Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs"
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Apache Apex: Next Gen Big Data Analytics
Apache Apex: Next Gen Big Data Analytics Thomas Weise <[email protected]> @thweise PMC Chair Apache Apex, Architect DataTorrent Apache Big Data Europe, Sevilla, Nov 14th 2016 Stream Data Processing Data Delivery Transform / Analytics Real-time visualization, … Declarative SQL API Data Beam Beam SAMOA Operator SAMOA DAG API Sources Library Events Logs Oper1 Oper2 Oper3 Sensor Data Social Databases CDC (roadmap) 2 Industries & Use Cases Financial Services Ad-Tech Telecom Manufacturing Energy IoT Real-time Call detail record customer facing (CDR) & Supply chain Fraud and risk Smart meter Data ingestion dashboards on extended data planning & monitoring analytics and processing key performance record (XDR) optimization indicators analysis Understanding Reduce outages Credit risk Click fraud customer Preventive & improve Predictive assessment detection behavior AND maintenance resource analytics context utilization Packaging and Improve turn around Asset & Billing selling Product quality & time of trade workforce Data governance optimization anonymous defect tracking settlement processes management customer data HORIZONTAL • Large scale ingest and distribution • Enforcing data quality and data governance requirements • Real-time ELTA (Extract Load Transform Analyze) • Real-time data enrichment with reference data • Dimensional computation & aggregation • Real-time machine learning model scoring 3 Apache Apex • In-memory, distributed stream processing • Application logic broken into components (operators) that execute distributed in a cluster • -
The Cloud‐Based Demand‐Driven Supply Chain
The Cloud-Based Demand-Driven Supply Chain Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions. Titles in the Wiley & SAS Business Series include: The Analytic Hospitality Executive by Kelly A. McGuire Analytics: The Agile Way by Phil Simon Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by Bart Baesens A Practical Guide to Analytics for Governments: Using Big Data for Good by Marie Lowman Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs Business Analytics for Customer Intelligence by Gert Laursen Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron Business Intelligence and the Cloud: Strategic Implementation Guide by Michael S. Gendron Business Transformation: A Roadmap for Maximizing Organizational Insights by Aiman Zeid Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry by Laura Madsen Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs ii Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase Demand-Driven Inventory -
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 -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
Opening Plenary State of the Feather
Opening Plenary Lars Eilebrecht V.P., Conference Planning at ASF and Lead for ApacheCon Europe 2009 State of the Feather Jim Jagielski Chairman, The Apache Software Foundation Welcome to Amsterdam Presented by The Apache Software Foundation Produced by Stone Circle Productions, Inc. Conference Program • Detailed conference program guide available as a PDF from the ApacheCon Web site – www.eu.apachecon.com • Printed Conference-at-a- Glance program available at registration desk Presentations • 4 Tracks every day starting at 9:00 • Presentation slides provided by speakers will be made available on the ApacheCon Web site during the conference Wednesday Special Events • 9:15-9:30: Jim Jagielski “State of the Feather” • 9:30-10:30: Raghu Ramakrishnan “Data Management in the Cloud” • 10:30-11:30: Arjé Cahn, Ajay Anand, Steve Loughran, and Mark Brewer “Panel: The Business of Open Source”, moderated by Sally Khudairi • 13:00-14:00: Lars Eilebrecht “Behind the Scenes of The ASF” Wednesday Special Events • 18:30-20:00: Welcome Reception and ASF 10th Anniversary Party – Celebrating a Decade of Open Source Leadership • 19:30: OpenPGP Key Signing – [email protected] – moderated by Jean-Frederic Clere Thursday Special Events • 13:00-14:00: Jim Jagielski “Sponsoring the ASF at the Corporate and Individual Level” • 17:30-18:30: James Governor “Open Sourcing The Analyst Business – Turning Prop. Knowledge Inside Out” • 18:30-20:00: “Lightning Talks”, mod. by Danese Cooper and Rich Bowen Friday Special Events • 11:30-13:00: Lars Eilebrecht, Dirk- Willem van Gulik, Jim Jagielski, Sally Khudairi, Cliff Skolnick, “Apache Pioneer's Panel – 10 years of the ASF”, mod. -
Assessment of Multiple Ingest Strategies for Accumulo Key-Value Store
Assessment of Multiple Ingest Strategies for Accumulo Key-Value Store by Hai Pham A thesis submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Master of Science Auburn, Alabama May 7, 2016 Keywords: Accumulo, noSQL, ingest Copyright 2016 by Hai Pham Approved by Weikuan Yu, Co-Chair, Associate Professor of Computer Science, Florida State University Saad Biaz, Co-Chair, Professor of Computer Science and Software Engineering, Auburn University Sanjeev Baskiyar, Associate Professor of Computer Science and Software Engineering, Auburn University Abstract In recent years, the emergence of heterogeneous data, especially of the unstructured type, has been extremely rapid. The data growth happens concurrently in 3 dimensions: volume (size), velocity (growth rate) and variety (many types). This emerging trend has opened