A Big Data Revolution Cloud Computing, 22 Aerospike, 91, 217 Competing Definitions, 21 Aerospike Query Language (AQL), 218 Industrial Revolution, 22 AJAX
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A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem
Noname manuscript No. (will be inserted by the editor) A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem César Soto-Valero · Nicolas Harrand · Martin Monperrus · Benoit Baudry Received: date / Accepted: date Abstract Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application’s code and its ex- ternal dependencies, and automate several software development tasks. How- ever, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that the build tool packages with the application’s compiled code but that are actually not necessary to build and run the application. This phenomenon artificially grows the size of the built binary and increases maintenance effort. We propose a tool, called DepClean, to analyze the presence of bloated dependencies in Maven artifacts. We ana- lyze 9; 639 Java artifacts hosted on Maven Central, which include a total of 723; 444 dependency relationships. Our key result is that 75:1% of the analyzed dependency relationships are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts up to 1=4 of its current count. We also perform a qualitative study with 30 notable open-source projects. Our results indicate that developers pay attention to their dependencies and are willing to remove bloated dependencies: 18/21 answered pull requests were accepted and merged by developers, removing 131 dependencies in total. -
Betrfs: a Right-Optimized Write-Optimized File System
BetrFS: A Right-Optimized Write-Optimized File System William Jannen, Jun Yuan, Yang Zhan, Amogh Akshintala, Stony Brook University; John Esmet, Tokutek Inc.; Yizheng Jiao, Ankur Mittal, Prashant Pandey, and Phaneendra Reddy, Stony Brook University; Leif Walsh, Tokutek Inc.; Michael Bender, Stony Brook University; Martin Farach-Colton, Rutgers University; Rob Johnson, Stony Brook University; Bradley C. Kuszmaul, Massachusetts Institute of Technology; Donald E. Porter, Stony Brook University https://www.usenix.org/conference/fast15/technical-sessions/presentation/jannen This paper is included in the Proceedings of the 13th USENIX Conference on File and Storage Technologies (FAST ’15). February 16–19, 2015 • Santa Clara, CA, USA ISBN 978-1-931971-201 Open access to the Proceedings of the 13th USENIX Conference on File and Storage Technologies is sponsored by USENIX BetrFS: A Right-Optimized Write-Optimized File System William Jannen, Jun Yuan, Yang Zhan, Amogh Akshintala, John Esmet∗, Yizheng Jiao, Ankur Mittal, Prashant Pandey, Phaneendra Reddy, Leif Walsh∗, Michael Bender, Martin Farach-Colton†, Rob Johnson, Bradley C. Kuszmaul‡, and Donald E. Porter Stony Brook University, ∗Tokutek Inc., †Rutgers University, and ‡Massachusetts Institute of Technology Abstract (microwrites). Examples include email delivery, creat- The Bε -tree File System, or BetrFS, (pronounced ing lock files for an editing application, making small “better eff ess”) is the first in-kernel file system to use a updates to a large file, or updating a file’s atime. The un- write-optimized index. Write optimized indexes (WOIs) derlying problem is that many standard data structures in are promising building blocks for storage systems be- the file-system designer’s toolbox optimize for one case cause of their potential to implement both microwrites at the expense of another. -
Algorithms and Complexity (AL)
Algorithms and Complexity (AL) Algorithms are fundamental to computer science and software engineering. The real-world performance of any software system depends on the algorithms chosen and the suitability of the various layers of implementation. Good algorithm design is therefore crucial for the performance of all software systems. Moreover, the study of algorithms provides insight into the intrinsic nature of the problem as well as possible solution techniques independent of programming language, programming paradigm, computer hardware, or any other implementation aspect. An important part of computing is the ability to select algorithms appropriate to particular purposes