Evaluating and Integrating Databases in the Area of NLP
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Basex Server
BaseX Documentation Version 7.8 BaseX Documentation: Version 7.8 Content is available under Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0). Table of Contents 1. Main Page .............................................................................................................................. 1 Getting Started .................................................................................................................. 1 XQuery Portal .................................................................................................................... 1 Advanced User's Guide ..................................................................................................... 2 I. Getting Started ........................................................................................................................ 3 2. Command-Line Options ................................................................................................... 4 BaseX Standalone ..................................................................................................... 4 BaseX Server ............................................................................................................ 6 BaseX Client ............................................................................................................. 7 BaseX HTTP Server .................................................................................................. 9 BaseX GUI ............................................................................................................. -
Apache Cassandra on AWS Whitepaper
Apache Cassandra on AWS Guidelines and Best Practices January 2016 Amazon Web Services – Apache Cassandra on AWS January 2016 © 2016, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Page 2 of 52 Amazon Web Services – Apache Cassandra on AWS January 2016 Notices 2 Abstract 4 Introduction 4 NoSQL on AWS 5 Cassandra: A Brief Introduction 6 Cassandra: Key Terms and Concepts 6 Write Request Flow 8 Compaction 11 Read Request Flow 11 Cassandra: Resource Requirements 14 Storage and IO Requirements 14 Network Requirements 15 Memory Requirements 15 CPU Requirements 15 Planning Cassandra Clusters on AWS 16 Planning Regions and Availability Zones 16 Planning an Amazon Virtual Private Cloud 18 Planning Elastic Network Interfaces 19 Planning -
Apache Cassandra and Apache Spark Integration a Detailed Implementation
Apache Cassandra and Apache Spark Integration A detailed implementation Integrated Media Systems Center USC Spring 2015 Supervisor Dr. Cyrus Shahabi Student Name Stripelis Dimitrios 1 Contents 1. Introduction 2. Apache Cassandra Overview 3. Apache Cassandra Production Development 4. Apache Cassandra Running Requirements 5. Apache Cassandra Read/Write Requests using the Python API 6. Types of Cassandra Queries 7. Apache Spark Overview 8. Building the Spark Project 9. Spark Nodes Configuration 10. Building the Spark Cassandra Integration 11. Running the Spark-Cassandra Shell 12. Summary 2 1. Introduction This paper can be used as a reference guide for a detailed technical implementation of Apache Spark v. 1.2.1 and Apache Cassandra v. 2.0.13. The integration of both systems was deployed on Google Cloud servers using the RHEL operating system. The same guidelines can be easily applied to other operating systems (Linux based) as well with insignificant changes. Cluster Requirements: Software Java 1.7+ installed Python 2.7+ installed Ports A number of at least 7 ports in each node of the cluster must be constantly opened. For Apache Cassandra the following ports are the default ones and must be opened securely: 9042 - Cassandra native transport for clients 9160 - Cassandra Port for listening for clients 7000 - Cassandra TCP port for commands and data 7199 - JMX Port Cassandra For Apache Spark any 4 random ports should be also opened and secured, excluding ports 8080 and 4040 which are used by default from apache Spark for creating the Web UI of each application. It is highly advisable that one of the four random ports should be the port 7077, because it is the default port used by the Spark Master listening service. -
Declarative Access to Filesystem Data New Application Domains for XML Database Management Systems
Declarative Access to Filesystem Data New application domains for XML database management systems Alexander Holupirek Dissertation zur Erlangung des akademischen Grades Doktor der Naturwissenschaften (Dr. rer. nat.) Fachbereich Informatik und Informationswissenschaft Mathematisch-Naturwissenschaftliche Sektion Universität Konstanz Referenten: Prof. Dr. Marc H. Scholl Prof. Dr. Marcel Waldvogel Tag der mündlichen Prüfung: 17. Juli 2012 Abstract XML and state-of-the-art XML database management systems (XML-DBMSs) can play a leading role in far more application domains as it is currently the case. Even in their basic configuration, they entail all components necessary to act as central systems for complex search and retrieval tasks. They provide language-specific index- ing of full-text documents and can store structured, semi-structured and binary data. Besides, they offer a great variety of standardized languages (XQuery, XSLT, XQuery Full Text, etc.) to develop applications inside a pure XML technology stack. Benefits are obvious: Data, logic, and presentation tiers can operate on a single data model, and no conversions have to be applied when switching in between. This thesis deals with the design and development of XML/XQuery driven informa- tion architectures that process formerly heterogeneous data sources in a standardized and uniform manner. Filesystems and their vast amounts of different file types are a prime example for such a heterogeneous dataspace. A new XML dialect, the Filesystem Markup Language (FSML), is introduced to construct a database view of the filesystem and its contents. FSML provides a uniform view on the filesystem’s contents and allows developers to leverage the complete XML technology stack on filesystem data. -
Implementing Replication for Predictability Within Apache Thrift Jianwei Tu the Ohio State University [email protected]
