Implementation of Portable Expath Extension Functions
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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. -
Porovnanie Dostupných Implementácií XML Databáz
Masarykova univerzita Fakulta informatiky Porovnanie dostupných implementácií XML databáz Bakalárska práca Milan Mozolák Brno, jar 2021 Masarykova univerzita Fakulta informatiky Porovnanie dostupných implementácií XML databáz Bakalárska práca Milan Mozolák Brno, jar 2021 Na tomto mieste sa v tlačenej práci nachádza oficiálne podpísané zadanie práce a vyhlásenie autora školského diela. Vyhlásenie Vyhlasujem, že táto bakalárska práca je mojím pôvodným autorským dielom, ktoré som vypracoval samostatne. Všetky zdroje, pramene a literatúru, ktoré som pri vypracovaní používal alebo z nich čerpal, v práci riadne citujem s uvedením úplného odkazu na príslušný zdroj. Milan Mozolák Vedúci práce: RNDr. Adam Rambousek, Ph.D. i Poďakovanie Ďakujem vedúcemu práce RNDr. Adamovi Rambouskovi, Ph.D. za pomoc a ochotu pri vypracovávaní práce. iii Zhrnutie Cieľom práce je otestovať dostupné implementácie open source XML databáz a vybrať najvhodnejšiu pre potreby Centra spracovania pri- rodzeného jazyka. Databázy budú naplnené slovníkovými dátami a otestované rôznymi typmi dotazov. Práca bude obsahovať popis vy- braných databáz, priebehu testovania a dosiahnutých výsledkov aj s odporúčaním. iv Kľúčové slová XML, XML databázy, Native XML databázy, XML-enabled databázy, eXist, Berkeley DB XML, BaseX, Sedna, PostgreSQL, MySQL v Obsah 1 Úvod 1 2 XML Dokument 3 2.1 Štruktúra XML . .3 2.2 Rozdelenie XML dokumentov . .3 2.2.1 Dokumentovo orientované XML dokumenty . .3 2.2.2 Dátovo orientované XML dokumenty . .4 2.2.3 Hybridné XML dokumenty . .4 2.3 Ukladanie XML dát . .4 2.4 XML dátový model . .4 2.5 Round-tripping . .4 3 XML dáta v databáze 7 3.1 Ukladanie dát . .7 3.1.1 Dokumentové ukladanie XML dát . -
XML Prague 2015
XML Prague 2015 Conference Proceedings University of Economics, Prague Prague, Czech Republic February 13–15, 2015 XML Prague 2015 – Conference Proceedings Copyright © 2015 Jiří Kosek ISBN 978-80-260-7667-4 Table of Contents General Information ..................................................................................................... vii Sponsors ........................................................................................................................... ix Preface .............................................................................................................................. xi Parallel Processing in the Saxon XSLT Processor – Michael Kay ............................... 1 Parallel XSLT Processing of Large Documents – Jakub Malý ................................... 15 Semantic Hybridization: Mixing RDFa and JSON-LD – R. Alexander Miłowski ..................................................................................................... 29 Using DocBook to Produce a Polyvalent Academic Work – Murray Maloney, Robert J. Glushko, and R. Alexander Milowski .................................. 35 Generation of a “semantic” eBook: all you need is XML – Vincent Gros, Jean-Claude Moissinac, and Luc Audrain ................................................ 55 Building Security Analytics using Native XML Database – Mansi Sheth .............. 71 Node search preceding node construction – Hans-Jürgen Rennau .......................... 87 Native XML Databases: Death or Coming of Age? – Craig Brown, Xavier Franc, Michael -
Storage Solutions for Big Data Systems: a Qualitative Study and Comparison
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison Samiya Khana,1, Xiufeng Liub, Syed Arshad Alia, Mansaf Alama,2 aJamia Millia Islamia, New Delhi, India bTechnical University of Denmark, Denmark Highlights Provides a classification of NoSQL solutions on the basis of their supported data model, which may be data- oriented, graph, key-value or wide-column. Performs feature analysis of 80 NoSQL solutions in view of technology selection criteria for big data systems along with a cumulative evaluation of appropriate and inappropriate use cases for each of the data model. Classifies big data file formats into five categories namely text-based, row-based, column-based, in-memory and data storage services. Compares available data file formats, analyzing benefits and shortcomings, and use cases for each data file format. Evaluates the challenges associated with shift of next-generation big data storage towards decentralized storage and blockchain technologies. Abstract Big data systems‘ development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems‘ design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real-world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. -
Contents History Types and Examples of Nosql Databases
NoSQL From Wikipedia, the free encyclopedia A NoSQL (originally referring to "non SQL" or "non relational")[1] database provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the "NoSQL" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies such as Facebook, Google and Amazon.com.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL- like query languages.[7][8] Motivations for this approach include: simplicity of design, simpler "horizontal" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as "more flexible" than relational database tables.[9] Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc JOINs across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB and OrientDB have made them central to their designs. -
Bivariate, Cluster and Suitability Analysis of Nosql Solutions for Different Application Areas
Bivariate, Cluster and Suitability Analysis of NoSQL Solutions for Different Application Areas Samiya Khana,1, Xiufeng Liub, Syed Arshad Alia, Mansaf Alama,2 aJamia Millia Islamia, New Delhi, India bTechnical University of Denmark, Denmark Highlights Performs feature analysis of 80 NoSQL solutions in view of technology selection criteria for big data systems and evaluates applications that use one of more of the mentioned NoSQL solutions. Presents quantitative analysis of a dataset created from qualitative analysis of 80 NoSQL solutions, investigating relationships between the chosen features. Proposes a classification scheme that uses 9 features, which are ‘supported data model’ (document-oriented, graph, key-value and wide-column), CAP (consistency, availability and partition tolerance) characteristics and free/proprietary ownership, on the basis of results obtained for k-modes clustering of the dataset. Determines relevant features for each class of applications and proposes a prediction model for determining the suitability of a NoSQL solution for a class of applications. Abstract Big data systems‘ development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems‘ design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for development of an optimized solution to a specific real-world problem, big data systems are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL is the right technology that fulfills its requirements.