and to apply them, recognizing the possibility that no suitable algorithm may exist. This facility relies on understanding the range of algorithms that address an important set of well-defined problems, recognizing their strengths and weaknesses, and their suitability in particular contexts. Efficiency is a pervasive theme throughout this area. This knowledge area defines the central concepts and skills required to design, implement, and analyze algorithms for solving problems. Algorithms are essential in all advanced areas of computer science: artificial intelligence, databases, distributed computing, graphics, networking, operating systems, programming languages, security, and so on. Algorithms that have specific utility in each of these are listed in the relevant knowledge areas. Cryptography, for example, appears in the new Knowledge Area on Information Assurance and Security (IAS), while parallel and distributed algorithms appear in the Knowledge Area in Parallel and Distributed Computing (PD). As with all knowledge areas, the order of topics and their groupings do not necessarily correlate to a specific order of presentation. Different programs will teach the topics in different courses and should do so in the order they believe is most appropriate for their students. -
Assignment of Master's Thesis
CZECH TECHNICAL UNIVERSITY IN PRAGUE FACULTY OF INFORMATION TECHNOLOGY ASSIGNMENT OF MASTER’S THESIS Title: Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods Student: Bc. Anna Kučerová Supervisor: Ing. Luboš Krčál Study Programme: Informatics Study Branch: Knowledge Engineering Department: Department of Theoretical Computer Science Validity: Until the end of winter semester 2018/19 Instructions Sparse multidimensional arrays are a common data structure for effective storage, analysis, and visualization of scientific datasets. Approximate pattern matching and processing is essential in many scientific domains. Previous algorithms focused on deterministic filtering and aggregate matching using synopsis style indexing. However, little work has been done on application of heuristic based machine learning methods for these approximate array pattern matching tasks. Research current methods for multidimensional array pattern matching, discovery, and processing. Propose a method for array pattern matching and processing tasks utilizing machine learning methods, such as kernels, clustering, or PSO in conjunction with inverted indexing. Implement the proposed method and demonstrate its efficiency on both artificial and real world datasets. Compare the algorithm with deterministic solutions in terms of time and memory complexities and pattern occurrence miss rates. References Will be provided by the supervisor. doc. Ing. Jan Janoušek, Ph.D. prof. Ing. Pavel Tvrdík, CSc. Head of Department Dean Prague February 28, 2017 Czech Technical University in Prague Faculty of Information Technology Department of Knowledge Engineering Master’s thesis Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods Bc. Anna Kuˇcerov´a Supervisor: Ing. LuboˇsKrˇc´al 9th May 2017 Acknowledgements Main credit goes to my supervisor Ing. -
UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ Optimizing Large
UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ Colegio de Ciencias e Ingenierías Optimizing Large Databases: A Study on Index Structures Proyecto de Investigación . Ricardo Andres Leon Ruiz Ingeniería en Sistemas Trabajo de titulación presentado como requisito para la obtención del título de Ingeniero en Sistemas Quito, 22 de Diciembre de 2017 2 UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ COLEGIO DE CIENCIAS E INGENIERÍAS HOJA DE CALIFICACIÓN DE TRABAJO DE TITULACIÓN Optimizing Large Databases: A Study on Index Structures Ricardo Andres Leon Ruiz Calificación: Nombre del profesor, Título académico Aldo Cassola, Ph.D. Firma del profesor Quito, Diciembre de 2017 3 Derechos de Autor Por medio del presente documento certifico que he leído todas las Políticas y Manuales de la Universidad San Francisco de Quito USFQ, incluyendo la Política de Propiedad Intelectual USFQ, y estoy de acuerdo con su contenido, por lo que los derechos de propiedad intelectual del presente trabajo quedan sujetos a lo dispuesto