacrylate 61 3.1.2.13 Synthesis of Ethoxyla
The Element Encyclopedia of Secret Signs and Symbols The Ultimate A-Z Guide from Alchemy to the Zodiac Adele Nozedar. For Adam and for the seven secrets ‘In every grain of sand there lies Hidden the soil of a star’ Arthur Machen ‘I do not need a leash or a tie To lead me astray In the land where dreams lie’ Yoav In Nature’s temple, living pillars rise Speaking sometimes in words of .
