A Survey of Embedded Database Technology for Mobile Applications
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Getting Started with Derby Version 10.14
Getting Started with Derby Version 10.14 Derby Document build: April 6, 2018, 6:13:12 PM (PDT) Version 10.14 Getting Started with Derby Contents Copyright................................................................................................................................3 License................................................................................................................................... 4 Introduction to Derby........................................................................................................... 8 Deployment options...................................................................................................8 System requirements.................................................................................................8 Product documentation for Derby........................................................................... 9 Installing and configuring Derby.......................................................................................10 Installing Derby........................................................................................................ 10 Setting up your environment..................................................................................10 Choosing a method to run the Derby tools and startup utilities...........................11 Setting the environment variables.......................................................................12 Syntax for the derbyrun.jar file............................................................................13 -
Operational Database Offload
Operational Database Offload Partner Brief Facing increased data growth and cost pressures, scale‐out technology has become very popular as more businesses become frustrated with their costly “Our partnership with Hortonworks is able to scale‐up RDBMSs. With Hadoop emerging as the de facto scale‐out file system, a deliver to our clients 5‐10x faster performance Hadoop RDBMS is a natural choice to replace traditional relational databases and over 75% reduction in TCO over traditional scale‐up databases. With Splice like Oracle and IBM DB2, which struggle with cost or scaling issues. Machine’s SQL‐based transactional processing Designed to meet the needs of real‐time, data‐driven businesses, Splice engine, our clients are able to migrate their legacy database applications without Machine is the only Hadoop RDBMS. Splice Machine offers an ANSI‐SQL application rewrites” database with support for ACID transactions on the distributed computing Monte Zweben infrastructure of Hadoop. Like Oracle and MySQL, it is an operational database Chief Executive Office that can handle operational (OLTP) or analytical (OLAP) workloads, while scaling Splice Machine out cost‐effectively from terabytes to petabytes on inexpensive commodity servers. Splice Machine, a technology partner with Hortonworks, chose HBase and Hadoop as its scale‐out architecture because of their proven auto‐sharding, replication, and failover technology. This partnership now allows businesses the best of all worlds: a standard SQL database, the proven scale‐out of Hadoop, and the ability to leverage current staff, operations, and applications without specialized hardware or significant application modifications. What Business Challenges are Solved? Leverage Existing SQL Tools Cost Effective Scaling Real‐Time Updates Leveraging the proven SQL processing of Splice Machine leverages the proven Splice Machine provides full ACID Apache Derby, Splice Machine is a true ANSI auto‐sharding of HBase to scale with transactions across rows and tables by using SQL database on Hadoop. -
JACSM No 1 2009
STORE: EMBEDDED PERSISTENT STORAGE FOR CLOJURE PROGRAMMING LANGUAGE Konrad Grzanek1 1IT Institute, Academy of Management, Lodz, Poland [email protected] Abstract Functional programming is the most popular declarative style of programming. Its lack of state leads to an increase of programmers' productivity and software robustness. Clojure is a very effective Lisp dialect, but it misses a solid embedded database implementation. A store is a proposed embedded database engine for Clojure that helps to deal with the problem of the inevitable state by mostly functional, minimalistic interface, abandoning SQL and tight integration with Clojure as a sole query and data-processing language. Key words: Functional programming, Lisp, Clojure, embedded database 1 Introduction Functional programming languages and functional programming style in general have been gaining a growing attention in the recent years. Lisp created by John McCarthy and specified in [8] is the oldest functional pro- gramming language. Some of its flavors (dialects, as some say [9]) are still in use today. Common Lisp was the first ANSI standardized Lisp dialect [13] and Common Lisp Object System (CLOS) was probably the first ANSI stan- dardized object oriented programming language [14]. Apart from its outstand- ing features as a Common Lisp subset. Various Lisps were used in artificial intelligence [11] and to some extent the language comes from AI labs and its ecosystem. Common Lisp was used as the language of choice by some AI tutors, like Peter Norvig (in [10]). But the whole family of languages address general problems in computer science, not only these in AI. John Backus argues [3] that the functional style is a real liberation from the traditional imperative languages and their problems. -
Data Analysis Expressions (DAX) in Powerpivot for Excel 2010