en esas Políticas. Asimismo, autorizo a la USFQ para que realice la digitalización y publicación de este trabajo en el repositorio virtual, de conformidad a lo dispuesto en el Art. 144 de la Ley Orgánica de Educación Superior. Firma del estudiante: Nombres y Apellidos: Ricardo Andres Leon Ruiz Código de estudiante: 110346 C. I.: 1714286265 Lugar, Fecha Quito, 22 de Diciembre de 2017 4 RESUMEN La siguiente investigación trata sobre comparar estructuras de índice para grandes bases de datos, tanto analíticamente, como experimentalmente. El estudio se encuentra dividido en dos partes principales. La primera parte se centra en índices de hash y B-trees. Ambas estructuras son estudiadas en el contexto del modelo de acceso de disco tradicional. -
Performance Tuning Apache Drill on Hadoop Clusters with Evolutionary Algorithms
Performance tuning Apache Drill on Hadoop Clusters with Evolutionary Algorithms Proposing the algorithm SIILCK (Society Inspired Incremental Learning through Collective Knowledge) Roger Bløtekjær Thesis submitted for the degree of Master of science in Informatikk: språkteknologi 30 credits Institutt for informatikk Faculty of mathematics and natural sciences UNIVERSITY OF OSLO Spring 2018 Performance tuning Apache Drill on Hadoop Clusters with Evolutionary Algorithms Proposing the algorithm SIILCK (Society Inspired Incremental Learning through Collective Knowledge) Roger Bløtekjær c 2018 Roger Bløtekjær Performance tuning Apache Drill on Hadoop Clusters with Evolutionary Algorithms http://www.duo.uio.no/ Printed: Reprosentralen, University of Oslo 0.1 Abstract 0.1.1 Research question How can we make a self optimizing distributed Apache Drill cluster, for high performance data readings across several file formats and database architec- tures? 0.1.2 Overview Apache Drill enables the user to perform schema-free querying of distributed data, and ANSI SQL programming on top of NoSQL datatypes like JSON or XML - typically in a Hadoop cluster. As it is with the core Hadoop stack, Drill is also highly customizable, with plenty of performance tuning parame- ters to ensure optimal efficiency. Tweaking these parameters however, requires deep domain knowledge and technical insight, and even then the optimal con- figuration may not be evident. Businesses will want to use Apache Drill in a Hadoop cluster, without the hassle of configuring it, for the most cost-effective implementation. This can be done by solving the following problems: • How to apply evolutionary algorithms to automatically tune a distributed Apache Drill configuration, regardless of cluster environment. -
Conception Et Exploitation D'une Base De Modèles: Application Aux Data
Conception et exploitation d’une base de modèles : application aux data sciences Cyrille Ponchateau To cite this version: Cyrille Ponchateau. Conception et exploitation d’une base de modèles : application aux data sciences. Autre [cs.OH]. ISAE-ENSMA Ecole Nationale Supérieure de Mécanique et d’Aérotechique - Poitiers, 2018. Français. NNT : 2018ESMA0005. tel-01939430 HAL Id: tel-01939430 https://tel.archives-ouvertes.fr/tel-01939430 Submitted on 29 Nov 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE pour l’obtention du Grade de DOCTEUR DE L'ÉCOLE NATIONALE SUPÉRIEURE DE MÉCANIQUE ET D'AÉROTECHNIQUE (Diplôme National — Arrêté du 25 mai 2016) Ecole Doctorale : Sciences et Ingénierie des Systèmes, Mathématiques, Informatique (SISMI) Secteur de Recherche : INFORMATIQUE ET APPLICATIONS Présentée par : Cyrille PONCHATEAU ******************************************************** Conception et exploitation d’une base de modèles : application aux data sciences ******************************************************** Directeur de thèse : Ladjel BELLATRECHE -