Implementing Replication for Predictability within Apache Thrift Jianwei Tu The Ohio State University [email protected] ABSTRACT have a large number of packets. A study indicated that about Interactive applications, such as search, social networking and 0.02% of all flows contributed more than 59.3% of the total retail, hosted in cloud data center generate large quantities of traffic volume [1]. TCP is the dominating transport protocol small workloads that require extremely low median and tail used in data center. However, the performance for short flows latency in order to provide soft real-time performance to users. in TCP is very poor: although in theory they can be finished These small workloads are known as short TCP flows. in 10-20 microseconds with 1G or 10G interconnects, the However, these short TCP flows experience long latencies actual flow completion time (FCT) is as high as tens of due in part to large workloads consuming most available milliseconds [2]. This is due in part to long flows consuming buffer in the switches. Imperfect routing algorithm such as some or all of the available buffers in the switches [3]. ECMP makes the matter even worse. We propose a transport Imperfect routing algorithms such as ECMP makes the matter mechanism using replication for predictability to achieve low even worse. State of the art forwarding in enterprise and data flow completion time (FCT) for short TCP flows. We center environment uses ECMP to statically direct flows implement replication for predictability within Apache Thrift across available paths using flow hashing. It doesn’t account transport layer that replicates each short TCP flow and sends for either current network utilization or flow size, and may out identical packets for both flows, then utilizes the first flow direct many long flows to the same path causing flash that finishes the transfer. -
Chapter 2 Introduction to Big Data Technology
Chapter 2 Introduction to Big data Technology Bilal Abu-Salih1, Pornpit Wongthongtham2 Dengya Zhu3 , Kit Yan Chan3 , Amit Rudra3 1The University of Jordan 2 The University of Western Australia 3 Curtin University Abstract: Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference. Keywords Big data, 3V of Big data, Cloud Computing, Data Lake, Enterprise Data Centre, PaaS, IaaS, SaaS, Hadoop, Spark, HBase, Information retrieval, Solr 2.1 Introduction The ability to exploit the ever-growing amounts of business-related data will al- low to comprehend what is emerging in the world. In this context, Big Data is one of the current major buzzwords [1]. Big Data (BD) is the technical term used in reference to the vast quantity of heterogeneous datasets which are created and spread rapidly, and for which the conventional techniques used to process, analyse, retrieve, store and visualise such massive sets of data are now unsuitable and inad- equate. This can be seen in many areas such as sensor-generated data, social media, uploading and downloading of digital media. -
Why Migrate from Mysql to Cassandra?
Why Migrate from MySQL to Cassandra? 1 Table of Contents Abstract ....................................................................................................................................................................................... 3 Introduction ............................................................................................................................................................................... 3 Why Stay with MySQL ........................................................................................................................................................... 3 Why Migrate from MySQL ................................................................................................................................................... 4 Architectural Limitations ........................................................................................................................................... 5 Data Model Limitations ............................................................................................................................................... 5 Scalability and Performance Limitations ............................................................................................................ 5 Why Migrate from MySQL ................................................................................................................................................... 6 A Technical Overview of Cassandra ..................................................................................................................... -
Using Map and Reduce for Querying Distributed XML Data
Using Map and Reduce for Querying Distributed XML Data Lukas Lewandowski Master Thesis in fulfillment of the requirements for the degree of Master of Science (M.Sc.) Submitted to the Department of Computer and Information Science at the University of Konstanz Reviewer: Prof. Dr. Marc H. Scholl Prof. Dr. Marcel Waldvogel Abstract Semi-structured information is often represented in the XML format. Although, a vast amount of appropriate databases exist that are responsible for efficiently storing semi- structured data, the vastly growing data demands larger sized databases. Even when the secondary storage is able to store the large amount of data, the execution time of complex queries increases significantly, if no suitable indexes are applicable. This situation is dramatic when short response times are an essential requirement, like in the most real-life database systems. Moreover, when storage limits are reached, the data has to be distributed to ensure availability of the complete data set. To meet this challenge this thesis presents two approaches to improve query evaluation on semi- structured and large data through parallelization. First, we analyze Hadoop and its MapReduce framework as candidate for our distributed computations and second, then we present an alternative implementation to cope with this requirements. We introduce three distribution algorithms usable for XML collections, which serve as base for our distribution to a cluster. Furthermore, we present a prototype implementation using a current open source database, named BaseX, which serves as base for our comprehensive query results. iii Acknowledgments I would like to thank my advisors Professor Marc H. Scholl and Professor Marcel Wald- vogel, who have supported me with advice and guidance throughout my master thesis. -
Apache Cassandra™ Architecture Inside Datastax Distribution of Apache Cassandra™