Data Analysis Expressions (DAX) In PowerPivot for Excel 2010 A. Table of Contents B. Executive Summary ............................................................................................................................... 3 C. Background ........................................................................................................................................... 4 1. PowerPivot ...............................................................................................................................................4 2. PowerPivot for Excel ................................................................................................................................5 3. Samples – Contoso Database ...................................................................................................................8 D. Data Analysis Expressions (DAX) – The Basics ...................................................................................... 9 1. DAX Goals .................................................................................................................................................9 2. DAX Calculations - Calculated Columns and Measures ...........................................................................9 3. DAX Syntax ............................................................................................................................................ 13 4. DAX uses PowerPivot data types ......................................................................................................... -
Lesson 17 Building Xqueries in Xquery Editor View
AquaLogic Data Services Platform™ Tutorial: Part II A Guide to Developing BEA AquaLogic Data Services Platform (DSP) Projects Note: This tutorial is based in large part on a guide originally developed for enterprises evaluating Data Services Platform for specific requirements. In some cases illustrations, directories, and paths reference Liquid Data, the previous name of the Data Services Platform. Version: 2.1 Document Date: June 2005 Revised: June 2006 Copyright Copyright © 2005, 2006 BEA Systems, Inc. All Rights Reserved. Restricted Rights Legend This software and documentation is subject to and made available only pursuant to the terms of the BEA Systems License Agreement and may be used or copied only in accordance with the terms of that agreement. It is against the law to copy the software except as specifically allowed in the agreement. This document may not, in whole or in part, be copied photocopied, reproduced, translated, or reduced to any electronic medium or machine readable form without prior consent, in writing, from BEA Systems, Inc. Use, duplication or disclosure by the U.S. Government is subject to restrictions set forth in the BEA Systems License Agreement and in subparagraph (c)(1) of the Commercial Computer Software- Restricted Rights Clause at FAR 52.227-19; subparagraph (c)(1)(ii) of the Rights in Technical Data and Computer Software clause at DFARS 252.227-7013, subparagraph (d) of the Commercial Computer Software--Licensing clause at NASA FAR supplement 16-52.227-86; or their equivalent. Information in this document is subject to change without notice and does not represent a commitment on the part of BEA Systems. -
Tuning Derby Version 10.14
Tuning Derby Version 10.14 Derby Document build: April 6, 2018, 6:14:42 PM (PDT) Version 10.14 Tuning Derby Contents Copyright................................................................................................................................4 License................................................................................................................................... 5 About this guide....................................................................................................................9 Purpose of this guide................................................................................................9 Audience..................................................................................................................... 9 How this guide is organized.....................................................................................9 Performance tips and tricks.............................................................................................. 10 Use prepared statements with substitution parameters......................................10 Create indexes, and make sure they are being used...........................................10 Ensure that table statistics are accurate.............................................................. 10 Increase the size of the data page cache............................................................. 11 Tune the size of database pages...........................................................................11 Performance trade-offs of large pages.............................................................. -
Rdbmss Why Use an RDBMS
RDBMSs • Relational Database Management Systems • A way of saving and accessing data on persistent (disk) storage. 51 - RDBMS CSC309 1 Why Use an RDBMS • Data Safety – data is immune to program crashes • Concurrent Access – atomic updates via transactions • Fault Tolerance – replicated dbs for instant failover on machine/disk crashes • Data Integrity – aids to keep data meaningful •Scalability – can handle small/large quantities of data in a uniform manner •Reporting – easy to write SQL programs to generate arbitrary reports 51 - RDBMS CSC309 2 1 Relational Model • First published by E.F. Codd in 1970 • A relational database consists of a collection of tables • A table consists of rows and columns • each row represents a record • each column represents an attribute of the records contained in the table 51 - RDBMS CSC309 3 RDBMS Technology • Client/Server Databases – Oracle, Sybase, MySQL, SQLServer • Personal Databases – Access • Embedded Databases –Pointbase 51 - RDBMS CSC309 4 2 Client/Server Databases client client client processes tcp/ip connections Server disk i/o server process 51 - RDBMS CSC309 5 Inside the Client Process client API application code tcp/ip db library connection to server 51 - RDBMS CSC309 6 3 Pointbase client API application code Pointbase lib. local file system 51 - RDBMS CSC309 7 Microsoft Access Access app Microsoft JET SQL DLL local file system 51 - RDBMS CSC309 8 4 APIs to RDBMSs • All are very similar • A collection of routines designed to – produce and send to the db engine an SQL statement • an original -