Apache Calcite: a Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources Edmon Begoli Jesús Camacho-Rodríguez Julian Hyde Oak Ridge National Laboratory Hortonworks Inc. Hortonworks Inc. (ORNL) Santa Clara, California, USA Santa Clara, California, USA Oak Ridge, Tennessee, USA [email protected] [email protected] [email protected] Michael J. Mior Daniel Lemire David R. Cheriton School of University of Quebec (TELUQ) Computer Science Montreal, Quebec, Canada University of Waterloo [email protected] Waterloo, Ontario, Canada [email protected] ABSTRACT argued that specialized engines can offer more cost-effective per- Apache Calcite is a foundational software framework that provides formance and that they would bring the end of the “one size fits query processing, optimization, and query language support to all” paradigm. Their vision seems today more relevant than ever. many popular open-source data processing systems such as Apache Indeed, many specialized open-source data systems have since be- Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite’s ar- come popular such as Storm [50] and Flink [16] (stream processing), chitecture consists of a modular and extensible query optimizer Elasticsearch [15] (text search), Apache Spark [47], Druid [14], etc. with hundreds of built-in optimization rules, a query processor As organizations have invested in data processing systems tai- capable of processing a variety of query languages, an adapter ar- lored towards their specific needs, two overarching problems have chitecture designed for extensibility, and support for heterogeneous arisen: data models and stores (relational, semi-structured, streaming, and • The developers of such specialized systems have encoun- geospatial). This flexible, embeddable, and extensible architecture tered related problems, such as query optimization [4, 25] is what makes Calcite an attractive choice for adoption in big- or the need to support query languages such as SQL and data frameworks. -
Big Data Solutions and Applications
P. Bellini, M. Di Claudio, P. Nesi, N. Rauch Slides from: M. Di Claudio, N. Rauch Big Data Solutions and applications Slide del corso: Sistemi Collaborativi e di Protezione Corso di Laurea Magistrale in Ingegneria 2013/2014 http://www.disit.dinfo.unifi.it Distributed Data Intelligence and Technology Lab 1 Related to the following chapter in the book: • P. Bellini, M. Di Claudio, P. Nesi, N. Rauch, "Tassonomy and Review of Big Data Solutions Navigation", in "Big Data Computing", Ed. Rajendra Akerkar, Western Norway Research Institute, Norway, Chapman and Hall/CRC press, ISBN 978‐1‐ 46‐657837‐1, eBook: 978‐1‐ 46‐657838‐8, july 2013, in press. http://www.tmrfindia.org/b igdata.html 2 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 3 Index 6. Access/Data Rendering aspects 7. Data Analysis and Mining/Ingestion Aspects 8. Comparison and Analysis of Architectural Features • Data Management • Data Access and Visual Rendering • Mining/Ingestion • Data Analysis • Architecture 4 Part 1 The Big Data Problem. 5 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 6 What is Big Data? Variety Volume Value 5V’s of Big Data Velocity Variability 7 5V’s of Big Data (1/2) • Volume: Companies amassing terabytes/petabytes of information, and they always look for faster, more efficient, and lower‐ cost solutions for data management. -
Umltographdb: Mapping Conceptual Schemas to Graph Databases
Citation for published version Daniel, G., Sunyé, G. & Cabot, J. (2016). UMLtoGraphDB: Mapping Conceptual Schemas to Graph Databases. Lecture Notes in Computer Science, 9974(), 430-444. DOI https://doi.org/10.1007/978-3-319-46397-1_33 Document Version This is the Submitted Manuscript version. The version in the Universitat Oberta de Catalunya institutional repository, O2 may differ from the final published version. https://doi.org/10.1007/978-3-319-61482-3_16 Copyright and Reuse This manuscript version is made available under the terms of the Creative Commons Attribution Non Commercial No Derivatives licence (CC-BY-NC-ND) http://creativecommons.org/licenses/by-nc-nd/3.0/es/, which permits others to download it and share it with others as long as they credit you, but they can’t change it in any way or use them commercially. Enquiries If you believe this document infringes copyright, please contact the Research Team at: [email protected] Universitat Oberta de Catalunya Research archive UMLtoGraphDB: Mapping Conceptual Schemas to Graph Databases Gwendal Daniel1, Gerson Sunyé1, and Jordi Cabot2;3 1 AtlanMod Team Inria, Mines Nantes & Lina {gwendal.daniel,gerson.sunye}@inria.fr 2 ICREA [email protected] 3 Internet Interdisciplinary Institute, UOC Abstract. The need to store and manipulate large volume of (unstructured) data has led to the development of several NoSQL databases for better scalability. Graph databases are a particular kind of NoSQL databases that have proven their efficiency to store and query highly interconnected data, and have become a promising solution for multiple applications. While the mapping of conceptual schemas to relational databases is a well-studied field of research, there are only few solutions that target conceptual modeling for NoSQL databases and even less focusing on graph databases. -