Apache Cassandra™ Architecture Inside DataStax Distribution of Apache Cassandra™ Inside DataStax Distribution of Apache Cassandra TABLE OF CONTENTS TABLE OF CONTENTS ......................................................................................................... 2 INTRODUCTION .................................................................................................................. 3 MOTIVATIONS FOR CASSANDRA ........................................................................................ 3 Dramatic changes in data management ....................................................................... 3 NoSQL databases ...................................................................................................... 3 About Cassandra ....................................................................................................... 4 WHERE CASSANDRA EXCELS ............................................................................................. 4 ARCHITECTURAL OVERVIEW .............................................................................................. 5 Highlights .................................................................................................................. 5 Cluster topology ......................................................................................................... 5 Logical ring structure .................................................................................................. 6 Queries, cluster-level replication ................................................................................ -
Evolutionary Tree-Structured Storage: Concepts, Interfaces, and Applications
Evolutionary Tree-Structured Storage: Concepts, Interfaces, and Applications Dissertation submitted for the degree of Doctor of Natural Sciences Presented by Marc Yves Maria Kramis at the Faculty of Sciences Department of Computer and Information Science Date of the oral examination: 22.04.2014 First supervisor: Prof. Dr. Marcel Waldvogel Second supervisor: Prof. Dr. Marc Scholl i Abstract Life is subdued to constant evolution. So is our data, be it in research, business or personal information management. From a natural, evolutionary perspective, our data evolves through a sequence of fine-granular modifications resulting in myriads of states, each describing our data at a given point in time. From a technical, anti- evolutionary perspective, mainly driven by technological and financial limitations, we treat the modifications as transient commands and only store the latest state of our data. It is surprising that the current approach is to ignore the natural evolution and to willfully forget about the sequence of modifications and therefore the past state. Sticking to this approach causes all kinds of confusion, complexity, and performance issues. Confusion, because we still somehow want to retrieve past state but are not sure how. Complexity, because we must repeatedly work around our own obsolete approaches. Performance issues, because confusion times complexity hurts. It is not surprising, however, that intelligence agencies notoriously try to collect, store, and analyze what the broad public willfully forgets. Significantly faster and cheaper random-access storage is the key driver for a paradigm shift towards remembering the sequence of modifications. We claim that (1) faster storage allows to efficiently and cleverly handle finer-granular modifi- cations and (2) that mandatory versioning elegantly exposes past state, radically simplifies the applications, and effectively lays a solid foundation for backing up, distributing and scaling of our data. -
Applying Representational State Transfer (REST) Architecture to Archetype-Based Electronic Health Record Systems
Applying representational state transfer (REST) architecture to archetype-based electronic health record systems Erik Sundvall, Mikael Nyström, Daniel Karlsson, Martin Eneling, Rong Chen and Håkan Örman Linköping University Post Print N.B.: When citing this work, cite the original article. Original Publication: Erik Sundvall, Mikael Nyström, Daniel Karlsson, Martin Eneling, Rong Chen and Håkan Örman, Applying representational state transfer (REST) architecture to archetype-based electronic health record systems, 2013, BMC Medical Informatics and Decision Making, (13), 57. http://dx.doi.org/10.1186/1472-6947-13-57 Licensee: BioMed Central http://www.biomedcentral.com/ Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-87696 Sundvall et al. BMC Medical Informatics and Decision Making 2013, 13:57 http://www.biomedcentral.com/1472-6947/13/57 SOFTWARE Open Access Applying representational state transfer (REST) architecture to archetype-based electronic health record systems Erik Sundvall1*, Mikael Nyström1, Daniel Karlsson1, Martin Eneling1, Rong Chen1,2 and Håkan Örman1 Abstract Background: The openEHR project and the closely related ISO 13606 standard have defined structures supporting the content of Electronic Health Records (EHRs). However, there is not yet any finalized openEHR specification of a service interface to aid application developers in creating, accessing, and storing the EHR content. The aim of this paper is to explore how the Representational State Transfer (REST) architectural style can be used as a basis for a platform-independent, HTTP-based openEHR service interface. Associated benefits and tradeoffs of such a design are also explored. Results: The main contribution is the formalization of the openEHR storage, retrieval, and version-handling semantics and related services into an implementable HTTP-based service interface. -
Polishing Structural Bulk Updates in a Native XML Database
Polishing Structural Bulk Updates in a Native XML Database Master thesis presented by Lukas Kircher Submitted to the Department of Computer and Information Science at the University of Konstanz Reviewers Prof. Dr. Marc H. Scholl Prof. Dr. Marcel Waldvogel October 2013 Abstract BaseX is a native XML database on the foundation of a fixed-length, sequen- tial document encoding. Built with a strong focus on reading performance, this thesis shows that the Pre/Dist/Size encoding is yet perfectly capable of handling massive bulk update transactions. Despite its theoretical limi- tations regarding structural changes of the tree, we show that performance is indeed restricted by the document order and disk access patterns. Dur- ing tests with the XQuery Update Facility (XQUF), we delete 1.8 million nodes in 22.4 seconds, evenly distributed over a 1.1GB XMark document in- stance. Compared to the prior approach, this equals a reduction of processing time by 99.99%. To achieve this, we extend the obligatory implementation of the XQUF pending update list with an additional low-level layer, that pre-calculates tree structure adjustments in-memory. This layer adds little overhead and further enables us to merge update operations and curb frag- mentation that finds its origin at the user level. To not violate the document order, the XQUF is only arduously brought together with the concept of effi- cient bulk updates. A method is introduced that imposes an order on update primitives to finally get a ready-to-apply sequence of atomic updates. The reviewed implementation is fully consistent with the XQUF specification and has already proven rock-solid efficiency in production use.