a new broad area of research, widely accepted as Big Data, which focuses on how to acquire, organize and manage huge amount of data effectively and efficiently. When coping with such Big Data, the traditional approach using RDBMS has been inefficient; because of this problem, a more efficient system named noSQL had to be created. This thesis will give an overview knowledge on the aforementioned noSQL systems and will then delve into a more specific instance of them which is Accumulo key-value store. Furthermore, since Accumulo is not designed with an ingest interface for users, this thesis focuses on investigating various methods for ingesting data, improving the performance and dealing with numerous parameters affecting this process. ii Acknowledgments First and foremost, I would like to express my profound gratitude to Professor Yu who with great kindness and patience has guided me through not only every aspect of computer science research but also many great directions towards my personal issues. -
Return of Organization Exempt from Income
OMB No. 1545-0047 Return of Organization Exempt From Income Tax Form 990 Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung benefit trust or private foundation) Open to Public Department of the Treasury Internal Revenue Service The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2011 calendar year, or tax year beginning 5/1/2011 , and ending 4/30/2012 B Check if applicable: C Name of organization The Apache Software Foundation D Employer identification number Address change Doing Business As 47-0825376 Name change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial return 1901 Munsey Drive (909) 374-9776 Terminated City or town, state or country, and ZIP + 4 Amended return Forest Hill MD 21050-2747 G Gross receipts $ 554,439 Application pending F Name and address of principal officer: H(a) Is this a group return for affiliates? Yes X No Jim Jagielski 1901 Munsey Drive, Forest Hill, MD 21050-2747 H(b) Are all affiliates included? Yes No I Tax-exempt status: X 501(c)(3) 501(c) ( ) (insert no.) 4947(a)(1) or 527 If "No," attach a list. (see instructions) J Website: http://www.apache.org/ H(c) Group exemption number K Form of organization: X Corporation Trust Association Other L Year of formation: 1999 M State of legal domicile: MD Part I Summary 1 Briefly describe the organization's mission or most significant activities: to provide open source software to the public that we sponsor free of charge 2 Check this box if the organization discontinued its operations or disposed of more than 25% of its net assets. -
Apache Karaf ${Karaf.Version}
Apache Karaf Version 2.2.5 Apache Karaf Users' Guide 1 Copyright 2011 The Apache Software Foundation The PDF format of the Karaf Manual has been generated by Prince XML (http://www.princexml.com). 2 Table of contents Overview Quick Start Users Guide Developers Guide 3 Overview 4 OVERVIEW Karaf Overview Apache Karaf is a small OSGi based runtime which provides a lightweight container onto which various components and applications can be deployed. Here is a short list of features supported by the Karaf: • Hot deployment: Karaf supports hot deployment of OSGi bundles by monitoring jar files inside the [home]/deploy directory. Each time a jar is copied in this folder, it will be installed inside the runtime. You can then update or delete it and changes will be handled automatically. In addition, Karaf also supports exploded bundles and custom deployers (Blueprint and Spring ones are included by default). • Dynamic configuration: Services are usually configured through the ConfigurationAdmin OSGi service. Such configuration can be defined in Karaf using property files inside the [home]/etc directory. These configurations are monitored and changes on the properties files will be propagated to the services. • Logging System: using a centralized logging back end supported by Log4J, Karaf supports a number of different APIs (JDK 1.4, JCL, SLF4J, Avalon, Tomcat, OSGi) • Provisioning: Provisioning of libraries or applications can be done through a number of different ways, by which they will be downloaded locally, installed and started. • Native OS integration: Karaf can be integrated into your own Operating System as a service so that the lifecycle will be bound to your Operating System. -
Xtext / Sirius - Integration the Main Use-Cases
Xtext / Sirius - Integration The Main Use-Cases White Paper December 2017 SUMMARY Chapter 1 Introduction 1 Chapter 2 Let’s start 2 Chapter 2.1 What modeling is about? 2 Chapter 2.2 Benefits of graphical modeling 3 Chapter 2.3 Benefits of textual modeling 5 Chapter 3 What is Xtext? 6 Chapter 4 What is Sirius? 8 Chapter 5 Xtext & Sirius in action 10 Chapter 5.1 Case 1: Editing the same models both graphically and textually 10 Chapter 5.2 Case 2: Embedding an Xtext Editor into Sirius 15 Chapter 6 How may we help you? 18 Introduction Introduction You are going to create a domain-specific modeling tool and you wonder how users will edit and visualize the models: textually with a dedicated syntax and a rich textual editor ? or graphically with diagrams drawn with a palette and smart tools? Both approaches are interesting and can be used complementary: While text is able to carry more detailed information, a diagram highlights the relationship between elements much better. In the end, a good tool should combine both, and use each notation where it suits best. In this white paper, we will explain the benefits of each approach. Then we will present Eclipse Xtext and Eclipse Sirius, two open-source frameworks for the development of textual and graphical model editors. And finally, we will detailed two use-cases where these two technologies can be integrated in the same modeling workbench. 1 Let’s start Let’s start What modeling is about? Before presenting the graphical and textual modeling approaches, it is important to briefly clarify what we mean by modeling. -