Eclipselink Understanding Eclipselink 2.4
EclipseLink Understanding EclipseLink 2.4 June 2013 EclipseLink Concepts Guide Copyright © 2012, 2013, by The Eclipse Foundation under the Eclipse Public License (EPL) http://www.eclipse.org/org/documents/epl-v10.php The initial contribution of this content was based on work copyrighted by Oracle and was submitted with permission. Print date: July 9, 2013 Contents Preface ............................................................................................................................................................... xiii Audience..................................................................................................................................................... xiii Related Documents ................................................................................................................................... xiii Conventions ............................................................................................................................................... xiii 1 Overview of EclipseLink 1.1 Understanding EclipseLink....................................................................................................... 1-1 1.1.1 What Is the Object-Persistence Impedance Mismatch?.................................................. 1-3 1.1.2 The EclipseLink Solution.................................................................................................... 1-3 1.2 Key Features ............................................................................................................................... -
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 -
20140620075114!12305
Enhancement of Open Source Monitoring Tool for Small Footprint Databases Dissertation Submitted in partial fulfillment of the requirements for the degree of Master of Technology in Computer Science and Engineering Submitted by Sukh Deo Roll No. 123050061 Under the Guidance of Prof. Deepak B. Phatak Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai, Mumbai, India 400076 June, 2014 Dissertation Approval Certificate Department of Computer Science and Engineering Indian Institute of Technology, Bombay The dissertation entitled \Enhancement of Open Source Monitoring Tool for Small Footprint Databases", submitted by Sukh Deo (Roll No: 123050061) is approved for the degree of Master of Technology in Computer Science and Engineering from Indian Institute of Technology, Bombay. Prof. Deepak B. Phatak Dept. of CSE, IIT Bombay Supervisor Internal Examiner External Examiner Place: IIT Bombay, Mumbai Date: June, 2014 Declaration I declare that this written submission represents my ideas in my own words and where other's ideas or words have been included, I have adequately cited and referenced the original sources. I also declare that I have adhered to all principles of academic honesty and integrity and have not misrepresented or fabricated or falsified any idea/data/fact/source in my submission. I un- derstand that any violation of the above will be cause for disciplinary action by the Institute and can also evoke penal action from the sources which have thus not been properly cited or from whom proper permission has not been taken when needed. Signature Name of Student Roll number Date 3 Acknowledgement I would like to thank my guide, Prof. -
LIST of NOSQL DATABASES [Currently 150]
Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features. -
Sentiment Analysis Using a Novel Human Computation Game
Sentiment Analysis Using a Novel Human Computation Game Claudiu-Cristian Musat THISONE Alireza Ghasemi Boi Faltings Artificial Intelligence Laboratory (LIA) Ecole Polytechnique Fed´ erale´ de Lausanne (EPFL) IN-Ecublens, 1015 Lausanne, Switzerland [email protected] Abstract data is obtained from people using human computa- tion platforms and games. We also prove that the In this paper, we propose a novel human com- method can provide not only labelled texts, but peo- putation game for sentiment analysis. Our ple also help by selecting sentiment-expressing fea- game aims at annotating sentiments of a col- tures that can generalize well. lection of text documents and simultaneously constructing a highly discriminative lexicon of Human computation is a newly emerging positive and negative phrases. paradigm. It tries to solve large-scale problems by Human computation games have been widely utilizing human knowledge and has proven useful used in recent years to acquire human knowl- in solving various problems (Von Ahn and Dabbish, edge and use it to solve problems which are 2004; Von Ahn, 2006; Von Ahn et al., 2006a). infeasible to solve by machine intelligence. To obtain high quality solution from human com- We package the problems of lexicon construc- putation, people should be motivated to make their tion and sentiment detection as a single hu- best effort. One way to incentivize people for sub- man computation game. We compare the re- mitting high-quality results is to package the prob- sults obtained by the game with that of other well-known sentiment detection approaches. lem at hand as a game and request people to play Obtained results are promising and show im- it.