The Tokufs Streaming File System
The TokuFS Streaming File System John Esmet Michael A. Bender Martin Farach-Colton Bradley C. Kuszmaul Tokutek & Tokutek & Tokutek & Tokutek & Rutgers Stony Brook Rutgers MIT Abstract files can be created and updated rapidly, but queries, such as reads or metadata lookups, may suffer from the lack The TokuFS file system outperforms write-optimized file of an up-to-date index or from poor locality in indexes systems by an order of magnitude on microdata write that are spread through the log. workloads, and outperforms read-optimized file systems by an order of magnitude on read workloads. Microdata Large-block reads and writes, which we call macro- write workloads include creating and destroying many data operations, can easily run at disk bandwidth. For small files, performing small unaligned writes within small writes, which we call microdata operations, in large files, and updating metadata. TokuFS is imple- which the bandwidth time to write the data is much mented using Fractal Tree indexes, which are primarily smaller than a disk seek, the tradeoff becomes more se- used in databases. TokuFS employs block-level com- vere. Examples of microdata operations include creating pression to reduce its disk usage. or destroying microfiles (small files), performing small writes within large files, and updating metadata (e.g., in- ode updates). 1 Introduction In this paper, we introduce the TokuFS file system. File system designers often must choose between good TokuFS achieves good performance for both reads and read performance and good write performance. For ex- writes and for both microdata and macrodata. Compared ample, most of today’s file systems employ some com- to ext4, XFS, Btrfs, and ZFS, TokuFS runs at least 18.4 bination of B-trees and log-structured updates to achieve times faster than the nearest competitor (Btrfs) for cre- a good tradeoff between reads and writes. -
Data Structures Dagstuhl Seminar
10091 Abstracts Collection Data Structures Dagstuhl Seminar Lars Arge1, Erik Demaine2 and Raimund Seidel3 1 Univ. of Aarhus, DK [email protected] 2 MIT - Cambridge, US [email protected] 3 Saarland University, DE [email protected] Abstract. From February 28th to March 5th 2010, the Dagstuhl Sem- inar 10091 "Data Structures" was held in Schloss Dagstuhl Leibniz Center for Informatics. It brought together 45 international researchers to discuss recent developments concerning data structures in terms of re- search, but also in terms of new technologies that impact how data can be stored, updated, and retrieved. During the seminar a fair number of participants presented their current research and open problems where discussed. This document rst briey describes the seminar topics and then gives the abstracts of the presentations given during the seminar. Keywords. Data structures, information retrieval, complexity, algorithms 10091 Summary - Data Structures The purpose of this workshop was to discuss recent developments in various aspects of data structure research, and also to familiarize the community with some of the problems that arise in the context of modern commodity parallel hardware architectures, such as multicore and GPU architectures. Thus while several attendees reported on progress on (twists on) old fundamental problems in data structures e.g. Gerth Brodal, Rolf Fagerberg, John Iacono and Sid- dharrha Sen on search tree and dictionary structures, Bob Tarjan on heaps, Kasper D. Larsen and Peyman Afshani on range search data structures, and Peter Sanders and Michiel Smid on proximity data structures there were also very inspiring presentations on new models of computation by Erik Demaine and on data structures on the GPU by John Owens.