SVG-Based Knowledge Visualization
MASARYK UNIVERSITY FACULTY}w¡¢£¤¥¦§¨ OF I !"#$%&'()+,-./012345<yA|NFORMATICS SVG-based Knowledge Visualization DIPLOMA THESIS Miloš Kaláb Brno, spring 2012 Declaration Hereby I declare, that this paper is my original authorial work, which I have worked out by my own. All sources, references and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source. Advisor: RNDr. Tomáš Gregar Ph.D. ii Acknowledgement I would like to thank RNDr. Tomáš Gregar Ph.D. for supervising the thesis. His opinions, comments and advising helped me a lot with accomplishing this work. I would also like to thank to Dr. Daniel Sonntag from DFKI GmbH. Saarbrücken, Germany, for the opportunity to work for him on the Medico project and for his supervising of the thesis during my erasmus exchange in Germany. Big thanks also to Jochen Setz from Dr. Sonntag’s team who worked on the server background used by my visualization. Last but not least, I would like to thank to my family and friends for being extraordinary supportive. iii Abstract The aim of this thesis is to analyze the visualization of semantic data and sug- gest an approach to general visualization into the SVG format. Afterwards, the approach is to be implemented in a visualizer allowing user to customize the visualization according to the nature of the data. The visualizer was integrated as an extension of Fresnel Editor. iv Keywords Semantic knowledge, SVG, Visualization, JavaScript, Java, XML, Fresnel, XSLT v Contents Introduction . .3 1 Brief Introduction to the Related Technologies ..........5 1.1 XML – Extensible Markup Language ..............5 1.1.1 XSLT – Extensible Stylesheet Lang. -
Regeldokument
Master’s degree project Source code quality in connection to self-admitted technical debt Author: Alina Hrynko Supervisor: Morgan Ericsson Semester: VT20 Subject: Computer Science Abstract The importance of software code quality is increasing rapidly. With more code being written every day, its maintenance and support are becoming harder and more expensive. New automatic code review tools are developed to reach quality goals. One of these tools is SonarQube. However, people keep their leading role in the development process. Sometimes they sacrifice quality in order to speed up the development. This is called Technical Debt. In some particular cases, this process can be admitted by the developer. This is called Self-Admitted Technical Debt (SATD). Code quality can also be measured by such static code analysis tools as SonarQube. On this occasion, different issues can be detected. The purpose of this study is to find a connection between code quality issues, found by SonarQube and those marked as SATD. The research questions include: 1) Is there a connection between the size of the project and the SATD percentage? 2) Which types of issues are the most widespread in the code, marked by SATD? 3) Did the introduction of SATD influence the bug fixing time? As a result of research, a certain percentage of SATD was found. It is between 0%–20.83%. No connection between the size of the project and the percentage of SATD was found. There are certain issues that seem to relate to the SATD, such as “Duplicated code”, “Unused method parameters should be removed”, “Cognitive Complexity of methods should not be too high”, etc. -
Extended Version
Sina Sheikholeslami C u rriculum V it a e ( Last U pdated N ovember 2 0 18) Website: http://sinash.ir Present Address : https://www.kth.se/profile/sinash EIT Digital Stockholm CLC , https://linkedin.com/in/sinasheikholeslami Isafjordsgatan 26, Email: si [email protected] 164 40 Kista (Stockholm), [email protected] Sweden [email protected] Educational Background: • M.Sc. Student of Data Science, Eindhoven University of Technology & KTH Royal Institute of Technology, Under EIT-Digital Master School. 2017-Present. • B.Sc. in Computer Software Engineering, Department of Computer Engineering and Information Technology, Amirkabir University of Technology (Tehran Polytechnic). 2011-2016. • Mirza Koochak Khan Pre-College in Mathematics and Physics, Rasht, National Organization for Development of Exceptional Talents (NODET). Overall GPA: 19.61/20. 2010-2011. • Mirza Koochak Khan Highschool in Mathematics and Physics, Rasht, National Organization for Development of Exceptional Talents (NODET). Overall GPA: 19.17/20, Final Year's GPA: 19.66/20. 2007-2010. Research Fields of Interest: • Distributed Deep Learning, Hyperparameter Optimization, AutoML, Data Intensive Computing Bachelor's Thesis: • “SDMiner: A Tool for Mining Data Streams on Top of Apache Spark”, Under supervision of Dr. Amir H. Payberah (Defended on June 29th 2016). Computer Skills: • Programming Languages & Markups: o F luent in Java, Python, Scala, JavaS cript, C/C++, A ndroid Pr ogram Develop ment o Familia r wit h R, SAS, SQL , Nod e.js, An gula rJS, HTM L, JSP •